Disable datasets caching when preparing dataset for packing

2024-01-15 23:48:24 +01:00
170 changed files with 4368 additions and 8853 deletions
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,6 +1,6 @@
 # These are supported funding model platforms

-github: [winglian, OpenAccess-AI-Collective] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
 ko_fi: axolotl_ai # Replace with a single Ko-fi username
--- a/.github/ISSUE_TEMPLATE/bug-report.yaml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yaml
@@ -59,7 +59,6 @@ body:
      label: Config yaml
      description: |
        Please attach the config yaml!
-      render: yaml

  - type: textarea
    id: possible-solution
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -1,31 +1,39 @@
 name: ci-cd-base

 on:
-  workflow_dispatch:
+  push:
+    branches:
+      - "main-base"
+      - "dev-base"

 jobs:
  build-base:
    if: github.repository_owner == 'OpenAccess-AI-Collective'
    # this job needs to be run on self-hosted GPU runners...
-    runs-on: axolotl-gpu-runner
+    runs-on: self-hosted
    strategy:
      fail-fast: false
      matrix:
        include:
+          - cuda: "118"
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
          - cuda: "118"
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.0.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
+          - cuda: "118"
+            cuda_version: 11.8.0
+            python_version: "3.10"
+            pytorch: 2.1.1
            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
          - cuda: "121"
            cuda_version: 12.1.0
            python_version: "3.10"
-            pytorch: 2.1.2
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.1.2
+            pytorch: 2.1.1
            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
    steps:
      - name: Checkout
@@ -48,7 +56,7 @@ jobs:
          context: .
          file: ./docker/Dockerfile-base
          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          labels: ${{ steps.metadata.outputs.labels }}
          build-args: |
            CUDA_VERSION=${{ matrix.cuda_version }}
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -17,6 +17,6 @@ jobs:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
-          python-version: "3.10"
+          python-version: "3.9"
          cache: 'pip' # caching pip dependencies
      - uses: pre-commit/action@v3.0.0
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -4,33 +4,37 @@ on:
  push:
    branches:
      - "main"
-  workflow_dispatch:

 jobs:
  build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
+    if: github.repository_owner == 'OpenAccess-AI-Collective'
+    # this job needs to be run on self-hosted GPU runners...
    strategy:
      fail-fast: false
      matrix:
        include:
          - cuda: 118
            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.2
+            python_version: "3.9"
+            pytorch: 2.0.1
+            axolotl_extras:
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.10"
+            pytorch: 2.0.1
            axolotl_extras:
-            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
            is_latest: true
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.10"
+            pytorch: 2.1.1
+            axolotl_extras:
          - cuda: 121
            cuda_version: 12.1.0
            python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.1.1
            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.1.2
-            axolotl_extras:
-    runs-on: axolotl-gpu-runner
+    runs-on: [self-hosted, gpu, docker]
    steps:
      - name: Checkout
        uses: actions/checkout@v4
@@ -51,42 +55,57 @@ jobs:
        uses: docker/build-push-action@v5
        with:
          context: .
+          load: true
          build-args: |
            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
            CUDA=${{ matrix.cuda }}
            PYTORCH_VERSION=${{ matrix.pytorch }}
-            AXOLOTL_ARGS=${{ matrix.axolotl_args }}
          file: ./docker/Dockerfile
-          push: ${{ github.event_name != 'pull_request' }}
          tags: |
            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
            ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
          labels: ${{ steps.metadata.outputs.labels }}
+      - name: Unit Tests
+        run: |
+          docker run --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }} pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+      - name: Push to Docker Hub
+        if: github.event_name != 'pull_request'
+        run: |
+          docker push ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          latest_tag=${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
+          if [ -n "$latest_tag" ]; then
+            docker push "$latest_tag"
+          fi

  build-axolotl-runpod:
    needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
+    if: github.repository_owner == 'OpenAccess-AI-Collective'
    # this job needs to be run on self-hosted GPU runners...
    strategy:
      matrix:
        include:
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.1
+            axolotl_extras:
          - cuda: 118
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.0.1
            axolotl_extras:
            is_latest: true
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.10"
+            pytorch: 2.1.1
+            axolotl_extras:
          - cuda: 121
            cuda_version: 12.1.0
            python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.1.1
            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.1.2
-            axolotl_extras:
-    runs-on: axolotl-gpu-runner
+    runs-on: [self-hosted, gpu, docker]
    steps:
      - name: Checkout
        uses: actions/checkout@v4
@@ -94,7 +113,7 @@ jobs:
        id: metadata
        uses: docker/metadata-action@v5
        with:
-          images: winglian/axolotl-cloud
+          images: winglian/axolotl-runpod
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
@@ -109,11 +128,9 @@ jobs:
          build-args: |
            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
            CUDA=${{ matrix.cuda }}
-          file: ./docker/Dockerfile-cloud
+          file: ./docker/Dockerfile-runpod
          push: ${{ github.event_name != 'pull_request' }}
          tags: |
             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-             winglian/axolotl-runpod:main-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
-             ${{ (matrix.is_latest) && format('{0}-latest', 'winglian/axolotl-runpod:main') || '' }}
          labels: ${{ steps.metadata.outputs.labels }}
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -23,7 +23,7 @@ jobs:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
-          python-version: "3.10"
+          python-version: "3.9"
          cache: 'pip' # caching pip dependencies
      - uses: pre-commit/action@v3.0.0

@@ -33,7 +33,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        python_version: ["3.10", "3.11"]
+        python_version: ["3.9", "3.10", "3.11"]
    timeout-minutes: 10

    steps:
@@ -58,8 +58,8 @@ jobs:
  docker-e2e-tests:
    if: github.repository_owner == 'OpenAccess-AI-Collective'
    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 60
+    runs-on: [self-hosted, gpu, docker]
+    timeout-minutes: 30
    needs: [pre-commit, pytest]

    strategy:
@@ -69,32 +69,40 @@ jobs:
          - cuda: 118
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
-            num_gpus: 1
+            pytorch: 2.0.1
          - cuda: 121
            cuda_version: 12.1.0
            python_version: "3.10"
-            pytorch: 2.1.2
-            num_gpus: 1
+            pytorch: 2.1.1
    steps:
      - name: Checkout
        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
+      - name: Docker metadata
+        id: metadata
+        uses: docker/metadata-action@v5
        with:
-          python-version: "3.10"
-      - name: Install Modal
+          images: winglian/axolotl-tests
+      - name: Build Docker image
        run: |
-          python -m pip install --upgrade pip
-          pip install modal jinja2
-      - name: Update env vars
+          # Set up build arguments
+          BASE_TAG="main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}"
+          CUDA="${{ matrix.cuda }}"
+          PYTORCH_VERSION="${{ matrix.pytorch }}"
+          # Build the Docker image
+          docker build . \
+            --file ./docker/Dockerfile-tests \
+            --build-arg BASE_TAG=$BASE_TAG \
+            --build-arg CUDA=$CUDA \
+            --build-arg GITHUB_REF=$GITHUB_REF \
+            --build-arg PYTORCH_VERSION=$PYTORCH_VERSION \
+            --tag ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} \
+            --no-cache
+      - name: Unit Tests w docker image
        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
+          docker run --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+      - name: GPU Unit Tests w docker image
        run: |
-          modal run cicd.tests
+          docker run --privileged --gpus "all" --env WANDB_DISABLED=true --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/
+      - name: GPU Unit Tests monkeypatched w docker image
+        run: |
+          docker run --privileged --gpus "all" --env WANDB_DISABLED=true --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} pytest /workspace/axolotl/tests/e2e/patched/
--- a/.gitignore
+++ b/.gitignore
@@ -167,8 +167,3 @@ cython_debug/
 # WandB
 # wandb creates a folder to store logs for training runs
 wandb
-
-# Runs
-lora-out/*
-qlora-out/*
-mlruns/*
--- a/.mypy.ini
+++ b/.mypy.ini
@@ -1,5 +1,5 @@
 [mypy]
-plugins = pydantic.mypy
+
 exclude = venv

 [mypy-alpaca_lora_4bit.*]
@@ -32,9 +32,6 @@ ignore_missing_imports = True
 [mypy-bitsandbytes]
 ignore_missing_imports = True

-[mypy-requests]
-ignore_missing_imports = True
-
 [mypy-datasets]
 ignore_missing_imports = True

--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -31,7 +31,6 @@ repos:
      additional_dependencies:
        [
            'types-PyYAML',
-            'pydantic>=2.5.3',
        ]
 -   repo: https://github.com/PyCQA/bandit
    rev: 1.7.5
--- a/README.md
+++ b/README.md
@@ -22,11 +22,11 @@ Features:
 - [Introduction](#axolotl)
 - [Supported Features](#axolotl-supports)
 - [Quickstart](#quickstart-)
- [Environment](#environment)
+- [Installation](#installation)
  - [Docker](#docker)
  - [Conda/Pip venv](#condapip-venv)
-  - [Cloud GPU](#cloud-gpu) - Latitude.sh, JarvisLabs, RunPod
-  - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
+  - [Runpod](#runpod)
+  - [LambdaLabs](#lambdalabs)
  - [Windows](#windows)
  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
 - [Dataset](#dataset)
@@ -34,12 +34,9 @@ Features:
  - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
 - [Config](#config)
  - [Train](#train)
-  - [Inference](#inference-playground)
+  - [Inference](#inference)
  - [Merge LORA to Base](#merge-lora-to-base)
  - [Special Tokens](#special-tokens)
- Advanced Topics
-  - [Multipack](./docs/multipack.md)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [RLHF & DPO](./docs/rlhf.md)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
 - [Common Errors](#common-errors-)
  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
 - [Debugging Axolotl](#debugging-axolotl)
@@ -87,17 +84,15 @@ Features:
 | phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
 | RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
 | Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |

-✅: supported
-❌: not supported
-❓: untested

 ## Quickstart ⚡

 Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.

-**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
+**Requirements**: Python >=3.9 and Pytorch >=2.0.
+
+`pip3 install "axolotl[flash-attn,deepspeed] @ git+https://github.com/OpenAccess-AI-Collective/axolotl"`

 ### For developers
 ```bash
@@ -105,23 +100,11 @@ git clone https://github.com/OpenAccess-AI-Collective/axolotl
 cd axolotl

 pip3 install packaging
-```
-
-General case:
-```
 pip3 install -e '.[flash-attn,deepspeed]'
 ```

-Mac: see https://github.com/OpenAccess-AI-Collective/axolotl/blob/13199f678b9aab39e92961323bdbce3234ee4b2b/docs/mac.md
-```
-pip3 install -e '.'
-```
-
 ### Usage
 ```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
-
 # finetune lora
 accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml

@@ -132,20 +115,15 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
 # gradio
 accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
    --lora_model_dir="./lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/examples/openllama-3b/lora.yml
 ```

-## Advanced Setup
+## Installation

 ### Environment

 #### Docker
-
  ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
  ```

  Or run on the current files for development:
@@ -164,7 +142,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/OpenAcc
  A more powerful Docker command to run would be this:

  ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
  ```

  It additionally:
@@ -179,7 +157,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
  </details>

 #### Conda/Pip venv
-  1. Install python >=**3.10**
+  1. Install python >=**3.9**

  2. Install pytorch stable https://pytorch.org/get-started/locally/

@@ -194,18 +172,11 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
        ```
        Get the token at huggingface.co/settings/tokens

-#### Cloud GPU
+#### Runpod

-For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
-
- on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
- on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
- on RunPod use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-
-#### Bare Metal Cloud GPU
-
-##### LambdaLabs
+Use `winglian/axolotl-runpod:main-latest` or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)

+#### LambdaLabs
  <details>

  <summary>Click to Expand</summary>
@@ -213,11 +184,11 @@ For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud
  1. Install python
  ```bash
  sudo apt update
-  sudo apt install -y python3.10
+  sudo apt install -y python3.9

-  sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1
-  sudo update-alternatives --config python # pick 3.10 if given option
-  python -V # should be 3.10
+  sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
+  sudo update-alternatives --config python # pick 3.9 if given option
+  python -V # should be 3.9

  ```

@@ -255,18 +226,15 @@ Please use WSL or Docker!

 #### Launching on public clouds via SkyPilot
 To launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use [SkyPilot](https://skypilot.readthedocs.io/en/latest/index.html):
-
 ```bash
 pip install "skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]"  # choose your clouds
 sky check
 ```
-
 Get the [example YAMLs](https://github.com/skypilot-org/skypilot/tree/master/llm/axolotl) of using Axolotl to finetune `mistralai/Mistral-7B-v0.1`:
 ```
 git clone https://github.com/skypilot-org/skypilot.git
 cd skypilot/llm/axolotl
 ```
-
 Use one command to launch:
 ```bash
 # On-demand
@@ -276,32 +244,31 @@ HF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN
 HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET
 ```

+
 ### Dataset

 Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
 Have dataset(s) in one of the following format (JSONL recommended):

-#### Pretraining
-
- `completion`: raw corpus
-  ```json
-  {"text": "..."}
-  ```
-
-Note: Axolotl usually loads the entire dataset into memory. This will be challenging for large datasets. Use the following config to enable streaming:
-
-```yaml
-pretraining_dataset: # hf path only
-```
-
-#### Supervised finetuning
-
-##### Instruction
-
 - `alpaca`: instruction; input(optional)
  ```json
  {"instruction": "...", "input": "...", "output": "..."}
  ```
+- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: `system` to override default system prompt)
+  ```json
+  {"conversations": [{"from": "...", "value": "..."}]}
+  ```
+- `llama-2`: the json is the same format as `sharegpt` above, with the following config (see the [config section](#config) for more details)
+    ```yml
+    datasets:
+      - path: <your-path>
+        type: sharegpt
+        conversation: llama-2
+    ```
+- `completion`: raw corpus
+  ```json
+  {"text": "..."}
+  ```

 <details>

@@ -379,37 +346,14 @@ pretraining_dataset: # hf path only
  ```json
  {"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
  ```
- `metharme`: instruction, adds additional eos tokens
-  ```json
-  {"prompt": "...", "generation": "..."}
-  ```
-
-</details>
-
-##### Template-Free
-
- `input_output`: template-free prompt construction
-  ```json
-   {"segments": [{"label": true|false, "text": "..."}]}
-  ```
-
-This is a special format that allows you to construct prompts without using templates. This is for advanced users who want more freedom with prompt construction.  See [these docs](docs/input_output.md) for more details.
-
-##### Conversation
-
- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
-  ```json
-  {"conversations": [{"from": "...", "value": "..."}]}
-  ```
-
-<details>
-
-<summary>See other formats</summary>
-
 - `pygmalion`: pygmalion
  ```json
  {"conversations": [{"role": "...", "value": "..."}]}
  ```
+- `metharme`: instruction, adds additional eos tokens
+  ```json
+  {"prompt": "...", "generation": "..."}
+  ```
 - `sharegpt.load_role`: conversations where `role` is used instead of `from`
  ```json
  {"conversations": [{"role": "...", "value": "..."}]}
@@ -425,14 +369,12 @@ This is a special format that allows you to construct prompts without using temp

 </details>

-Note: `type: sharegpt` opens a special config `conversation:` that enables conversions to many Conversation types. See dataset section under [all yaml options](#all-yaml-options).
-
 #### How to add custom prompts

 For a dataset that is preprocessed for instruction purposes:

 ```json
-{"input": "...", "output": "..."}
+{"instruction": "...", "output": "..."}
 ```

 You can use this example in your YAML config:
@@ -443,21 +385,15 @@ datasets:
    type:
      system_prompt: ""
      field_system: system
-      field_instruction: input
-      field_output: output
      format: "[INST] {instruction} [/INST]"
      no_input_format: "[INST] {instruction} [/INST]"
 ```
-See full config options under [all yaml options](#all-yaml-options).

 #### How to use your custom pretokenized dataset

 - Do not pass a `type:`
 - Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`

-```yaml
- path: ...
-```

 ### Config

@@ -471,18 +407,22 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod

 - dataset
  ```yaml
-  datasets:
-      # huggingface repo
-    - path: vicgalle/alpaca-gpt4
-      type: alpaca
+  sequence_len: 2048 # max token length for prompt

-      # huggingface repo with specific configuration/subset
+  # huggingface repo
+  datasets:
+    - path: vicgalle/alpaca-gpt4
+      type: alpaca # format from earlier
+
+  # huggingface repo with specific configuration/subset
+  datasets:
    - path: EleutherAI/pile
      name: enron_emails
      type: completion # format from earlier
      field: text # Optional[str] default: text, field to use for completion data

-      # huggingface repo with multiple named configurations/subsets
+  # huggingface repo with multiple named configurations/subsets
+  datasets:
    - path: bigcode/commitpackft
      name:
        - ruby
@@ -490,42 +430,39 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
        - typescript
      type: ... # unimplemented custom format

-      # fastchat conversation
-      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+  # fastchat conversation
+  # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+  datasets:
    - path: ...
      type: sharegpt
-      conversation: chatml # default: vicuna_v1.1
+      conversation: chatml

-      # local
+  # local
+  datasets:
    - path: data.jsonl # or json
      ds_type: json # see other options below
      type: alpaca

-      # dataset with splits, but no train split
+  # dataset with splits, but no train split
+  dataset:
    - path: knowrohit07/know_sql
      type: context_qa.load_v2
      train_on_split: validation

-      # loading from s3 or gcs
-      # s3 creds will be loaded from the system default and gcs only supports public access
+  # loading from s3 or gcs
+  # s3 creds will be loaded from the system default and gcs only supports public access
+  dataset:
    - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.
      ...
-
-      # Loading Data From a Public URL
-      # - The file format is `json` (which includes `jsonl`) by default. For different formats, adjust the `ds_type` option accordingly.
-    - path: https://some.url.com/yourdata.jsonl # The URL should be a direct link to the file you wish to load. URLs must use HTTPS protocol, not HTTP.
-      ds_type: json # this is the default, see other options below.
  ```

 - loading
  ```yaml
  load_in_4bit: true
  load_in_8bit: true
-
-  bf16: auto # require >=ampere, auto will detect if your GPU supports this and choose automatically.
-  fp16: # leave empty to use fp16 when bf16 is 'auto'. set to false if you want to fallback to fp32
+  bf16: true # require >=ampere
+  fp16: true
  tf32: true # require >=ampere
-
  bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)
  float16: true # use instead of fp16 when you don't want AMP
  ```
@@ -533,7 +470,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod

 - lora
  ```yaml
-  adapter: lora # 'qlora' or leave blank for full finetune
+  adapter: lora # qlora or leave blank for full finetune
  lora_r: 8
  lora_alpha: 16
  lora_dropout: 0.05
@@ -542,9 +479,9 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
    - v_proj
  ```

-<details id="all-yaml-options">
+<details>

-<summary>All yaml options (click to expand)</summary>
+<summary>All yaml options (click me)</summary>

 ```yaml
 # This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
@@ -556,8 +493,8 @@ base_model_ignore_patterns:
 # You can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
 # You can specify to choose a specific model revision from huggingface hub
-revision_of_model:
-# Optional tokenizer configuration path in case you want to use a different tokenizer
+model_revision:
+# Optional tokenizer configuration override in case you want to use a different tokenizer
 # than the one defined in the base model
 tokenizer_config:
 # If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
@@ -574,16 +511,15 @@ tokenizer_legacy:
 # This is reported to improve training speed on some models
 resize_token_embeddings_to_32x:

-# (Internal use only)
 # Used to identify which the model is based on
 is_falcon_derived_model:
 is_llama_derived_model:
-is_qwen_derived_model:
 # Please note that if you set this to true, `padding_side` will be set to "left" by default
 is_mistral_derived_model:
+is_qwen_derived_model:

 # optional overrides to the base model configuration
-overrides_of_model_config:
+model_config:
  # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
  rope_scaling:
    type: # linear | dynamic
@@ -600,6 +536,8 @@ bnb_config_kwargs:

 # Whether you are training a 4-bit GPTQ quantized model
 gptq: true
+gptq_groupsize: 128 # group size
+gptq_model_v1: false # v1 or v2

 # This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
 load_in_8bit: true
@@ -639,10 +577,10 @@ datasets:
    field_human: # Optional[str]. Human key to use for conversation.
    field_model: # Optional[str]. Assistant key to use for conversation.

-  # Custom user instruction prompt
+  # Custom user prompt
  - path: repo
    type:
-      # The below are defaults. only set what's needed if you use a different column name.
+      # The below are defaults. only set what's needed.
      system_prompt: ""
      system_format: "{system}"
      field_system: system
@@ -651,7 +589,6 @@ datasets:
      field_output: output

      # Customizable to be single line or multi-line
-      # Use {instruction}/{input} as key to be replaced
      # 'format' can include {input}
      format: |-
        User: {instruction} {input}
@@ -662,25 +599,12 @@ datasets:
      # For `completion` datsets only, uses the provided field instead of `text` column
      field:

-# A list of one or more datasets to eval the model with.
-# You can use either test_datasets, or val_set_size, but not both.
-test_datasets:
-  - path: /workspace/data/eval.jsonl
-    ds_type: json
-    # You need to specify a split. For "json" datasets the default split is called "train".
-    split: train
-    type: completion
-    data_files:
-      - /workspace/data/eval.jsonl
-
-# use RL training: 'dpo', 'ipo', 'kto_pair'
+# use RL training: dpo, ipo, kto_pair
 rl:

 # Saves the desired chat template to the tokenizer_config.json for easier inferencing
 # Currently supports chatml and inst (mistral/mixtral)
 chat_template: chatml
-# Changes the default system message
-default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -689,11 +613,8 @@ push_dataset_to_hub: # repo path
 # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
 # if not set.
 dataset_processes: # defaults to os.cpu_count() if not set
-# Keep dataset in memory while preprocessing
-# Only needed if cached dataset is taking too much storage
-dataset_keep_in_memory:
 # push checkpoints to hub
-hub_model_id: # private repo path to push finetuned model
+hub_model_id: # repo path to push finetuned model
 # how to push checkpoints to hub
 # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
 hub_strategy:
@@ -713,6 +634,10 @@ sequence_len: 2048
 # Pad inputs so each step uses constant sized buffers
 # This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
 pad_to_sequence_len:
+# Max sequence length to concatenate training samples together up to
+# Inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
+# FutureWarning: This will soon be DEPRECATED
+max_packed_sequence_len: 1024
 # Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
 sample_packing:
 # Set to 'false' if getting errors during eval with sample_packing on.
@@ -749,8 +674,7 @@ lora_target_modules:
 #  - gate_proj
 #  - down_proj
 #  - up_proj
-lora_target_linear: # If true, will target all linear modules
-peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
+lora_target_linear: # If true, will target all linear layers

 # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
 # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
@@ -762,18 +686,10 @@ lora_modules_to_save:

 lora_fan_in_fan_out: false

-peft:
-  # Configuration options for loftq initialization for LoRA
-  # https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
-  loftq_config:
-    loftq_bits:  # typically 4 bits
-
 # ReLoRA configuration
 # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
 relora_steps: # Number of steps per ReLoRA restart
 relora_warmup_steps: # Number of per-restart warmup steps
-relora_anneal_steps: # Number of anneal steps for each relora cycle
-relora_prune_ratio: # threshold for optimizer magnitude when pruning
 relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings

 # wandb configuration if you're using it
@@ -789,7 +705,6 @@ wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_step
 # mlflow configuration if you're using it
 mlflow_tracking_uri: # URI to mlflow
 mlflow_experiment_name: # Your experiment name
-hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry

 # Where to save the full-finetuned model to
 output_dir: ./completed-model
@@ -823,8 +738,7 @@ save_total_limit: # Checkpoints saved at a time
 max_steps:

 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
-eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
+eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128

 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
@@ -853,11 +767,14 @@ early_stopping_patience: 3
 lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
 lr_scheduler_kwargs:
 cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
-cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)

 # For one_cycle optim
 lr_div_factor: # Learning rate div factor

+# For log_sweep optim
+log_sweep_min_lr:
+log_sweep_max_lr:
+
 # Specify optimizer
 # Valid values are driven by the Transformers OptimizerNames class, see:
 # https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
@@ -911,8 +828,7 @@ flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 # Whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:
-# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
-s2_attention:
+
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:
 # If resume_from_checkpoint isn't set and you simply want it to start where it left off.
@@ -936,7 +852,7 @@ tokens:
 fsdp:
 fsdp_config:

-# Deepspeed config path. e.g., deepspeed_configs/zero3.json
+# Deepspeed config path. e.g., deepspeed/zero3.json
 deepspeed:

 # Advanced DDP Arguments
@@ -1029,9 +945,6 @@ Run
 accelerate launch -m axolotl.cli.train your_config.yml
 ```

-> [!TIP]
-> You can also reference a config file that is hosted on a public URL, for example `accelerate launch -m axolotl.cli.train https://yourdomain.com/your_config.yml`
-
 #### Preprocess dataset

 You can optionally pre-tokenize dataset with the following before finetuning.
@@ -1060,11 +973,11 @@ for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usa
 We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.

 ```yaml
-deepspeed: deepspeed_configs/zero1.json
+deepspeed: deepspeed/zero1.json
 ```

 ```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed_configs/zero1.json
+accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
 ```

 ##### FSDP
@@ -1141,7 +1054,7 @@ Please use `--sample_packing False` if you have it on and receive the error simi

 ### Merge LORA to base

-The following command will merge your LORA adapater with your base model. You can optionally pass the argument `--lora_model_dir` to specify the directory where your LORA adapter was saved, otherwhise, this will be inferred from `output_dir` in your axolotl config file.  The merged model is saved in the sub-directory `{lora_model_dir}/merged`.
+The following command will merge your LORA adapater with your base model.  You can optionally pass the argument `--lora_model_dir` to specify the directory where your LORA adapter was saved, otherwhise, this will be inferred from `output_dir` in your axolotl config file.  The merged model is saved in the sub-directory `{lora_model_dir}/merged`.

 ```bash
 python3 -m axolotl.cli.merge_lora your_config.yml --lora_model_dir="./completed-model"
@@ -1202,8 +1115,8 @@ If you decode a prompt constructed by axolotl, you might see spaces between toke

 1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
 2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
-3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines.  If they aren't the same, adjust your inference server accordingly.
-4. As an additional troubleshooting step, you can look at the token ids between 1 and 2 to make sure they are identical.
+3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines.  If they aren't the same adjust your inference server accordingly.
+4. As an additional troubleshooting step, you can look look at the token ids between 1 and 2 to make sure they are identical.

 Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this.  See [this blog post](https://hamel.dev/notes/llm/05_tokenizer_gotchas.html) for a concrete example.

@@ -1211,11 +1124,9 @@ Having misalignment between your prompts during training and inference can cause

 See [this debugging guide](docs/debugging.md) for tips on debugging Axolotl, along with an example configuration for debugging with VSCode.

-## Need help? 🙋
+## Need help? 🙋♂️

-Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we our community members can help you.
-
-Need dedicated support? Please contact us at [✉️wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org) for dedicated support options.
+Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you

 ## Badge ❤🏷️

@@ -1232,7 +1143,7 @@ Building something cool with Axolotl? Consider adding a badge to your model card
 Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.

 Open Access AI Collective
- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed)
+- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
 - [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
 - [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)

@@ -1249,28 +1160,13 @@ PRs are **greatly welcome**!

 Please run below to setup env
 ```bash
-git clone https://github.com/OpenAccess-AI-Collective/axolotl
-cd axolotl
-
-pip3 install packaging
-pip3 install -e '.[flash-attn,deepspeed]'
-
 pip3 install -r requirements-dev.txt -r requirements-tests.txt
 pre-commit install

 # test
 pytest tests/
-
-# optional: run against all files
-pre-commit run --all-files
 ```

-Thanks to all of our contributors to date. Help drive open source AI progress forward by contributing to Axolotl.
-
-<a href="https://github.com/openaccess-ai-collective/axolotl/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=openaccess-ai-collective/axolotl" alt="contributor chart by https://contrib.rocks"/>
-</a>
-
 ## Sponsors 🤝❤

 OpenAccess AI Collective is run by volunteer contributors such as [winglian](https://github.com/winglian),
@@ -1299,6 +1195,4 @@ consider sponsoring the project via [GitHub Sponsors](https://github.com/sponsor

 #### 🥉 Bronze Sponsors - $500/mo

- - [JarvisLabs.ai](https://jarvislabs.ai)
-
 ---
--- a/cicd/Dockerfile.jinja
+++ b/cicd/Dockerfile.jinja
@@ -1,39 +0,0 @@
-FROM winglian/axolotl-base:{{ BASE_TAG }}
-
-ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
-ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
-ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
-ENV CUDA="{{ CUDA }}"
-ENV BNB_CUDA_VERSION="{{ CUDA }}"
-ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
-ENV GITHUB_REF="{{ GITHUB_REF }}"
-ENV GITHUB_SHA="{{ GITHUB_SHA }}"
-
-RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
-
-WORKDIR /workspace
-
-RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
-
-WORKDIR /workspace/axolotl
-
-RUN git fetch origin +$GITHUB_REF && \
-    git checkout FETCH_HEAD
-
-# If AXOLOTL_EXTRAS is set, append it in brackets
-RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
-    else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
-    fi
-
-# So we can test the Docker image
-RUN pip install pytest
-
-# fix so that git fetch/pull from remote works
-RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch
-
-# helper for huggingface-login cli
-RUN git config --global credential.helper store
--- a/cicd/cicd.sh
+++ b/cicd/cicd.sh
@@ -1,5 +0,0 @@
-#!/bin/bash
-
-pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
-pytest /workspace/axolotl/tests/e2e/patched/
-pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/
--- a/cicd/tests.py
+++ b/cicd/tests.py
@@ -1,75 +0,0 @@
-"""
- modal application to run axolotl gpu tests in Modal
- """
-import os
-import pathlib
-import tempfile
-
-import jinja2
-import modal
-from jinja2 import select_autoescape
-from modal import Image, Stub
-
-cicd_path = pathlib.Path(__file__).parent.resolve()
-
-template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
-template_env = jinja2.Environment(
-    loader=template_loader, autoescape=select_autoescape()
-)
-df_template = template_env.get_template("Dockerfile.jinja")
-
-df_args = {
-    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
-    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.0.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.10-cu118-2.0.1"),
-    "CUDA": os.environ.get("CUDA", "118"),
-    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
-    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-}
-
-dockerfile_contents = df_template.render(**df_args)
-
-temp_dir = tempfile.mkdtemp()
-with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
-    f.write(dockerfile_contents)
-
-cicd_image = (
-    Image.from_dockerfile(
-        pathlib.Path(temp_dir) / "Dockerfile",
-        force_build=True,
-        gpu="A10G",
-    )
-    .env(df_args)
-    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
-)
-
-stub = Stub("Axolotl CI/CD", secrets=[])
-
-
-N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = modal.gpu.A10G(count=N_GPUS)
-
-
-def run_cmd(cmd: str, run_folder: str):
-    import subprocess  # nosec
-
-    # Propagate errors from subprocess.
-    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
-
-
-@stub.function(
-    image=cicd_image,
-    gpu=GPU_CONFIG,
-    timeout=45 * 60,
-    cpu=8.0,
-    memory=131072,
-)
-def cicd_pytest():
-    run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
-
-
-@stub.local_entrypoint()
-def main():
-    cicd_pytest.remote()
--- a/deepspeed_configs/zero1.json
+++ b/deepspeed_configs/zero1.json
@@ -15,6 +15,15 @@
    "hysteresis": 2,
    "min_loss_scale": 1
  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
  "gradient_accumulation_steps": "auto",
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
--- a/deepspeed_configs/zero2.json
+++ b/deepspeed_configs/zero2.json
@@ -19,6 +19,15 @@
    "hysteresis": 2,
    "min_loss_scale": 1
  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
  "gradient_accumulation_steps": "auto",
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
--- a/deepspeed_configs/zero3.json
+++ b/deepspeed_configs/zero3.json
@@ -23,6 +23,15 @@
    "hysteresis": 2,
    "min_loss_scale": 1
  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
  "gradient_accumulation_steps": "auto",
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
--- a/deepspeed_configs/zero3_bf16.json
+++ b/deepspeed_configs/zero3_bf16.json
@@ -23,6 +23,15 @@
    "hysteresis": 2,
    "min_loss_scale": 1
  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
  "gradient_accumulation_steps": "auto",
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
--- a/devtools/dev_sharegpt.yml
+++ b/devtools/dev_sharegpt.yml
@@ -2,6 +2,7 @@
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: true
 load_in_4bit: false
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -3,10 +3,9 @@ FROM winglian/axolotl-base:$BASE_TAG

 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
-ARG AXOLOTL_ARGS=""
 ARG CUDA="118"
 ENV BNB_CUDA_VERSION=$CUDA
-ARG PYTORCH_VERSION="2.1.2"
+ARG PYTORCH_VERSION="2.0.1"

 ENV PYTORCH_VERSION=$PYTORCH_VERSION

@@ -21,9 +20,9 @@ WORKDIR /workspace/axolotl

 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS]; \
    else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm]; \
    fi

 # So we can test the Docker image
--- a/docker/Dockerfile-base
+++ b/docker/Dockerfile-base
@@ -7,8 +7,8 @@ FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION a

 ENV PATH="/root/miniconda3/bin:${PATH}"

-ARG PYTHON_VERSION="3.10"
-ARG PYTORCH_VERSION="2.1.2"
+ARG PYTHON_VERSION="3.9"
+ARG PYTORCH_VERSION="2.0.1"
 ARG CUDA="118"
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"

@@ -29,7 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace

 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} deepspeed-kernels --extra-index-url https://download.pytorch.org/whl/cu$CUDA

 RUN git lfs install --skip-repo && \
    pip3 install awscli && \
--- a/docker/Dockerfile-runpod
+++ b/docker/Dockerfile-runpod
@@ -7,21 +7,14 @@ ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"

-EXPOSE 8888
-EXPOSE 22
+COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh

-COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
-COPY scripts/motd /etc/motd
-
-RUN pip install jupyterlab notebook ipywidgets && \
-    jupyter lab clean
 RUN apt install --yes --no-install-recommends openssh-server tmux && \
    mkdir -p ~/.ssh && \
    chmod 700 ~/.ssh && \
    printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
-    printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
-    chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
-    chmod +x /root/cloud-entrypoint.sh
+    chmod +x /workspace/axolotl/scripts/runpod-entrypoint.sh && \
+    chmod +x /root/runpod-entrypoint.sh

-ENTRYPOINT ["/root/cloud-entrypoint.sh"]
+ENTRYPOINT ["/root/runpod-entrypoint.sh"]
 CMD ["sleep", "infinity"]
--- a/docker/Dockerfile-tests
+++ b/docker/Dockerfile-tests
@@ -3,10 +3,9 @@ FROM winglian/axolotl-base:$BASE_TAG

 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
-ARG AXOLOTL_ARGS=""
 ARG CUDA="118"
 ENV BNB_CUDA_VERSION=$CUDA
-ARG PYTORCH_VERSION="2.1.2"
+ARG PYTORCH_VERSION="2.0.1"
 ARG GITHUB_REF="main"

 ENV PYTORCH_VERSION=$PYTORCH_VERSION
@@ -25,9 +24,9 @@ RUN git fetch origin +$GITHUB_REF && \

 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS]; \
    else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm]; \
    fi

 # So we can test the Docker image
--- a/docs/debugging.md
+++ b/docs/debugging.md
@@ -74,6 +74,7 @@ pip3 install -e '.[flash-attn,deepspeed]'

 If you developing on a remote host, you can easily use VSCode to debug remotely.  To do so, you will need to follow this [remote - SSH guide](https://code.visualstudio.com/docs/remote/ssh).  You can also see the video below on [Docker and Remote SSH debugging](#video---attaching-to-docker-on-remote-host).

+```bash

 ### Configuration

--- a/docs/images/4d-mask.png
+++ b/docs/images/4d-mask.png
--- a/docs/input_output.md
+++ b/docs/input_output.md
@@ -1,260 +0,0 @@
-# Template-free prompt construction with the `input_output` format
-
-<!-- TOC -->
-
- [Background](#background)
-    - [Masking Inputs](#masking-inputs)
-    - [You may not want prompt templates](#you-may-not-want-prompt-templates)
-    - [The `input_output` format](#the-input_output-format)
- [Usage](#usage)
-    - [1. Prepare Data](#1-prepare-data)
-    - [2. Use `type: input_output`](#2-use-type-input_output)
-    - [3. Check the prompts](#3-check-the-prompts)
-
-<!-- /TOC -->
-
-<a id="markdown-background" name="background"></a>
-
-## Background
-
-<a id="markdown-masking-inputs" name="masking-inputs"></a>
-
-### Masking Inputs
-
-One of the most popular features of
-[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) is
-setting the following configuration value:
-
-
-```yaml
-train_on_inputs: false
-```
-
-If you declare a [dataset formats](https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#dataset)
-such as `alpaca` or `chatml`, axolotl knows what is an input
-(i.e. human) vs. an output (i.e. the assistant) and masks the input
-labels so that your model can focus on predicting the outputs only.
-
-<a id="markdown-you-may-not-want-prompt-templates" name="you-may-not-want-prompt-templates"></a>
-
-### You may not want prompt templates
-
-However, there are many situations where you don't want to use one of
-these formats or templates (I usually don't!). This is because they can:
-
-   Add unnecessary boilerplate to your prompts.
-   Create artifacts like special delimiters `<|im_start|>` that can
-    quickly become footguns if you don't include them correctly at
-    inference time.
-   Enforce a *chat* interface when you do not want one. Sometimes you
-    just want to fine-tune a model to a very specific task and do NOT
-    want multi-turn conversations, roles, etc.
-   Limit you to only certain roles that the template allows.
-
-<a id="markdown-the-inputoutput-format" name="the-inputoutput-format"></a>
-
-### The `input_output` format
-
-You can construct your prompts without a template by using the
-`input_output` format, by setting `type: input_output` in your
-configuration file like this:
-
-**config.yml**
-
-```yaml
-train_on_inputs: false # Mask segments of your data
-datasets:
-  - path: output.jsonl
-    type: input_output  # use template free prompt construction
-```
-
-Unlike `type: completion`, which is also template-free,
-`type: input_output` allows you to mask segments of your text. More
-details on how this works are described below.
-
-<a id="markdown-usage" name="usage"></a>
-
-## Usage
-
-This is how you can use the `input_output` format:
-
-<a id="markdown-1-prepare-data" name="1-prepare-data"></a>
-
-### 1. Prepare Data
-
-To use the `input_output` format, collect your data in the following
-format into a jsonl file (below is the first row from the file
-`output`.jsonl` pretty printed):
-
-```bash
-$ head -n1 output.jsonl | python -m json.tool
-
-{.cell-output .cell-output-stdout}
-    {
-        "segments": [
-            {
-                "label": true,
-                "text": "<s>Hello\n"
-            },
-            {
-                "label": true,
-                "text": "hi there!. "
-            },
-            {
-                "label": false,
-                "text": "goodbye "
-            },
-            {
-                "label": true,
-                "text": "farewell</s>"
-            }
-        ]
-    }
-```
-
-Set `label:false` when you want to mask a segment of text so that the
-model isn't trained on it. Some things to keep in mind:
-
-> [!IMPORTANT]
-> 1.  **EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl
-    concatenates all the segments as-is.** The tokenizer doesn't add
-    anything additional. Notice how I added spaces, newlines, `<s>`
-    (BOS), and `</s>` (EOS) myself.
-> 2.  Make sure you check the materialized output to validate that the
-    prompt is getting assembled how you like.
-
-<a id="markdown-2-use-type-inputoutput" name="2-use-type-inputoutput"></a>
-
-### 2. Use `type: input_output`
-
-Let's materialize data with our `output.jsonl` file by setting
-`type: input_output` in our axolotl config:
-
-```yaml
-# training_config.yaml
-base_model: mistralai/Mistral-7B-v0.1
-data_seed: 49
-seed: 49
-
-datasets:
-  - path: output.jsonl
-    type: input_output
-val_set_size: 0.1
-
-sequence_len: 896
-sample_packing: false
-
-micro_batch_size: 2
-gradient_accumulation_steps: 3
-eval_batch_size: 2
-num_epochs: 1
-learning_rate: 0.0002
-
-train_on_inputs: false
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-```
-
-You can use the following command to materialize your data. The
-`--debug` flag will print the tokens, along with the labels so you can
-verify that the correct items are being ignored:
-
-```bash
-$ python -m axolotl.cli.preprocess training_config.yaml --debug
-
-...
-[2024-03-05 23:36:46,969] [INFO] [axolotl.check_example_labels:35] [PID:607731] [RANK:0] <s>(1, 1) Hello(22557, 22557)
-(13, 13) hi(12014, 12014) there(736, 736) !(28808, 28808) .(28723, 28723) (28705, 28705) good(-100, 1179) bye(-100, 17664) (-100, 28705) fare(19111, 19111) well(5458, 5458) </s>(2, 2)
-
-```
-
-The format is `decoded_token`(`label`, `token_id`), for example,
-`<s>(1, 1)` means that the token is `<s>`, the label is `1` and the
-token_id is `1`. When the label is `-100` then that token is ignored for
-training.
-
-<a id="markdown-3-check-the-prompts" name="3-check-the-prompts"></a>
-
-### 3. Check the prompts
-
-Here is another way to check the materialized output:
-
-```python
-from transformers import AutoTokenizer
-from datasets import load_from_disk
-import yaml
-
-directory = !ls last_run_prepared/
-with open('training_config.yaml', 'r') as f:
-    cfg = yaml.safe_load(f)
-model_id = cfg['base_model']
-tok = AutoTokenizer.from_pretrained(model_id)
-ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
-```
-
-```python
->>> row = ds[0]
->>> print(tok.decode(row['input_ids']))
-<s> Hello
-    hi there!.  goodbye  farewell</s>
-```
-
-We can check that the right tokens are ingored by comparing the labels
-to each token:
-
-```python
-import pandas as pd
-pd.DataFrame([{'token': tok.decode(i), 'label': l, 'id':i} for i,l in
-              zip(row['input_ids'], row['labels'])])
-```
-
-| token | label | id    |
-|-------|-------|-------|
-| 0     | \<s\> | 1     |
-| 1     | Hello | 22557 |
-| 2     | \\n   | 13    |
-| 3     | hi    | 12014 |
-| 4     | there | 736   |
-| 5     | !     | 28808 |
-| 6     | .     | 28723 |
-| 7     |       | 28705 |
-| 8     | good  | -100  |
-| 9     | bye   | -100  |
-| 10    |       | -100  |
-| 11    | fare  | 19111 |
-| 12    | well  | 5458  |
-| 13    | \</s\>| 2     |
-
-
-
-If we look at the input data, the above table seems correct! (The jsonl
-version is repeated below for reference):
-
-
-```bash
-$ head -n1 output.jsonl | python -m json.tool
-
-{.cell-output .cell-output-stdout}
-    {
-        "segments": [
-            {
-                "label": true,
-                "text": "<s>Hello\n"
-            },
-            {
-                "label": true,
-                "text": "hi there!. "
-            },
-            {
-                "label": false,
-                "text": "goodbye "
-            },
-            {
-                "label": true,
-                "text": "farewell</s>"
-            }
-        ]
-    }
-```
--- a/docs/mac.md
+++ b/docs/mac.md
@@ -1,18 +0,0 @@
-# Mac M series support
-
-Currently Axolotl on Mac is partially usable, many of the dependencies of Axolotl including Pytorch do not support MPS or have incomplete support.
-
-Current support:
- [x] Support for all models
- [x] Full training of models
- [x] LoRA training
- [x] Sample packing
- [ ] FP16 and BF16 (awaiting AMP support for MPS in Pytorch)
- [ ] Tri-dao's flash-attn (until it is supported use spd_attention as an alternative)
- [ ] xformers
- [ ] bitsandbytes (meaning no 4/8 bits loading and bnb optimizers)
- [ ] qlora
- [ ] DeepSpeed
-
-Untested:
- FSDP
--- a/docs/multipack.md
+++ b/docs/multipack.md
@@ -1,11 +1,4 @@
-# Multipack (Sample Packing)
-
-## Visualization of Multipack with Flash Attention
-
-Because Flash Attention simply drops the attention mask, we do not need to
-construct a 4d attention mask. We only need to concatenate the sequences into
-a single batch and let flash attention know where each new sequence begins.
-
+# Multipack

 4k context, bsz =4,
 each character represents 256 tokens
@@ -56,18 +49,3 @@ w packing ( note it's the same effective number of tokens per step, but a true b
   E E E E F F F F F G G G H H H H
   I I I J J J J K K K K K L L L X ]]
 ```
-
-cu_seqlens:
-[[ 0, 11, 17, 24, 28, 36, 41 44, 48, 51, 55, 60, 64]]
-
-
-## Multipack without Flash Attention
-
-Multipack can still be achieved without Flash attention, but with lower packing
-efficiency as we are not able to join multiple batches into a single batch due to
-context length limits without flash attention. We can use either Pytorch's Scaled
-Dot Product Attention implementation or native Pytorch attention implementation
-along with [4d attention masks](https://github.com/huggingface/transformers/pull/27539)
-to pack sequences together and avoid cross attention.
-
-<img src="./images/4d-mask.png" alt="axolotl" width="800">
--- a/docs/rlhf.md
+++ b/docs/rlhf.md
@@ -12,21 +12,21 @@ feedback. Various methods include, but not limited to:

 ### RLHF using Axolotl

->[!IMPORTANT]
->This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.
+[!IMPORTANT]
+This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.

 The various RL training methods are implemented in trl and wrapped via axolotl. Below are various examples with how you can use various preference datasets to train models that use ChatML

 #### DPO
 ```yaml
-rl: dpo
+rl: true
 datasets:
  - path: Intel/orca_dpo_pairs
    split: train
-    type: chatml.intel
+    type: intel_apply_chatml
  - path: argilla/ultrafeedback-binarized-preferences
    split: train
-    type: chatml.argilla
+    type: argilla_apply_chatml
 ```

 #### IPO
@@ -34,16 +34,6 @@ datasets:
 rl: ipo
 ```

-#### Using local dataset files
-```yaml
-datasets:
-  - ds_type: json
-    data_files:
-      - orca_rlhf.jsonl
-    split: train
-    type: chatml.intel
-```
-
 #### Trl autounwrap for peft

 Trl supports autounwrapping peft models, so that a ref model does not need to be additionally loaded, leading to less VRAM needed. This is on by default. To turn it off, pass the following config.
--- a/examples/cerebras/btlm-ft.yml
+++ b/examples/cerebras/btlm-ft.yml
@@ -53,8 +53,8 @@ lr_quadratic_warmup: true
 learning_rate: 0.000085
 train_on_inputs: true
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true

 gradient_checkpointing: false
--- a/examples/cerebras/qlora.yml
+++ b/examples/cerebras/qlora.yml
@@ -11,6 +11,7 @@ val_set_size: 0.05
 adapter: qlora
 lora_model_dir:
 sequence_len: 2048
+max_packed_sequence_len: 2048
 lora_r: 16
 lora_alpha: 32
 lora_dropout: 0.05
@@ -35,8 +36,8 @@ lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true
 gradient_checkpointing: true
 early_stopping_patience:
--- a/examples/code-llama/13b/lora.yml
+++ b/examples/code-llama/13b/lora.yml
@@ -1,6 +1,7 @@
 base_model: codellama/CodeLlama-13b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: true
 load_in_4bit: false
@@ -40,8 +41,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -51,7 +52,6 @@ local_rank:
 logging_steps: 1
 xformers_attention:
 flash_attention: true
-s2_attention:

 warmup_steps: 10
 evals_per_epoch: 4
--- a/examples/code-llama/13b/qlora.yml
+++ b/examples/code-llama/13b/qlora.yml
@@ -1,6 +1,7 @@
 base_model: codellama/CodeLlama-13b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/code-llama/34b/lora.yml
+++ b/examples/code-llama/34b/lora.yml
@@ -1,6 +1,7 @@
 base_model: codellama/CodeLlama-34b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: true
 load_in_4bit: false
@@ -40,8 +41,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -51,7 +52,6 @@ local_rank:
 logging_steps: 1
 xformers_attention:
 flash_attention: true
-s2_attention:

 warmup_steps: 10
 evals_per_epoch: 4
--- a/examples/code-llama/34b/qlora.yml
+++ b/examples/code-llama/34b/qlora.yml
@@ -1,6 +1,7 @@
 base_model: codellama/CodeLlama-34b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/code-llama/7b/lora.yml
+++ b/examples/code-llama/7b/lora.yml
@@ -1,6 +1,7 @@
 base_model: codellama/CodeLlama-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: true
 load_in_4bit: false
@@ -40,8 +41,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -51,7 +52,6 @@ local_rank:
 logging_steps: 1
 xformers_attention:
 flash_attention: true
-s2_attention:

 warmup_steps: 10
 evals_per_epoch: 4
--- a/examples/code-llama/7b/qlora.yml
+++ b/examples/code-llama/7b/qlora.yml
@@ -1,6 +1,7 @@
 base_model: codellama/CodeLlama-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/colab-notebooks/colab-axolotl-example.ipynb
+++ b/examples/colab-notebooks/colab-axolotl-example.ipynb
@@ -1,216 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AKjdG7tbTb-n"
-      },
-      "source": [
-        "# Example notebook for running Axolotl on google colab"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "RcbNpOgWRcii"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "# Check so there is a gpu available, a T4(free tier) is enough to run this notebook\n",
-        "assert (torch.cuda.is_available()==True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h3nLav8oTRA5"
-      },
-      "source": [
-        "## Install Axolotl and dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3c3yGAwnOIdi",
-        "outputId": "e3777b5a-40ef-424f-e181-62dfecd1dd01"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install torch==\"2.1.2\"\n",
-        "!pip install -e git+https://github.com/OpenAccess-AI-Collective/axolotl#egg=axolotl\n",
-        "!pip install flash-attn==\"2.5.0\"\n",
-        "!pip install deepspeed==\"0.13.1\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BW2MFr7HTjub"
-      },
-      "source": [
-        "## Create an yaml config file"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9pkF2dSoQEUN"
-      },
-      "outputs": [],
-      "source": [
-        "import yaml\n",
-        "\n",
-        "# Your YAML string\n",
-        "yaml_string = \"\"\"\n",
-        "base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
-        "model_type: LlamaForCausalLM\n",
-        "tokenizer_type: LlamaTokenizer\n",
-        "is_llama_derived_model: true\n",
-        "\n",
-        "load_in_8bit: false\n",
-        "load_in_4bit: true\n",
-        "strict: false\n",
-        "\n",
-        "datasets:\n",
-        "  - path: mhenrichsen/alpaca_2k_test\n",
-        "    type: alpaca\n",
-        "dataset_prepared_path:\n",
-        "val_set_size: 0.05\n",
-        "output_dir: ./qlora-out\n",
-        "\n",
-        "adapter: qlora\n",
-        "lora_model_dir:\n",
-        "\n",
-        "sequence_len: 1096\n",
-        "sample_packing: true\n",
-        "pad_to_sequence_len: true\n",
-        "\n",
-        "lora_r: 32\n",
-        "lora_alpha: 16\n",
-        "lora_dropout: 0.05\n",
-        "lora_target_modules:\n",
-        "lora_target_linear: true\n",
-        "lora_fan_in_fan_out:\n",
-        "\n",
-        "wandb_project:\n",
-        "wandb_entity:\n",
-        "wandb_watch:\n",
-        "wandb_name:\n",
-        "wandb_log_model:\n",
-        "\n",
-        "mlflow_experiment_name: colab-example\n",
-        "\n",
-        "gradient_accumulation_steps: 1\n",
-        "micro_batch_size: 1\n",
-        "num_epochs: 4\n",
-        "max_steps: 20\n",
-        "optimizer: paged_adamw_32bit\n",
-        "lr_scheduler: cosine\n",
-        "learning_rate: 0.0002\n",
-        "\n",
-        "train_on_inputs: false\n",
-        "group_by_length: false\n",
-        "bf16: false\n",
-        "fp16: true\n",
-        "tf32: false\n",
-        "\n",
-        "gradient_checkpointing: true\n",
-        "early_stopping_patience:\n",
-        "resume_from_checkpoint:\n",
-        "local_rank:\n",
-        "logging_steps: 1\n",
-        "xformers_attention:\n",
-        "flash_attention: false\n",
-        "\n",
-        "warmup_steps: 10\n",
-        "evals_per_epoch:\n",
-        "saves_per_epoch:\n",
-        "debug:\n",
-        "deepspeed:\n",
-        "weight_decay: 0.0\n",
-        "fsdp:\n",
-        "fsdp_config:\n",
-        "special_tokens:\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "# Convert the YAML string to a Python dictionary\n",
-        "yaml_dict = yaml.safe_load(yaml_string)\n",
-        "\n",
-        "# Specify your file path\n",
-        "file_path = 'test_axolotl.yaml'\n",
-        "\n",
-        "# Write the YAML file\n",
-        "with open(file_path, 'w') as file:\n",
-        "    yaml.dump(yaml_dict, file)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bidoj8YLTusD"
-      },
-      "source": [
-        "## Launch the training"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ydTI2Jk2RStU",
-        "outputId": "d6d0df17-4b53-439c-c802-22c0456d301b"
-      },
-      "outputs": [],
-      "source": [
-        "# Buy using the ! the comand will be executed as a bash command\n",
-        "!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Play with inference"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Buy using the ! the comand will be executed as a bash command\n",
-        "!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \\\n",
-        "    --qlora_model_dir=\"./qlora-out\" --gradio"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
--- a/examples/falcon/config-7b-lora.yml
+++ b/examples/falcon/config-7b-lora.yml
@@ -2,7 +2,7 @@ base_model: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-
+is_falcon_derived_model: true
 load_in_8bit: true
 load_in_4bit: false
 gptq: false
@@ -38,8 +38,8 @@ lr_scheduler: cosine
 learning_rate: 0.00003
 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true
 gradient_checkpointing: true
 early_stopping_patience:
@@ -60,5 +60,5 @@ fsdp:
 fsdp_config:
 special_tokens:
  pad_token: "<|endoftext|>"
-  bos_token: "<|endoftext|>"
+  bos_token: ">>ABSTRACT<<"
  eos_token: "<|endoftext|>"
--- a/examples/falcon/config-7b-qlora.yml
+++ b/examples/falcon/config-7b-qlora.yml
@@ -5,7 +5,7 @@ base_model: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-
+is_falcon_derived_model: true
 load_in_8bit: false
 # enable 4bit for QLoRA
 load_in_4bit: true
@@ -64,8 +64,8 @@ lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true
 gradient_checkpointing: true
 # stop training after this many evaluation losses have increased in a row
@@ -89,5 +89,5 @@ fsdp:
 fsdp_config:
 special_tokens:
  pad_token: "<|endoftext|>"
-  bos_token: "<|endoftext|>"
+  bos_token: ">>ABSTRACT<<"
  eos_token: "<|endoftext|>"
--- a/examples/falcon/config-7b.yml
+++ b/examples/falcon/config-7b.yml
@@ -2,7 +2,7 @@ base_model: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-
+is_falcon_derived_model: true
 load_in_8bit: false
 load_in_4bit: false
 gptq: false
@@ -38,8 +38,8 @@ lr_scheduler: cosine
 learning_rate: 0.00003
 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true
 gradient_checkpointing: true
 early_stopping_patience:
@@ -60,5 +60,5 @@ fsdp:
 fsdp_config:
 special_tokens:
  pad_token: "<|endoftext|>"
-  bos_token: "<|endoftext|>"
+  bos_token: ">>ABSTRACT<<"
  eos_token: "<|endoftext|>"
--- a/examples/gemma/qlora.yml
+++ b/examples/gemma/qlora.yml
@@ -1,65 +0,0 @@
-# use google/gemma-7b if you have access
-base_model: mhenrichsen/gemma-7b
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-val_set_size: 0.1
-output_dir: ./out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 3
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/gptj/qlora.yml
+++ b/examples/gptj/qlora.yml
@@ -33,8 +33,8 @@ lr_scheduler: cosine
 learning_rate: 0.0001
 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true
 gradient_checkpointing: true
 early_stopping_patience:
--- a/examples/jeopardy-bot/config.yml
+++ b/examples/jeopardy-bot/config.yml
@@ -31,7 +31,7 @@ lr_scheduler: cosine
 learning_rate: 0.00003
 train_on_inputs: false
 group_by_length: false
-bf16: auto
+bf16: true
 tf32: true
 early_stopping_patience:
 resume_from_checkpoint:
--- a/examples/llama-2/fft_optimized.yml
+++ b/examples/llama-2/fft_optimized.yml
@@ -1,6 +1,7 @@
 base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: false
@@ -40,8 +41,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -61,8 +62,11 @@ evals_per_epoch: 4
 eval_table_size:
 saves_per_epoch: 1
 debug:
-deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
+deepspeed: #deepspeed/zero2.json # multi-gpu only
 weight_decay: 0.1
 fsdp:
 fsdp_config:
 special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
--- a/examples/llama-2/gptq-lora.yml
+++ b/examples/llama-2/gptq-lora.yml
@@ -1,4 +1,5 @@
 base_model: TheBloke/Llama-2-7B-GPTQ
+is_llama_derived_model: false
 gptq: true
 gptq_disable_exllama: true
 model_type: AutoModelForCausalLM
--- a/examples/llama-2/loftq.yml
+++ b/examples/llama-2/loftq.yml
@@ -1,69 +0,0 @@
-base_model: NousResearch/Llama-2-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-peft:
-  loftq_config:
-    loftq_bits: 4
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/llama-2/lora.yml
+++ b/examples/llama-2/lora.yml
@@ -1,6 +1,7 @@
 base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: true
 load_in_4bit: false
@@ -40,8 +41,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -51,12 +52,11 @@ local_rank:
 logging_steps: 1
 xformers_attention:
 flash_attention: true
-s2_attention:

 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:
@@ -64,3 +64,6 @@ weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
--- a/examples/llama-2/qlora.yml
+++ b/examples/llama-2/qlora.yml
@@ -1,6 +1,7 @@
 base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -64,3 +65,6 @@ weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
--- a/examples/llama-2/relora.yml
+++ b/examples/llama-2/relora.yml
@@ -1,7 +1,7 @@
 base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -47,8 +47,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/mamba/config.yml
+++ b/examples/mamba/config.yml
@@ -34,8 +34,8 @@ learning_rate: 5e-5
 train_on_inputs: false
 group_by_length: true

-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true

 gradient_checkpointing: false
@@ -49,7 +49,7 @@ flash_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:
--- a/examples/mistral/Mistral-7b-example/README.md
+++ b/examples/mistral/Mistral-7b-example/README.md
@@ -1,12 +0,0 @@
-# Description
-This repository presents an in-depth guide for fine-tuning Mistral-7b or any other compatible model using Axolotl, tailored specifically for chatbot development. It streamlines the process of fine-tuning and uploading the enhanced model to HuggingFace 🤗, thereby serving as an invaluable tool for developers in the AI and chatbot domain.
-
-**What’s Inside:**
-
-Beginner-Friendly Instructions: Comprehensive steps to guide you through fine-tuning your chosen model, including details on the data structure (jsonl), configuration, and the code itself.
-
-Hardware Utilized: For reference, the fine-tuning in this guide was performed using 4x NVIDIA GeForce RTX 3090 (rented 2.1.2-cuda12.1-cudnn8-devel).
-
-**Uploading to HuggingFace 🤗:**
-To upload your fine-tuned model to Hugging Face, include the following files:
-![Screenshot 2024-01-19 213932](https://github.com/OpenAccess-AI-Collective/axolotl/assets/138583191/d660eb84-2d76-46a1-9846-cf0aeb3006d9)
--- a/examples/mistral/Mistral-7b-example/code.ipynb
+++ b/examples/mistral/Mistral-7b-example/code.ipynb
@@ -1,970 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3fe31229-8f6b-48bc-a86d-af8e5466d11c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GPU available? True\n",
-      "BF16 is supported? True\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check if GPU is available I used 4x NVIDIA GeForce RTX 3090 (rented 2.1.2-cuda12.1-cudnn8-devel)\n",
-    "import torch\n",
-    "print('GPU available?', torch.cuda.is_available())\n",
-    "print('BF16 is supported?', torch.cuda.is_bf16_supported())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1dee845b-f3cb-4b1e-bdd9-1a918eac140b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting huggingface_hub\n",
-      "  Downloading huggingface_hub-0.20.1-py3-none-any.whl.metadata (12 kB)\n",
-      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (3.9.0)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (2023.10.0)\n",
-      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (2.31.0)\n",
-      "Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (4.65.0)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (6.0.1)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (4.7.1)\n",
-      "Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (23.1)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub) (2.0.4)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub) (3.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub) (1.26.18)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub) (2023.7.22)\n",
-      "Downloading huggingface_hub-0.20.1-py3-none-any.whl (330 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m330.1/330.1 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
-      "\u001b[?25hInstalling collected packages: huggingface_hub\n",
-      "Successfully installed huggingface_hub-0.20.1\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install huggingface_hub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "88731672-9050-4034-8266-11aaace2a44e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6b5aa7d7-3b18-4c14-afd4-043c2c545259",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60df98d7b0294289aad8b6c8cd023c3b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Login to huggingface so you can push the model to hub later\n",
-    "import sys\n",
-    "stdout = sys.stdout\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b74d0635-5033-4494-b7bd-ff6822103d93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#I noticed that when you use notebook_login() nothing gets printed after so we use sys \n",
-    "sys.stdout = stdout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e3c3b088-45e7-484b-ae39-66beabc48da8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cloning into 'axolotl'...\n",
-      "remote: Enumerating objects: 235, done.\u001b[K\n",
-      "remote: Counting objects: 100% (235/235), done.\u001b[K\n",
-      "remote: Compressing objects: 100% (207/207), done.\u001b[K\n",
-      "remote: Total 235 (delta 48), reused 123 (delta 13), pack-reused 0\u001b[K\n",
-      "Receiving objects: 100% (235/235), 1.46 MiB | 11.65 MiB/s, done.\n",
-      "Resolving deltas: 100% (48/48), done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#axolotl\n",
-    "!git clone -b main --depth 1 https://github.com/OpenAccess-AI-Collective/axolotl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "66927751-4fd6-4477-97fc-6ab08c9d9a74",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/axolotl\n"
-     ]
-    }
-   ],
-   "source": [
-    "cd axolotl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fcccf8da-353b-4d70-8f55-5cfe08c7e6b9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (23.1)\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0mObtaining file:///axolotl\n",
-      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25hCollecting auto-gptq==0.5.1\n",
-      "  Downloading auto_gptq-0.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
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-      "Building wheels for collected packages: flash-attn, optimum, rouge-score, deepspeed, fire, ffmpy, wavedrom\n",
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-      "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
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-      "\u001b[?25h  Created wheel for wavedrom: filename=wavedrom-2.0.3.post3-py2.py3-none-any.whl size=30052 sha256=7f0cbd15d63ee9c120190bac122ab51bbbfc91ee374bc3c046fadb320816c17e\n",
-      "  Stored in directory: /root/.cache/pip/wheels/9c/52/8c/38b454b42f712f325e26f633287484c7dc1ad469e1580c5954\n",
-      "Successfully built flash-attn optimum rouge-score deepspeed fire ffmpy wavedrom\n",
-      "Installing collected packages: sentencepiece, pydub, py-cpuinfo, ninja, nh3, hjson, ffmpy, bitsandbytes, appdirs, addict, xxhash, wrapt, werkzeug, websockets, tzdata, typing-extensions, threadpoolctl, termcolor, tensorboard-data-server, svgwrite, smmap, shortuuid, setproctitle, sentry-sdk, semantic-version, scipy, safetensors, rouge, regex, python-multipart, pyparsing, pynvml, pyasn1, pyarrow-hotfix, pyarrow, protobuf, orjson, oauthlib, multidict, mdurl, markdown2, markdown, llvmlite, kiwisolver, joblib, jmespath, importlib-resources, humanfriendly, hf_transfer, h11, grpcio, google-crc32c, gekko, frozenlist, fonttools, einops, docker-pycreds, dill, cycler, contourpy, colorama, cachetools, async-timeout, art, aioitertools, aiofiles, absl-py, yarl, wavedrom, uvicorn, tiktoken, scikit-learn, rsa, responses, requests-oauthlib, pydantic, pyasn1-modules, pandas, numba, nltk, multiprocess, matplotlib, markdown-it-py, httpcore, googleapis-common-protos, google-resumable-media, gitdb, fire, coloredlogs, botocore, aiosignal, xformers, tokenizers, starlette, rouge-score, rich, httpx, google-auth, GitPython, flash-attn, deepspeed, aiohttp, accelerate, wandb, transformers, gradio-client, google-auth-oauthlib, google-api-core, fastapi, altair, aiobotocore, tensorboard, s3fs, peft, gradio, google-cloud-core, fschat, datasets, bert-score, optimum, google-cloud-storage, evaluate, auto-gptq, gcsfs, axolotl\n",
-      "  Attempting uninstall: typing-extensions\n",
-      "    Found existing installation: typing_extensions 4.7.1\n",
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-      "  Running setup.py develop for axolotl\n",
-      "Successfully installed GitPython-3.1.40 absl-py-2.0.0 accelerate-0.24.1 addict-2.4.0 aiobotocore-2.7.0 aiofiles-23.2.1 aiohttp-3.9.1 aioitertools-0.11.0 aiosignal-1.3.1 altair-5.2.0 appdirs-1.4.4 art-6.1 async-timeout-4.0.3 auto-gptq-0.5.1 axolotl-0.3.0 bert-score-0.3.13 bitsandbytes-0.41.3.post2 botocore-1.31.64 cachetools-5.3.2 colorama-0.4.6 coloredlogs-15.0.1 contourpy-1.2.0 cycler-0.12.1 datasets-2.16.0 deepspeed-0.12.6 dill-0.3.7 docker-pycreds-0.4.0 einops-0.7.0 evaluate-0.4.0 fastapi-0.108.0 ffmpy-0.3.1 fire-0.5.0 flash-attn-2.3.3 fonttools-4.47.0 frozenlist-1.4.1 fschat-0.2.34 gcsfs-2023.10.0 gekko-1.0.6 gitdb-4.0.11 google-api-core-2.15.0 google-auth-2.25.2 google-auth-oauthlib-1.2.0 google-cloud-core-2.4.1 google-cloud-storage-2.14.0 google-crc32c-1.5.0 google-resumable-media-2.7.0 googleapis-common-protos-1.62.0 gradio-3.50.2 gradio-client-0.6.1 grpcio-1.60.0 h11-0.14.0 hf_transfer-0.1.4 hjson-3.1.0 httpcore-1.0.2 httpx-0.26.0 humanfriendly-10.0 importlib-resources-6.1.1 jmespath-1.0.1 joblib-1.3.2 kiwisolver-1.4.5 llvmlite-0.41.1 markdown-3.5.1 markdown-it-py-3.0.0 markdown2-2.4.12 matplotlib-3.8.2 mdurl-0.1.2 multidict-6.0.4 multiprocess-0.70.15 nh3-0.2.15 ninja-1.11.1.1 nltk-3.8.1 numba-0.58.1 oauthlib-3.2.2 optimum-1.13.2 orjson-3.9.10 pandas-2.1.4 peft-0.6.0 protobuf-4.23.4 py-cpuinfo-9.0.0 pyarrow-14.0.2 pyarrow-hotfix-0.6 pyasn1-0.5.1 pyasn1-modules-0.3.0 pydantic-1.10.13 pydub-0.25.1 pynvml-11.5.0 pyparsing-3.1.1 python-multipart-0.0.6 regex-2023.12.25 requests-oauthlib-1.3.1 responses-0.18.0 rich-13.7.0 rouge-1.0.1 rouge-score-0.1.2 rsa-4.9 s3fs-2023.10.0 safetensors-0.4.1 scikit-learn-1.2.2 scipy-1.11.4 semantic-version-2.10.0 sentencepiece-0.1.99 sentry-sdk-1.39.1 setproctitle-1.3.3 shortuuid-1.0.11 smmap-5.0.1 starlette-0.32.0.post1 svgwrite-1.4.3 tensorboard-2.15.1 tensorboard-data-server-0.7.2 termcolor-2.4.0 threadpoolctl-3.2.0 tiktoken-0.5.2 tokenizers-0.15.0 transformers-4.36.2 typing-extensions-4.8.0 tzdata-2023.3 uvicorn-0.25.0 wandb-0.16.1 wavedrom-2.0.3.post3 websockets-11.0.3 werkzeug-3.0.1 wrapt-1.16.0 xformers-0.0.23 xxhash-3.4.1 yarl-1.9.4\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0mCollecting git+https://github.com/huggingface/peft.git\n",
-      "  Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-hka8xgk2\n",
-      "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-hka8xgk2\n",
-      "  Resolved https://github.com/huggingface/peft.git to commit cf04d0353f0343cbf66627228c4495f51669af34\n",
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-      "Requirement already satisfied: accelerate>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.24.1)\n",
-      "Requirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.4.1)\n",
-      "Requirement already satisfied: huggingface-hub>=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.20.1)\n",
-      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (3.9.0)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2023.10.0)\n",
-      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2.31.0)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (4.8.0)\n",
-      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (1.11.1)\n",
-      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (3.1)\n",
-      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (3.1.2)\n",
-      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers->peft==0.7.2.dev0) (2023.12.25)\n",
-      "Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers->peft==0.7.2.dev0) (0.15.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.13.0->peft==0.7.2.dev0) (2.1.1)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2.0.4)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (3.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (1.26.18)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2023.7.22)\n",
-      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.13.0->peft==0.7.2.dev0) (1.3.0)\n",
-      "Building wheels for collected packages: peft\n",
-      "  Building wheel for peft (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for peft: filename=peft-0.7.2.dev0-py3-none-any.whl size=169456 sha256=4c70d23e759fa6abb3827fb2f3a8683be3b24d78777d0f403bbc2c0548e5dd4b\n",
-      "  Stored in directory: /tmp/pip-ephem-wheel-cache-my5ncou6/wheels/d7/c7/de/1368fac8590e1b103ddc2ec2a28ad51d83aded1a3830e8a087\n",
-      "Successfully built peft\n",
-      "Installing collected packages: peft\n",
-      "  Attempting uninstall: peft\n",
-      "    Found existing installation: peft 0.6.0\n",
-      "    Uninstalling peft-0.6.0:\n",
-      "      Successfully uninstalled peft-0.6.0\n",
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-      "axolotl 0.3.0 requires peft==0.6.0, but you have peft 0.7.2.dev0 which is incompatible.\u001b[0m\u001b[31m\n",
-      "\u001b[0mSuccessfully installed peft-0.7.2.dev0\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "#instaling what is needed inside axolotl file\n",
-    "!pip install packaging\n",
-    "!pip install -e '.[flash-attn,deepspeed]'\n",
-    "!pip install -U git+https://github.com/huggingface/peft.git"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "82d1a380-1e87-48fe-89fe-25331326014d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following values were not passed to `accelerate launch` and had defaults used instead:\n",
-      "\t`--num_processes` was set to a value of `3`\n",
-      "\t\tMore than one GPU was found, enabling multi-GPU training.\n",
-      "\t\tIf this was unintended please pass in `--num_processes=1`.\n",
-      "\t`--num_machines` was set to a value of `1`\n",
-      "\t`--mixed_precision` was set to a value of `'no'`\n",
-      "\t`--dynamo_backend` was set to a value of `'no'`\n",
-      "To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\n",
-      "/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\n",
-      "  warnings.warn(\n",
-      "[2023-12-28 15:44:09,979] [INFO] [datasets.<module>:58] [PID:2814] PyTorch version 2.1.1 available.\n",
-      "/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\n",
-      "  warnings.warn(\n",
-      "/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\n",
-      "  warnings.warn(\n",
-      "[2023-12-28 15:44:10,011] [INFO] [datasets.<module>:58] [PID:2812] PyTorch version 2.1.1 available.\n",
-      "[2023-12-28 15:44:10,013] [INFO] [datasets.<module>:58] [PID:2813] PyTorch version 2.1.1 available.\n",
-      "[2023-12-28 15:44:10,805] [INFO] [axolotl.normalize_config:150] [PID:2814] [RANK:2] GPU memory usage baseline: 0.000GB (+0.317GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:10,830] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
-      "[2023-12-28 15:44:10,842] [INFO] [axolotl.normalize_config:150] [PID:2813] [RANK:1] GPU memory usage baseline: 0.000GB (+0.317GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:10,865] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
-      "[2023-12-28 15:44:10,869] [INFO] [axolotl.normalize_config:150] [PID:2812] [RANK:0] GPU memory usage baseline: 0.000GB (+0.351GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:10,887] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
-      "[2023-12-28 15:44:10,961] [INFO] [comm.py:637:init_distributed] cdb=None\n",
-      "[2023-12-28 15:44:10,994] [INFO] [comm.py:637:init_distributed] cdb=None\n",
-      "[2023-12-28 15:44:11,015] [INFO] [comm.py:637:init_distributed] cdb=None\n",
-      "[2023-12-28 15:44:11,015] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl\n",
-      "                                 dP            dP   dP \n",
-      "                                 88            88   88 \n",
-      "      .d8888b. dP.  .dP .d8888b. 88 .d8888b. d8888P 88 \n",
-      "      88'  `88  `8bd8'  88'  `88 88 88'  `88   88   88 \n",
-      "      88.  .88  .d88b.  88.  .88 88 88.  .88   88   88 \n",
-      "      `88888P8 dP'  `dP `88888P' dP `88888P'   dP   dP \n",
-      "                                                       \n",
-      "                                                       \n",
-      "\n",
-      "[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:184] [PID:2812] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:185] [PID:2812] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:186] [PID:2812] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:187] [PID:2812] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
-      "[2023-12-28 15:44:11,413] [INFO] [axolotl.load_tokenized_prepared_datasets:143] [PID:2812] [RANK:0] Loading prepared dataset from disk at tilemachos/GF_new.json/1adc45d2edc1e98ce657814412c6593c...\u001b[39m\n",
-      "[2023-12-28 15:44:11,415] [INFO] [axolotl.load_tokenized_prepared_datasets:145] [PID:2812] [RANK:0] Prepared dataset loaded from disk...\u001b[39m\n",
-      "[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:184] [PID:2814] [RANK:2] EOS: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:185] [PID:2814] [RANK:2] BOS: 1 / <s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:186] [PID:2814] [RANK:2] PAD: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:187] [PID:2814] [RANK:2] UNK: 0 / <unk>\u001b[39m\n",
-      "[2023-12-28 15:44:11,530] [DEBUG] [axolotl.load_tokenizer:184] [PID:2813] [RANK:1] EOS: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,531] [DEBUG] [axolotl.load_tokenizer:185] [PID:2813] [RANK:1] BOS: 1 / <s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,531] [DEBUG] [axolotl.load_tokenizer:186] [PID:2813] [RANK:1] PAD: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:11,531] [DEBUG] [axolotl.load_tokenizer:187] [PID:2813] [RANK:1] UNK: 0 / <unk>\u001b[39m\n",
-      "[2023-12-28 15:44:12,158] [INFO] [axolotl.load_tokenized_prepared_datasets:143] [PID:2813] [RANK:1] Loading prepared dataset from disk at tilemachos/GF_new.json/1adc45d2edc1e98ce657814412c6593c...\u001b[39m\n",
-      "[2023-12-28 15:44:12,158] [INFO] [axolotl.load_tokenized_prepared_datasets:143] [PID:2814] [RANK:2] Loading prepared dataset from disk at tilemachos/GF_new.json/1adc45d2edc1e98ce657814412c6593c...\u001b[39m\n",
-      "[2023-12-28 15:44:12,160] [INFO] [axolotl.load_tokenized_prepared_datasets:145] [PID:2813] [RANK:1] Prepared dataset loaded from disk...\u001b[39m\n",
-      "[2023-12-28 15:44:12,161] [INFO] [axolotl.load_tokenized_prepared_datasets:145] [PID:2814] [RANK:2] Prepared dataset loaded from disk...\u001b[39m\n",
-      "[2023-12-28 15:44:12,236] [DEBUG] [axolotl.log:60] [PID:2812] [RANK:0] total_num_tokens: 28120\u001b[39m\n",
-      "[2023-12-28 15:44:12,238] [DEBUG] [axolotl.log:60] [PID:2812] [RANK:0] `total_supervised_tokens: 7990`\u001b[39m\n",
-      "[2023-12-28 15:44:12,238] [DEBUG] [axolotl.log:60] [PID:2812] [RANK:0] total_num_steps: 6\u001b[39m\n",
-      "[2023-12-28 15:44:12,242] [DEBUG] [axolotl.train.log:60] [PID:2812] [RANK:0] loading tokenizer... mistralai/Mistral-7B-v0.1\u001b[39m\n",
-      "[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:184] [PID:2812] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:185] [PID:2812] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:186] [PID:2812] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:187] [PID:2812] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
-      "[2023-12-28 15:44:12,518] [DEBUG] [axolotl.train.log:60] [PID:2812] [RANK:0] loading model and peft_config...\u001b[39m\n",
-      "[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:184] [PID:2814] [RANK:2] EOS: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:185] [PID:2814] [RANK:2] BOS: 1 / <s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:186] [PID:2814] [RANK:2] PAD: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:187] [PID:2814] [RANK:2] UNK: 0 / <unk>\u001b[39m\n",
-      "[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:184] [PID:2813] [RANK:1] EOS: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:185] [PID:2813] [RANK:1] BOS: 1 / <s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:186] [PID:2813] [RANK:1] PAD: 2 / </s>\u001b[39m\n",
-      "[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:187] [PID:2813] [RANK:1] UNK: 0 / <unk>\u001b[39m\n",
-      "[2023-12-28 15:44:13,049] [INFO] [partition_parameters.py:348:__exit__] finished initializing model - num_params = 291, num_elems = 7.24B\n",
-      "Loading checkpoint shards: 100%|██████████████████| 2/2 [00:11<00:00,  5.81s/it]\n",
-      "Loading checkpoint shards: 100%|██████████████████| 2/2 [00:11<00:00,  5.98s/it]\n",
-      "[2023-12-28 15:44:25,395] [INFO] [axolotl.load_model:503] [PID:2813] [RANK:1] GPU memory usage after model load: 7.576GB (+0.524GB cache, +0.708GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:25,399] [INFO] [axolotl.load_model:526] [PID:2813] [RANK:1] converting PEFT model w/ prepare_model_for_kbit_training\u001b[39m\n",
-      "[2023-12-28 15:44:25,403] [INFO] [axolotl.load_model:538] [PID:2813] [RANK:1] converting modules to torch.bfloat16 for flash attention\u001b[39m\n",
-      "trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836\n",
-      "[2023-12-28 15:44:25,480] [INFO] [axolotl.load_model:568] [PID:2813] [RANK:1] GPU memory usage after adapters: 7.589GB (+1.501GB cache, +0.708GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:25,572] [INFO] [axolotl.load_model:503] [PID:2814] [RANK:2] GPU memory usage after model load: 7.576GB (+0.410GB cache, +0.708GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:25,576] [INFO] [axolotl.load_model:526] [PID:2814] [RANK:2] converting PEFT model w/ prepare_model_for_kbit_training\u001b[39m\n",
-      "[2023-12-28 15:44:25,580] [INFO] [axolotl.load_model:538] [PID:2814] [RANK:2] converting modules to torch.bfloat16 for flash attention\u001b[39m\n",
-      "trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836\n",
-      "[2023-12-28 15:44:25,660] [INFO] [axolotl.load_model:568] [PID:2814] [RANK:2] GPU memory usage after adapters: 7.589GB (+1.388GB cache, +0.708GB misc)\u001b[39m\n",
-      "Loading checkpoint shards: 100%|██████████████████| 2/2 [00:12<00:00,  6.30s/it]\n",
-      "[2023-12-28 15:44:26,170] [INFO] [axolotl.load_model:503] [PID:2812] [RANK:0] GPU memory usage after model load: 7.576GB (+0.776GB cache, +0.741GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:26,177] [INFO] [axolotl.load_model:526] [PID:2812] [RANK:0] converting PEFT model w/ prepare_model_for_kbit_training\u001b[39m\n",
-      "[2023-12-28 15:44:26,181] [INFO] [axolotl.load_model:538] [PID:2812] [RANK:0] converting modules to torch.bfloat16 for flash attention\u001b[39m\n",
-      "trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836\n",
-      "[2023-12-28 15:44:26,259] [INFO] [axolotl.load_model:568] [PID:2812] [RANK:0] GPU memory usage after adapters: 7.589GB (+1.753GB cache, +0.741GB misc)\u001b[39m\n",
-      "[2023-12-28 15:44:26,293] [INFO] [axolotl.train.log:60] [PID:2812] [RANK:0] Pre-saving adapter config to ./out\u001b[39m\n",
-      "[2023-12-28 15:44:26,296] [INFO] [axolotl.train.log:60] [PID:2812] [RANK:0] Starting trainer...\u001b[39m\n",
-      "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n",
-      "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n",
-      "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n",
-      "Detected CUDA files, patching ldflags\n",
-      "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/fused_adam/build.ninja...\n",
-      "Building extension module fused_adam...\n",
-      "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n",
-      "ninja: no work to do.\n",
-      "Loading extension module fused_adam...\n",
-      "Time to load fused_adam op: 0.05891108512878418 seconds\n",
-      "Loading extension module fused_adam...\n",
-      "Time to load fused_adam op: 0.10173463821411133 seconds\n",
-      "Loading extension module fused_adam...\n",
-      "Time to load fused_adam op: 0.10152459144592285 seconds\n",
-      "/opt/conda/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1699449201336/work/torch/csrc/tensor/python_tensor.cpp:83.)\n",
-      "  self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n",
-      "/opt/conda/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1699449201336/work/torch/csrc/tensor/python_tensor.cpp:83.)\n",
-      "  self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n",
-      "/opt/conda/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1699449201336/work/torch/csrc/tensor/python_tensor.cpp:83.)\n",
-      "  self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n",
-      "Parameter Offload: Total persistent parameters: 3674112 in 193 params\n",
-      "  0%|                                                    | 0/17 [00:00<?, ?it/s]/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
-      "  warnings.warn(\n",
-      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
-      "  warnings.warn(\n",
-      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
-      "  warnings.warn(\n",
-      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization\n",
-      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
-      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization\n",
-      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
-      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization\n",
-      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
-      "{'loss': 2.0448, 'learning_rate': 2e-05, 'epoch': 0.06}                         \n",
-      "  6%|██▌                                         | 1/17 [00:28<07:32, 28.30s/it]\n",
-      "  0%|                                                     | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      " 67%|██████████████████████████████               | 2/3 [00:03<00:01,  1.85s/it]\u001b[A\n",
-      "                                                                                \u001b[A\n",
-      "\u001b[A{'eval_loss': 1.9694719314575195, 'eval_runtime': 11.391, 'eval_samples_per_second': 1.492, 'eval_steps_per_second': 0.263, 'epoch': 0.06}\n",
-      "  6%|██▌                                         | 1/17 [00:39<07:32, 28.30s/it]\n",
-      "100%|█████████████████████████████████████████████| 3/3 [00:07<00:00,  2.65s/it]\u001b[A\n",
-      "                                                                                \u001b[A[2023-12-28 15:45:35,358] [INFO] [axolotl.callbacks.on_step_end:122] [PID:2812] [RANK:0] GPU memory usage while training: 12.210GB (+4.259GB cache, +0.776GB misc)\u001b[39m\n",
-      " 12%|█████▏                                      | 2/17 [01:04<08:18, 33.20s/it][2023-12-28 15:45:35,358] [INFO] [axolotl.callbacks.on_step_end:122] [PID:2814] [RANK:2] GPU memory usage while training: 12.269GB (+4.522GB cache, +0.743GB misc)\u001b[39m\n",
-      "[2023-12-28 15:45:35,358] [INFO] [axolotl.callbacks.on_step_end:122] [PID:2813] [RANK:1] GPU memory usage while training: 12.283GB (+4.493GB cache, +0.743GB misc)\u001b[39m\n",
-      "{'loss': 2.0022, 'learning_rate': 4e-05, 'epoch': 0.12}                         \n",
-      "{'loss': 2.1054, 'learning_rate': 6e-05, 'epoch': 0.17}                         \n",
-      "{'loss': 1.9004, 'learning_rate': 8e-05, 'epoch': 0.23}                         \n",
-      "{'loss': 1.8794, 'learning_rate': 0.0001, 'epoch': 0.29}                        \n",
-      " 29%|████████████▉                               | 5/17 [02:20<05:23, 26.92s/it]\n",
-      "  0%|                                                     | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      " 67%|██████████████████████████████               | 2/3 [00:03<00:01,  1.88s/it]\u001b[A\n",
-      "                                                                                \u001b[A\n",
-      "\u001b[A{'eval_loss': 1.7912336587905884, 'eval_runtime': 11.3106, 'eval_samples_per_second': 1.503, 'eval_steps_per_second': 0.265, 'epoch': 0.29}\n",
-      " 29%|████████████▉                               | 5/17 [02:32<05:23, 26.92s/it]\n",
-      "100%|█████████████████████████████████████████████| 3/3 [00:07<00:00,  2.67s/it]\u001b[A\n",
-      "{'loss': 1.7871, 'learning_rate': 0.00012, 'epoch': 0.35}                       \u001b[A\n",
-      "{'loss': 1.7758, 'learning_rate': 0.00014, 'epoch': 0.4}                        \n",
-      "{'loss': 1.4645, 'learning_rate': 0.00016, 'epoch': 0.46}                       \n",
-      "{'loss': 1.4009, 'learning_rate': 0.00018, 'epoch': 0.52}                       \n",
-      "{'loss': 1.3927, 'learning_rate': 0.0002, 'epoch': 0.58}                        \n",
-      " 59%|█████████████████████████▎                 | 10/17 [04:38<03:04, 26.33s/it]\n",
-      "  0%|                                                     | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      " 67%|██████████████████████████████               | 2/3 [00:03<00:01,  1.89s/it]\u001b[A\n",
-      "                                                                                \u001b[A\n",
-      "\u001b[A{'eval_loss': 1.1426481008529663, 'eval_runtime': 11.3344, 'eval_samples_per_second': 1.5, 'eval_steps_per_second': 0.265, 'epoch': 0.58}\n",
-      " 59%|█████████████████████████▎                 | 10/17 [04:49<03:04, 26.33s/it]\n",
-      "100%|█████████████████████████████████████████████| 3/3 [00:07<00:00,  2.68s/it]\u001b[A\n",
-      "{'loss': 1.0122, 'learning_rate': 0.0001900968867902419, 'epoch': 0.63}         \u001b[A\n",
-      "{'loss': 1.0019, 'learning_rate': 0.00016234898018587337, 'epoch': 0.69}        \n",
-      "{'loss': 0.8976, 'learning_rate': 0.00012225209339563145, 'epoch': 0.75}        \n",
-      "{'loss': 0.9301, 'learning_rate': 7.774790660436858e-05, 'epoch': 0.81}         \n",
-      "{'loss': 0.8595, 'learning_rate': 3.7651019814126654e-05, 'epoch': 0.87}        \n",
-      " 88%|█████████████████████████████████████▉     | 15/17 [06:55<00:52, 26.17s/it]\n",
-      "  0%|                                                     | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      " 67%|██████████████████████████████               | 2/3 [00:03<00:01,  1.88s/it]\u001b[A\n",
-      "                                                                                \u001b[A\n",
-      "\u001b[A{'eval_loss': 0.8175248503684998, 'eval_runtime': 11.2932, 'eval_samples_per_second': 1.505, 'eval_steps_per_second': 0.266, 'epoch': 0.87}\n",
-      " 88%|█████████████████████████████████████▉     | 15/17 [07:06<00:52, 26.17s/it]\n",
-      "100%|█████████████████████████████████████████████| 3/3 [00:07<00:00,  2.67s/it]\u001b[A\n",
-      "{'loss': 0.7931, 'learning_rate': 9.903113209758096e-06, 'epoch': 0.92}         \u001b[A\n",
-      "{'loss': 0.6909, 'learning_rate': 0.0, 'epoch': 0.98}                           \n",
-      "100%|███████████████████████████████████████████| 17/17 [07:56<00:00, 28.03s/it]/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n",
-      "  warnings.warn(\n",
-      "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n",
-      "  warnings.warn(\n",
-      "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n",
-      "  warnings.warn(\n",
-      "{'train_runtime': 489.0649, 'train_samples_per_second': 0.63, 'train_steps_per_second': 0.035, 'train_loss': 1.408153467318591, 'epoch': 0.98}\n",
-      "100%|███████████████████████████████████████████| 17/17 [08:09<00:00, 28.77s/it]\n",
-      "[2023-12-28 15:52:39,488] [INFO] [axolotl.train.log:60] [PID:2812] [RANK:0] Training Completed!!! Saving pre-trained model to ./out\u001b[39m\n",
-      "\u001b[0m\u001b[0m\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "Training using the config.yml file and using deepspeed:zero3_bf16 the most aggressive optimization out of zero1,zero2,zero3 stages which partitions \n",
-    "not only optimizer states but also gradients and parameters across GPUs. The bf16 indicate mixed precision training using bfloat16.\n",
-    "For more information read axolotl's readme\n",
-    "\"\"\"\n",
-    "!accelerate launch -m axolotl.cli.train /folder/config.yml --deepspeed deepspeed_configs/zero3_bf16.json"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/examples/mistral/Mistral-7b-example/config.yml
+++ b/examples/mistral/Mistral-7b-example/config.yml
@@ -1,74 +0,0 @@
-#Mistral-7b
-base_model: mistralai/Mistral-7B-v0.1
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: tilemachos/Demo-Dataset #Path to json dataset file in huggingface
-    #for type,conversation arguments read axolotl readme and pick what is suited for your project, I wanted a chatbot and put sharegpt and chatml
-    type: sharegpt
-    conversation: chatml
-dataset_prepared_path: tilemachos/Demo-Dataset #Path to json dataset file in huggingface
-val_set_size: 0.05
-output_dir: ./out
-
-#using lora for lower cost
-adapter: lora
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-
-sequence_len: 512
-sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-#only 2 epochs because of small dataset
-gradient_accumulation_steps: 3
-micro_batch_size: 2
-num_epochs: 2
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-#default deepspeed, can use more aggresive if needed like zero2, zero3
-deepspeed: deepspeed_configs/zero1.json
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
--- a/examples/mistral/Mistral-7b-example/data.jsonl
+++ b/examples/mistral/Mistral-7b-example/data.jsonl
@@ -1,10 +0,0 @@
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: Who is the Founder of Apple\""}, {"from": "gpt", "value": "\"<Chatbot>: The founder of Apple is Steve Jobs\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: What is the capital of France?\""}, {"from": "gpt", "value": "\"<Chatbot>: The capital of France is Paris.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: How far is the Moon from Earth?\""}, {"from": "gpt", "value": "\"<Chatbot>: The Moon is approximately 384,400 kilometers from Earth.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: What is the tallest mountain in the world?\""}, {"from": "gpt", "value": "\"<Chatbot>: The tallest mountain in the world is Mount Everest.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: Who wrote Romeo and Juliet?\""}, {"from": "gpt", "value": "\"<Chatbot>: Romeo and Juliet was written by William Shakespeare.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: What is the boiling point of water?\""}, {"from": "gpt", "value": "\"<Chatbot>: The boiling point of water is 100 degrees Celsius.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: When was the first man on the moon?\""}, {"from": "gpt", "value": "\"<Chatbot>: The first man landed on the moon in 1969.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: What is the largest ocean?\""}, {"from": "gpt", "value": "\"<Chatbot>: The largest ocean is the Pacific Ocean.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: Who invented the telephone?\""}, {"from": "gpt", "value": "\"<Chatbot>: The telephone was invented by Alexander Graham Bell.\""}]}
-{"conversations": [{"from": "Customer", "value": "\"<Customer>: What is the formula for water?\""}, {"from": "gpt", "value": "\"<Chatbot>: The chemical formula for water is H2O.\""}]}
--- a/examples/mistral/README.md
+++ b/examples/mistral/README.md
@@ -8,5 +8,5 @@ accelerate launch -m axolotl.cli.train examples/mistral/config.yml

 If you run into CUDA OOM, use deepspeed with config zero2.json:
 ```shell
-accelerate launch -m axolotl.cli.train examples/mistral/config.yml --deepspeed deepspeed_configs/zero2.json
+accelerate launch -m axolotl.cli.train examples/mistral/config.yml --deepspeed deepspeed/zero2.json
 ```
--- a/examples/mistral/config.yml
+++ b/examples/mistral/config.yml
@@ -1,6 +1,7 @@
 base_model: mistralai/Mistral-7B-v0.1
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true

 load_in_8bit: false
 load_in_4bit: false
@@ -33,8 +34,8 @@ learning_rate: 0.000005

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -48,7 +49,7 @@ flash_attention: true
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:
--- a/examples/mistral/lora-mps.yml
+++ b/examples/mistral/lora-mps.yml
@@ -1,79 +0,0 @@
-base_model: mistralai/Mistral-7B-v0.1
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./lora-out
-eval_sample_packing: false
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 2
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: true
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: false
-sdp_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_table_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/mistral/mixtral.yml
+++ b/examples/mistral/mixtral.yml
@@ -63,8 +63,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -81,10 +81,10 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
-deepspeed: deepspeed_configs/zero2.json
+deepspeed: deepspeed/zero2.json
 weight_decay: 0.0
 fsdp:
 fsdp_config:
--- a/examples/mistral/qlora.yml
+++ b/examples/mistral/qlora.yml
@@ -1,6 +1,7 @@
 base_model: mistralai/Mistral-7B-v0.1
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -49,8 +50,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
@@ -67,7 +68,7 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:
--- a/examples/mpt-7b/config.yml
+++ b/examples/mpt-7b/config.yml
@@ -33,7 +33,7 @@ lr_scheduler: cosine
 learning_rate: 0.0000002
 train_on_inputs: false
 group_by_length: false
-bf16: auto
+bf16: true
 tf32: true
 early_stopping_patience:
 resume_from_checkpoint:
--- a/examples/openllama-3b/lora.yml
+++ b/examples/openllama-3b/lora.yml
@@ -52,7 +52,6 @@ logging_steps: 1
 xformers_attention:
 flash_attention: true
 gptq_groupsize:
-s2_attention:
 gptq_model_v1:
 warmup_steps: 20
 evals_per_epoch: 4
--- a/examples/phi/README.md
+++ b/examples/phi/README.md
@@ -3,7 +3,7 @@
 Due to some nuances with the phi code, please use deepspeed when training phi for full finetune.

 ```shell
-accelerate launch -m axolotl.cli.train examples/phi/phi-ft.yml --deepspeed deepspeed_configs/zero1.json
+accelerate launch -m axolotl.cli.train examples/phi/phi-ft.yml --deepspeed deepspeed/zero1.json

 # OR

--- a/examples/phi/phi-ft.yml
+++ b/examples/phi/phi-ft.yml
@@ -1,6 +1,8 @@
 base_model: microsoft/phi-1_5
-model_type: AutoModelForCausalLM
+model_type: PhiForCausalLM
 tokenizer_type: AutoTokenizer
+is_llama_derived_model: false
+trust_remote_code: true

 load_in_8bit: false
 load_in_4bit: false
@@ -16,7 +18,7 @@ output_dir: ./phi-sft-out

 sequence_len: 2048
 sample_packing: true
-pad_to_sequence_len: true
+pad_to_sequence_len:

 adapter:
 lora_model_dir:
@@ -33,7 +35,7 @@ wandb_name:
 wandb_log_model:

 gradient_accumulation_steps: 1
-micro_batch_size: 2
+micro_batch_size: 1
 num_epochs: 4
 optimizer: adamw_torch
 adam_beta2: 0.95
@@ -43,20 +45,18 @@ lr_scheduler: cosine
 learning_rate: 0.000003

 train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
+group_by_length: true
+bf16: true
+fp16: false
 tf32: true

-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: True
+gradient_checkpointing:
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
 xformers_attention:
-flash_attention: true
+flash_attention:

 warmup_steps: 100
 evals_per_epoch: 4
@@ -68,4 +68,7 @@ fsdp:
 fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
+  bos_token: "<|endoftext|>"
+  eos_token: "<|endoftext|>"
+  unk_token: "<|endoftext|>"
  pad_token: "<|endoftext|>"
--- a/examples/phi/phi-qlora.yml
+++ b/examples/phi/phi-qlora.yml
@@ -1,6 +1,8 @@
 base_model: microsoft/phi-1_5
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+is_llama_derived_model: false
+trust_remote_code: true

 load_in_8bit: false
 load_in_4bit: true
@@ -14,9 +16,9 @@ dataset_prepared_path:
 val_set_size: 0.05
 output_dir: ./phi-sft-out

-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
+sequence_len: 1024
+sample_packing: false  # not CURRENTLY compatible with LoRAs
+pad_to_sequence_len:

 adapter: qlora
 lora_model_dir:
@@ -33,7 +35,7 @@ wandb_name:
 wandb_log_model:

 gradient_accumulation_steps: 1
-micro_batch_size: 2
+micro_batch_size: 1
 num_epochs: 4
 optimizer: adamw_torch
 adam_beta2: 0.95
@@ -43,20 +45,18 @@ lr_scheduler: cosine
 learning_rate: 0.000003

 train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
+group_by_length: true
+bf16: true
+fp16: false
 tf32: true

-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: True
+gradient_checkpointing:
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
 xformers_attention:
-flash_attention: true
+flash_attention:

 warmup_steps: 100
 evals_per_epoch: 4
@@ -68,4 +68,7 @@ fsdp:
 fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
+  bos_token: "<|endoftext|>"
+  eos_token: "<|endoftext|>"
+  unk_token: "<|endoftext|>"
  pad_token: "<|endoftext|>"
--- a/examples/phi/phi2-ft.yml
+++ b/examples/phi/phi2-ft.yml
@@ -1,6 +1,8 @@
 base_model: microsoft/phi-2
+model_revision:  834565c  # pin model repo to the previous architecture
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+trust_remote_code: true

 load_in_8bit: false
 load_in_4bit: false
@@ -15,16 +17,19 @@ val_set_size: 0.05
 output_dir: ./phi-sft-out

 sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
+sample_packing: false  # currently unsupported
+pad_to_sequence_len:

 adapter:
 lora_model_dir:
-lora_r:
-lora_alpha:
-lora_dropout:
-lora_target_linear:
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.1
+lora_target_linear: true
 lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embd
+  - lm_head

 wandb_project:
 wandb_entity:
@@ -33,24 +38,22 @@ wandb_name:
 wandb_log_model:

 gradient_accumulation_steps: 1
-micro_batch_size: 2
+micro_batch_size: 1
 num_epochs: 4
-optimizer: adamw_torch
+optimizer: paged_adamw_8bit
 adam_beta2: 0.95
 adam_epsilon: 0.00001
 max_grad_norm: 1.0
 lr_scheduler: cosine
-learning_rate: 0.000003
+learning_rate: 1e-5

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: true

 gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: True
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
--- a/examples/pythia/lora.yml
+++ b/examples/pythia/lora.yml
@@ -27,7 +27,7 @@ num_epochs: 4
 learning_rate: 0.00001
 train_on_inputs: false
 group_by_length: false
-bf16: auto
+bf16: true
 tf32: true
 early_stopping_patience:
 resume_from_checkpoint:
--- a/examples/qwen/lora.yml
+++ b/examples/qwen/lora.yml
@@ -2,6 +2,7 @@ base_model: Qwen/Qwen-7B
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer

+is_qwen_derived_model: true
 trust_remote_code: true

 load_in_8bit: true
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: false
@@ -57,7 +58,7 @@ flash_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:
--- a/examples/qwen/qlora.yml
+++ b/examples/qwen/qlora.yml
@@ -2,6 +2,7 @@ base_model: Qwen/Qwen-7B
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer

+is_qwen_derived_model: true
 trust_remote_code: true

 load_in_8bit: false
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: false
@@ -57,7 +58,7 @@ flash_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:
--- a/examples/redpajama/config-3b.yml
+++ b/examples/redpajama/config-3b.yml
@@ -34,7 +34,7 @@ lr_scheduler: cosine
 learning_rate: 0.0000002
 train_on_inputs: false
 group_by_length: false
-bf16: auto
+bf16: true
 tf32: true
 early_stopping_patience:
 resume_from_checkpoint:
--- a/examples/replit-3b/config-lora.yml
+++ b/examples/replit-3b/config-lora.yml
@@ -33,7 +33,7 @@ lr_scheduler:
 learning_rate: 0.00001
 train_on_inputs: false
 group_by_length: false
-bf16: auto
+bf16: true
 tf32: true
 gradient_checkpointing:
 early_stopping_patience:
--- a/examples/stablelm-2/1.6b/fft.yml
+++ b/examples/stablelm-2/1.6b/fft.yml
@@ -1,69 +0,0 @@
-base_model: stabilityai/stablelm-2-1_6b
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
-output_dir: ./out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-adapter:
-lora_model_dir:
-lora_r:
-lora_alpha:
-lora_dropout:
-lora_target_linear:
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-flash_attn_cross_entropy: false
-flash_attn_rms_norm: true
-flash_attn_fuse_qkv: false
-flash_attn_fuse_mlp: true
-
-warmup_steps: 100
-evals_per_epoch: 4
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
-weight_decay: 0.1
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/stablelm-2/1.6b/lora.yml
+++ b/examples/stablelm-2/1.6b/lora.yml
@@ -1,66 +0,0 @@
-base_model: stabilityai/stablelm-2-1_6b
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-trust_remote_code: true
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-flash_attn_cross_entropy: false
-flash_attn_rms_norm: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/stablelm-2/README.md
+++ b/examples/stablelm-2/README.md
@@ -1,36 +0,0 @@
-# StableLM 2
-
-This repository contains examples for training and processing using StableLM-2. It also includes a section to help you estimate the GPU requirements for your specific use case.
-
-## Estimating GPU Requirements
-
-| type          | deepspeed | batch size | context length | vRAM GPU (GBs) |
-|---------------|-----------|------------|----------------|----------------|
-| full finetune | N/A       | 1          | 4096           | ~21.5GBs       |
-| full finetune | zero2     | 1          | 4096           | ~20GBs         |
-| lora          | N/A       | 1          | 4096           | ~16.6GBs       |
-
-The above are estimates and might differ slight depending on the setup for example whether you pack your sequence lengths or not (the above assumes you do to length 4096).
-
-This blog post from Hamel Husain was a great resource for estimating these numbers: https://hamel.dev/notes/llm/03_estimating_vram.html
-
-## Training
-We have example scripts here for both full finetuning and lora using the popular alpaca dataset:
-
-```shell
-# preprocess the dataset
-CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/stablelm-2/1.6b/lora.yml
-```
-
-Single GPU Training:
-```shell
-python -m axolotl.cli.train examples/stablelm-2/fft.yml --deepspeed deepspeed_configs/zero2.json
-# OR
-python -m axolotl.cli.train examples/stablelm-2/1.6b/lora.yml
-```
-
-Multinode GPU Training with `accelerate`:
-```shell
-# make sure you've configured accelerate properly
-accelerate launch -m axolotl.cli.train examples/stablelm-2/1.6b/fft.yml --deepspeed deepspeed_configs/zero2.json
-```
--- a/examples/starcoder2/qlora.yml
+++ b/examples/starcoder2/qlora.yml
@@ -1,69 +0,0 @@
-base_model: bigcode/starcoder2-3b
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-
-
-dataset_prepared_path:
-val_set_size: 0.2
-output_dir: ./qlora
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 8192
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 2
-num_epochs: 3
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 20
-evals_per_epoch: 4
-eval_steps:
-eval_table_size:
-saves_per_epoch: 4
-save_steps:
-save_total_limit: 2
-debug:
-deepspeed:
-weight_decay:
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/tiny-llama/lora-mps.yml
+++ b/examples/tiny-llama/lora-mps.yml
@@ -1,64 +0,0 @@
-base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./lora-out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-eval_sample_packing: false
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: true
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: false
-
-warmup_steps: 10
-evals_per_epoch: 0
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/tiny-llama/lora.yml
+++ b/examples/tiny-llama/lora.yml
@@ -1,6 +1,7 @@
 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: true
 load_in_4bit: false
@@ -15,7 +16,6 @@ output_dir: ./lora-out

 sequence_len: 4096
 sample_packing: true
-eval_sample_packing: false
 pad_to_sequence_len: true

 adapter: lora
@@ -41,8 +41,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/tiny-llama/pretrain.yml
+++ b/examples/tiny-llama/pretrain.yml
@@ -2,6 +2,7 @@ base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: false
@@ -11,7 +12,6 @@ max_steps: 200
 pretraining_dataset:
  path: c4
  name: en
-  type: pretrain
 dataset_prepared_path:
 val_set_size: 0.0
 output_dir: ./model-out
@@ -34,8 +34,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/tiny-llama/qlora.yml
+++ b/examples/tiny-llama/qlora.yml
@@ -1,6 +1,7 @@
 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true

 load_in_8bit: false
 load_in_4bit: true
@@ -42,8 +43,8 @@ learning_rate: 0.0002

 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false

 gradient_checkpointing: true
--- a/examples/xgen-7b/xgen-7b-8k-qlora.yml
+++ b/examples/xgen-7b/xgen-7b-8k-qlora.yml
@@ -62,8 +62,8 @@ lr_scheduler: cosine
 learning_rate: 0.00002
 train_on_inputs: false
 group_by_length: false
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false
 gradient_checkpointing: true
 # stop training after this many evaluation losses have increased in a row
--- a/examples/yi-34B-chat/qlora.yml
+++ b/examples/yi-34B-chat/qlora.yml
@@ -1,13 +1,14 @@
 base_model: 01-ai/Yi-34B-Chat
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-
+is_mistral_derived_model: false
+is_llama_derived_model: true
 load_in_8bit: false
 load_in_4bit: true
 strict: false
 sequence_len: 1024
-bf16: auto
-fp16:
+bf16: true
+fp16: false
 tf32: false
 flash_attention: true
 special_tokens:
@@ -28,7 +29,7 @@ num_epochs: 1
 val_set_size: 0.1
 evals_per_epoch: 5
 eval_table_size:
-eval_max_new_tokens: 128
+eval_table_max_new_tokens: 128
 eval_sample_packing: false
 eval_batch_size: 1

--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,4 +1,3 @@
 pre-commit
 black
 mypy
-types-requests
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,34 +1,35 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.9.0
-transformers==4.38.2
+peft==0.7.0
+transformers @ git+https://github.com/huggingface/transformers.git@3cefac1d974db5e2825a0cb2b842883a628be7a0
 tokenizers==0.15.0
 bitsandbytes>=0.41.1
-accelerate==0.26.1
-deepspeed==0.13.1
-pydantic==2.6.3
+accelerate @ git+https://github.com/huggingface/accelerate.git@0d2280dadc6a93413a5496613b7fdda3a4d2551b
+deepspeed
 addict
 fire
 PyYAML>=6.0
-requests
 datasets>=2.15.0
-flash-attn==2.5.5
+flash-attn==2.3.3
 sentencepiece
 wandb
 einops
 xformers==0.0.22
-optimum==1.16.2
+optimum==1.13.2
 hf_transfer
 colorama
 numba
 numpy>=1.24.4
+mlflow
 # qlora things
-evaluate==0.4.1
+bert-score==0.3.13
+evaluate==0.4.0
+rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat==0.2.36
+fschat==0.2.34
 gradio==3.50.2
 tensorboard

--- a/scripts/cloud-entrypoint.sh
+++ b/scripts/cloud-entrypoint.sh
@@ -1,40 +0,0 @@
-#!/bin/bash
-
-# Export specific ENV variables to /etc/rp_environment
-echo "Exporting environment variables..."
-printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
-echo 'source /etc/rp_environment' >> ~/.bashrc
-
-if [[ $PUBLIC_KEY ]]; then
-    # runpod
-    mkdir -p ~/.ssh
-    chmod 700 ~/.ssh
-    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
-    chmod 700 -R ~/.ssh
-    # Start the SSH service in the background
-    service ssh start
-elif [ -n "$SSH_KEY" ]; then
-    # latitude.sh
-    mkdir -p ~/.ssh
-    chmod 700 ~/.ssh
-    echo $SSH_KEY >> ~/.ssh/authorized_keys
-    chmod 700 -R ~/.ssh
-    # Start the SSH service in the background
-    service ssh start
-else
-    echo "No PUBLIC_KEY or SSH_KEY environment variable provided, not starting openSSH daemon"
-fi
-
-# Check if JUPYTER_PASSWORD is set and not empty
-if [ -n "$JUPYTER_PASSWORD" ]; then
-    # Set JUPYTER_TOKEN to the value of JUPYTER_PASSWORD
-    export JUPYTER_TOKEN="$JUPYTER_PASSWORD"
-fi
-
-if [ "$JUPYTER_DISABLE" != "1" ]; then
-    # Run Jupyter Lab in the background
-    jupyter lab --port=8888 --ip=* --allow-root --ServerApp.allow_origin=* --ServerApp.preferred_dir=/workspace &
-fi
-
-# Execute the passed arguments (CMD)
-exec "$@"
--- a/scripts/motd
+++ b/scripts/motd
@@ -1,17 +0,0 @@
-
-                                 dP            dP   dP
-                                 88            88   88
-      .d8888b. dP.  .dP .d8888b. 88 .d8888b. d8888P 88
-      88'  `88  `8bd8'  88'  `88 88 88'  `88   88   88
-      88.  .88  .d88b.  88.  .88 88 88.  .88   88   88
-      `88888P8 dP'  `dP `88888P' dP `88888P'   dP   dP
-
-Welcome to the axolotl cloud image! If the you've mounted a disk to /workspace and the axolotl directory ie empty, run the following commands:
-
-```
-cd /workspace
-rm -rf /workspace/axolotl
-git clone https://github.com/OpenAccess-AI-Collective/axolotl.git
-cd axolotl
-pip install --no-deps -e .
-```
--- a/scripts/runpod-entrypoint.sh
+++ b/scripts/runpod-entrypoint.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Export specific ENV variables to /etc/rp_environment
+echo "Exporting environment variables..."
+printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
+echo 'source /etc/rp_environment' >> ~/.bashrc
+
+if [[ $PUBLIC_KEY ]]
+then
+    mkdir -p ~/.ssh
+    chmod 700 ~/.ssh
+    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
+    chmod 700 -R ~/.ssh
+    # Start the SSH service in the background
+    service ssh start
+else
+    echo "No PUBLIC_KEY ENV variable provided, not starting openSSH daemon"
+fi
+
+# Execute the passed arguments (CMD)
+exec "$@"
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,5 @@
 """setup.py for axolotl"""

-import platform
-import re
 from importlib.metadata import PackageNotFoundError, version

 from setuptools import find_packages, setup
@@ -18,7 +16,6 @@ def parse_requirements():
                or "flash-attention" in line
                or "deepspeed" in line
                or "mamba-ssm" in line
-                or "lion-pytorch" in line
            )
            if line.startswith("--extra-index-url"):
                # Handle custom index URLs
@@ -29,25 +26,10 @@ def parse_requirements():
                _install_requires.append(line)

    try:
-        if "Darwin" in platform.system():
+        torch_version = version("torch")
+        if torch_version.startswith("2.1.1"):
            _install_requires.pop(_install_requires.index("xformers==0.0.22"))
-        else:
-            torch_version = version("torch")
-            _install_requires.append(f"torch=={torch_version}")
-
-            version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
-            if version_match:
-                major, minor, patch = version_match.groups()
-                major, minor = int(major), int(minor)
-                patch = (
-                    int(patch) if patch is not None else 0
-                )  # Default patch to 0 if not present
-            else:
-                raise ValueError("Invalid version format")
-
-            if (major, minor) >= (2, 1):
-                _install_requires.pop(_install_requires.index("xformers==0.0.22"))
-                _install_requires.append("xformers>=0.0.23")
+            _install_requires.append("xformers==0.0.23")
    except PackageNotFoundError:
        pass

@@ -59,7 +41,7 @@ install_requires, dependency_links = parse_requirements()

 setup(
    name="axolotl",
-    version="0.4.0",
+    version="0.3.0",
    description="LLM Trainer",
    long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
    package_dir={"": "src"},
@@ -68,14 +50,13 @@ setup(
    dependency_links=dependency_links,
    extras_require={
        "flash-attn": [
-            "flash-attn==2.5.5",
+            "flash-attn==2.3.3",
        ],
        "fused-dense-lib": [
            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.3.3#subdirectory=csrc/fused_dense_lib",
        ],
        "deepspeed": [
-            "deepspeed==0.13.1",
-            "deepspeed-kernels",
+            "deepspeed",
        ],
        "mamba-ssm": [
            "mamba-ssm==1.0.1",
@@ -83,11 +64,5 @@ setup(
        "auto-gptq": [
            "auto-gptq==0.5.1",
        ],
-        "mlflow": [
-            "mlflow",
-        ],
-        "lion-pytorch": [
-            "lion-pytorch==0.1.2",
-        ],
    },
 )
--- a/src/axolotl/cli/init.py
+++ b/src/axolotl/cli/init.py
@@ -1,29 +1,26 @@
 """Prepare and train a model on a dataset. Can also infer from a model or merge lora"""

 import importlib
-import json
 import logging
 import math
 import os
 import random
 import sys
-import tempfile
 from pathlib import Path
 from threading import Thread
 from typing import Any, Dict, List, Optional, Union
-from urllib.parse import urlparse

-import requests
+import gradio as gr
 import torch
 import yaml

 # add src to the pythonpath so we don't need to pip install this
 from accelerate.commands.config import config_args
 from art import text2art
+from datasets import concatenate_datasets, load_dataset
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
-from transformers.utils import is_torch_bf16_gpu_available

 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
@@ -33,7 +30,7 @@ from axolotl.utils.config import (
    normalize_config,
    validate_config,
 )
-from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
+from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
@@ -63,52 +60,6 @@ def print_axolotl_text_art(suffix=None):
        print(ascii_art)


-def check_remote_config(config: Union[str, Path]):
-    # Check if the config is a valid HTTPS URL to a .yml or .yaml file
-    if not (isinstance(config, str) and config.startswith("https://")):
-        return config  # Return the original value if it's not a valid URL
-
-    filename = os.path.basename(urlparse(config).path)
-    temp_dir = tempfile.mkdtemp()
-
-    try:
-        response = requests.get(config, timeout=30)
-        response.raise_for_status()  # Check for HTTP errors
-
-        content = response.content
-        try:
-            # Try parsing as JSON first to catch cases where JSON content is mistakenly considered YAML
-            json.loads(content)
-            # Log a warning but do not raise an error; JSON is technically valid YAML - this can happen when you forget to point to a raw github link
-            LOG.warning(
-                f"Warning: The content of the file at {config} is JSON, which is technically valid YAML but might not be intended."
-            )
-        except json.JSONDecodeError:
-            # If it's not valid JSON, verify it's valid YAML
-            try:
-                yaml.safe_load(content)
-            except yaml.YAMLError as err:
-                raise ValueError(
-                    f"Failed to parse the content at {config} as YAML: {err}"
-                ) from err
-
-        # Write the content to a file if it's valid YAML (or JSON treated as YAML)
-        output_path = Path(temp_dir) / filename
-        with open(output_path, "wb") as file:
-            file.write(content)
-        LOG.info(
-            f"Using the following config obtained from {config}:\n\n{content.decode('utf-8')}\n"
-        )
-        return output_path
-
-    except requests.RequestException as err:
-        # This catches all requests-related exceptions including HTTPError
-        raise RuntimeError(f"Failed to download {config}: {err}") from err
-    except Exception as err:
-        # Catch-all for any other exceptions
-        raise err
-
-
 def get_multi_line_input() -> Optional[str]:
    print("Give me an instruction (Ctrl + D to submit): ")
    instruction = ""
@@ -128,11 +79,7 @@ def do_merge_lora(

    LOG.info("running merge of LoRA with base model")
    model = model.merge_and_unload(progressbar=True)
-    try:
-        model.to(dtype=cfg.torch_dtype)
-    except RuntimeError:
-        pass
-    model.generation_config.do_sample = True
+    model.to(dtype=cfg.torch_dtype)

    if cfg.local_rank == 0:
        LOG.info(f"saving merged model to: {str(Path(cfg.output_dir) / 'merged')}")
@@ -214,8 +161,6 @@ def do_inference_gradio(
    cfg: DictDefault,
    cli_args: TrainerCliArgs,
 ):
-    import gradio as gr
-
    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
    prompter = cli_args.prompter
    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
@@ -322,14 +267,14 @@ def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> b
    return not any(el in list2 for el in list1)


-def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
-    config = check_remote_config(config)
+def load_cfg(config: Path = Path("examples/"), **kwargs):
    if Path(config).is_dir():
-        config = choose_config(Path(config))
+        config = choose_config(config)

    # load the config from the yaml file
    with open(config, encoding="utf-8") as file:
        cfg: DictDefault = DictDefault(yaml.safe_load(file))
+    cfg.axolotl_config_path = config
    # if there are any options passed in the cli, if it is something that seems valid from the yaml,
    # then overwrite the value
    cfg_keys = cfg.keys()
@@ -342,22 +287,7 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
            else:
                cfg[k] = kwargs[k]

-    cfg.axolotl_config_path = config
-
-    try:
-        device_props = torch.cuda.get_device_properties("cuda")
-        gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
-    except:  # pylint: disable=bare-except # noqa: E722
-        gpu_version = None
-
-    cfg = validate_config(
-        cfg,
-        capabilities={
-            "bf16": is_torch_bf16_gpu_available(),
-            "n_gpu": os.environ.get("WORLD_SIZE", 1),
-            "compute_capability": gpu_version,
-        },
-    )
+    validate_config(cfg)

    prepare_optim_env(cfg)

@@ -413,7 +343,78 @@ def load_rl_datasets(
    cfg: DictDefault,
    cli_args: TrainerCliArgs,  # pylint: disable=unused-argument
 ) -> TrainDatasetMeta:
-    train_dataset, eval_dataset = load_prepare_dpo_datasets(cfg)
+    train_datasets: List[Any] = []
+    for i, ds_cfg in enumerate(cfg.datasets):
+        train_datasets.insert(i, load_dataset(ds_cfg["path"], split=ds_cfg["split"]))
+    # eval_dataset = load_dataset(
+    #     cfg.test_datasets[0]["path"], split=cfg.test_datasets[0]["split"]
+    # )
+    eval_dataset = None
+
+    def argilla_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
+        return sample
+
+    def intel_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
+        return sample
+
+    def apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
+        return sample
+
+    def ultra_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
+        return sample
+
+    for i, data_set in enumerate(train_datasets):
+        _type = cfg.datasets[i]["type"]
+        ds_type_fn = locals()[_type]
+        train_datasets[i] = data_set.map(ds_type_fn)
+    train_dataset = concatenate_datasets(train_datasets)
+
+    # eval_dataset = eval_dataset.map(intel_apply_chatml)
+
    total_num_steps = int(
        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
    )
@@ -433,13 +434,6 @@ def check_accelerate_default_config():


 def check_user_token():
-    # Skip check if HF_HUB_OFFLINE is set to True
-    if os.getenv("HF_HUB_OFFLINE") == "1":
-        LOG.info(
-            "Skipping HuggingFace token verification because HF_HUB_OFFLINE is set to True. Only local files will be used."
-        )
-        return True
-
    # Verify if token is valid
    api = HfApi()
    try:
--- a/src/axolotl/cli/preprocess.py
+++ b/src/axolotl/cli/preprocess.py
@@ -3,7 +3,6 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Union

 import fire
 import transformers
@@ -14,21 +13,18 @@ from axolotl.cli import (
    check_user_token,
    load_cfg,
    load_datasets,
-    load_rl_datasets,
    print_axolotl_text_art,
 )
 from axolotl.common.cli import PreprocessCliArgs
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
-from axolotl.prompt_strategies.sharegpt import register_chatml_template

 LOG = logging.getLogger("axolotl.cli.preprocess")


-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
    # pylint: disable=duplicate-code
    print_axolotl_text_art()
    parsed_cfg = load_cfg(config, **kwargs)
-    parsed_cfg.is_preprocess = True
    check_accelerate_default_config()
    check_user_token()
    parser = transformers.HfArgumentParser((PreprocessCliArgs))
@@ -36,14 +32,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
        return_remaining_strings=True
    )

-    if parsed_cfg.chat_template == "chatml" and parsed_cfg.default_system_message:
-        LOG.info(
-            f"ChatML set. Adding default system message: {parsed_cfg.default_system_message}"
-        )
-        register_chatml_template(parsed_cfg.default_system_message)
-    else:
-        register_chatml_template()
-
    if not parsed_cfg.dataset_prepared_path:
        msg = (
            Fore.RED
@@ -54,11 +42,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
        LOG.warning(msg)
        parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH

-    if parsed_cfg.rl:
-        load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    else:
-        load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-
+    _ = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
    LOG.info(
        Fore.GREEN
        + f"Success! Preprocessed data path: `dataset_prepared_path: {parsed_cfg.dataset_prepared_path}`"
--- a/src/axolotl/cli/shard.py
+++ b/src/axolotl/cli/shard.py
@@ -3,7 +3,6 @@ CLI to shard a trained model into 10GiB chunks
 """
 import logging
 from pathlib import Path
-from typing import Union

 import fire
 import transformers
@@ -26,7 +25,7 @@ def shard(
    model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)


-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
    # pylint: disable=duplicate-code
    print_axolotl_text_art()
    parsed_cfg = load_cfg(config, **kwargs)
--- a/src/axolotl/cli/train.py
+++ b/src/axolotl/cli/train.py
@@ -3,12 +3,9 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Tuple, Union

 import fire
-from transformers.hf_argparser import HfArgumentParser
-from transformers.modeling_utils import PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizer
+import transformers

 from axolotl.cli import (
    check_accelerate_default_config,
@@ -19,40 +16,27 @@ from axolotl.cli import (
    print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.prompt_strategies.sharegpt import register_chatml_template
 from axolotl.train import train

 LOG = logging.getLogger("axolotl.cli.train")


-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
    # pylint: disable=duplicate-code
    parsed_cfg = load_cfg(config, **kwargs)
-    parser = HfArgumentParser((TrainerCliArgs))
-    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
-        return_remaining_strings=True
-    )
-    return do_train(parsed_cfg, parsed_cli_args)
-
-
-def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
    print_axolotl_text_art()
    check_accelerate_default_config()
    check_user_token()
-    if cfg.chat_template == "chatml" and cfg.default_system_message:
-        LOG.info(
-            f"ChatML set. Adding default system message: {cfg.default_system_message}"
-        )
-        register_chatml_template(cfg.default_system_message)
-    else:
-        register_chatml_template()
+    parser = transformers.HfArgumentParser((TrainerCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )

-    if cfg.rl:
-        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
+    if parsed_cfg.rl:
+        dataset_meta = load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
    else:
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)


 if __name__ == "__main__":
--- a/src/axolotl/common/cli.py
+++ b/src/axolotl/common/cli.py
@@ -6,7 +6,6 @@ import logging
 from dataclasses import dataclass, field
 from typing import Optional

-import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -5,7 +5,6 @@ Builder for the training args and trainer

 import abc
 import importlib
-import importlib.util
 import logging
 import math
 import sys
@@ -13,25 +12,17 @@ from abc import abstractmethod
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import List, Optional, Type, Union
+from typing import Optional

 import torch
 import transformers
 from datasets import Dataset
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
-from transformers import (
-    EarlyStoppingCallback,
-    Trainer,
-    TrainerCallback,
-    TrainingArguments,
-)
+from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_utils import seed_worker
-from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer

-from axolotl.loraplus import create_loraplus_optimizer
-from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
    EvalFirstStepCallback,
@@ -40,25 +31,19 @@ from axolotl.utils.callbacks import (
    SaveAxolotlConfigtoWandBCallback,
    SaveBetterTransformerModelCallback,
    bench_eval_callback_factory,
-    causal_lm_bench_eval_callback_factory,
    log_prediction_callback_factory,
 )
 from axolotl.utils.collators import (
    BatchSamplerDataCollatorForSeq2Seq,
    DataCollatorForSeq2Seq,
    MambaDataCollator,
-    V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
    get_cosine_schedule_with_min_lr,
    get_cosine_schedule_with_quadratic_warmup,
-    get_cosine_schedule_with_warmup_decay_constant,
 )

-if is_sagemaker_mp_enabled():
-    import smdistributed.modelparallel.torch as smp
-
 try:
    import torch._dynamo  # pylint: disable=ungrouped-imports
 except ImportError:
@@ -67,26 +52,6 @@ except ImportError:
 LOG = logging.getLogger("axolotl.core.trainer_builder")


-def is_mlflow_available():
-    return importlib.util.find_spec("mlflow") is not None
-
-
-def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
-    if isinstance(tag_names, str):
-        tag_names = [tag_names]
-
-    if kwargs is not None:
-        if "tags" not in kwargs:
-            kwargs["tags"] = tag_names
-        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
-            kwargs["tags"].extend(tag_names)
-        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
-            tag_names.append(kwargs["tags"])
-            kwargs["tags"] = tag_names
-
-    return kwargs
-
-
@dataclass
 class AxolotlTrainingArguments(TrainingArguments):
    """
@@ -110,10 +75,6 @@ class AxolotlTrainingArguments(TrainingArguments):
        default=False,
        metadata={"help": "Use sample packing for efficient training."},
    )
-    multipack_real_batches: bool = field(
-        default=False,
-        metadata={"help": "Use real batches for efficient training."},
-    )
    eval_sample_packing: Optional[bool] = field(
        default=None,
        metadata={"help": "Use sample packing for efficient evals."},
@@ -138,14 +99,6 @@ class AxolotlTrainingArguments(TrainingArguments):
        default=None,
        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
    )
-    relora_anneal_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_prune_ratio: Optional[float] = field(
-        default=0.9,
-        metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
-    )
    bench_split: Optional[str] = field(
        default="eval", metadata={"help": "The benchmark split to run on"}
    )
@@ -158,9 +111,6 @@ class AxolotlTrainingArguments(TrainingArguments):
    do_bench_eval: Optional[bool] = field(
        default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
    )
-    do_causal_lm_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
-    )
    max_bench_samples: Optional[int] = field(
        default=None,
        metadata={
@@ -178,19 +128,6 @@ class AxolotlTrainingArguments(TrainingArguments):
        default=None,
        metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
    )
-    cosine_constant_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
-        },
-    )
-    loraplus_lr_ratio: Optional[float] = field(
-        default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
-    )
-    loraplus_lr_embedding: Optional[float] = field(
-        default=1e-6,
-        metadata={"help": "loraplus learning rate for lora embedding layers."},
-    )


 class AxolotlTrainer(Trainer):
@@ -215,33 +152,6 @@ class AxolotlTrainer(Trainer):
        super().__init__(*_args, **kwargs)
        self.train_data_collator = self.data_collator

-    def create_optimizer(self):
-        if self.args.loraplus_lr_ratio is None:
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
-                self.args,
-            )
-
-            loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-            loraplus_lr_embedding = getattr(self.args, "loraplus_lr_embedding", None)
-            self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                opt_model,
-                optimizer_cls,
-                optimizer_kwargs,
-                loraplus_lr_ratio,
-                loraplus_lr_embedding,
-            )
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
-
    def create_scheduler(
        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
    ):
@@ -253,40 +163,24 @@ class AxolotlTrainer(Trainer):
            num_training_steps (int): The number of training steps to do.
            optimizer (torch.optim.Optimizer): The training optimizer
        """
-        use_cosine_quadratic = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.lr_quadratic_warmup is True
-        )
-
-        use_cosine_min_lr = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.cosine_min_lr_ratio is not None
-        )

        # fmt: off
        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
            # fmt: on
-            if use_cosine_quadratic:
-                if use_cosine_min_lr:
-                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
-
+            if (
+                self.args.lr_scheduler_type == "cosine"
+                and self.args.lr_quadratic_warmup is True
+            ):
                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
                    optimizer,
                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                    num_training_steps=num_training_steps,
                )
-            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
-                )
-            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
+            elif self.args.lr_scheduler_type == "cosine" and self.args.cosine_min_lr_ratio is not None:
                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                if self.args.deepspeed:
+                    LOG.warning("Using cosine scheduler with deepspeed. This may be ignored if a scheduler is set \
+                                in the deepspeed JSON")
                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
                    optimizer,
                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
@@ -295,30 +189,15 @@ class AxolotlTrainer(Trainer):
                )
            else:
                return super().create_scheduler(num_training_steps, optimizer)
-        else:
-            if use_cosine_quadratic:
-                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
-
-            if use_cosine_min_lr:
-                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
-
        return self.lr_scheduler

    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
        if self.args.sample_packing and not self.args.pretraining:
-            if self.args.multipack_real_batches:
-                batch_size = self.args.per_device_train_batch_size
-                batch_max_len = self.args.max_seq_length
-            else:
-                batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_train_batch_size * self.args.max_seq_length
-                )
            return MultipackBatchSampler(
                RandomSampler(self.train_dataset),
-                batch_size=batch_size,
+                self.args.train_batch_size,
                drop_last=True,
-                batch_max_len=batch_max_len,
+                batch_max_len=self._train_batch_size * self.args.max_seq_length,
                lengths=get_dataset_lengths(self.train_dataset),
                packing_efficiency_estimate=self.args.sample_packing_efficiency,
            )
@@ -328,19 +207,11 @@ class AxolotlTrainer(Trainer):
        self, eval_dataset: Dataset
    ) -> Optional[torch.utils.data.Sampler]:
        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            if self.args.multipack_real_batches:
-                batch_size = self.args.per_device_eval_batch_size
-                batch_max_len = self.args.max_seq_length
-            else:
-                batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_eval_batch_size * self.args.max_seq_length
-                )
            return MultipackBatchSampler(
                SequentialSampler(eval_dataset),
-                batch_size=batch_size,
+                self.args.per_device_eval_batch_size,
                drop_last=True,
-                batch_max_len=batch_max_len,
+                batch_max_len=self.args.eval_batch_size * self.args.max_seq_length,
                lengths=get_dataset_lengths(eval_dataset),
                packing_efficiency_estimate=self.args.sample_packing_efficiency,
            )
@@ -349,8 +220,7 @@ class AxolotlTrainer(Trainer):
    def get_train_dataloader(self) -> DataLoader:
        if self.args.sample_packing and not self.args.pretraining:
            train_dataset = self.train_dataset
-            if "length" in train_dataset.features.keys():
-                train_dataset = train_dataset.remove_columns(["length"])
+            train_dataset = train_dataset.remove_columns(["length"])
            data_collator = self.data_collator
            dataloader_params = {
                "batch_size": self._train_batch_size,
@@ -458,13 +328,30 @@ class AxolotlTrainer(Trainer):
        #     return (loss, outputs) if return_outputs else loss
        return super().compute_loss(model, inputs, return_outputs=return_outputs)

+    def _sanitize_kwargs_for_tagging(self, tag_names, kwargs=None):
+        if isinstance(tag_names, str):
+            tag_names = [tag_names]
+
+        if kwargs is not None:
+            if "tags" not in kwargs:
+                kwargs["tags"] = tag_names
+            elif "tags" in kwargs and isinstance(kwargs["tags"], list):
+                kwargs["tags"].extend(tag_names)
+            elif "tags" in kwargs and isinstance(kwargs["tags"], str):
+                tag_names.append(kwargs["tags"])
+                kwargs["tags"] = tag_names
+
+        return kwargs
+
    @wraps(Trainer.push_to_hub)
    def push_to_hub(self, *args, **kwargs) -> str:
        """
        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
        """
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = self._sanitize_kwargs_for_tagging(
+            tag_names=self.tag_names, kwargs=kwargs
+        )

        return super().push_to_hub(*args, **kwargs)

@@ -551,14 +438,10 @@ class ReLoRATrainer(AxolotlTrainer):
            warmup_steps = (
                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
            )
-            anneal_steps = (
-                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
-            )
            self.lr_scheduler = ReLoRAScheduler(
                optimizer,
                lr_scheduler,
                self.args.relora_steps,
-                anneal_steps,
                warmup_steps,
            )
        else:
@@ -567,24 +450,6 @@ class ReLoRATrainer(AxolotlTrainer):
        return self.lr_scheduler


-class AxolotlDPOTrainer(DPOTrainer):
-    """
-    Extend the base DPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "dpo"]
-
-    @wraps(DPOTrainer.push_to_hub)
-    def push_to_hub(self, *args, **kwargs) -> str:
-        """
-        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
-        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
-        """
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
-
-        return super().push_to_hub(*args, **kwargs)
-
-
 class TrainerBuilderBase(abc.ABC):
    """
    Base class for trainer builder
@@ -593,7 +458,6 @@ class TrainerBuilderBase(abc.ABC):
    _train_dataset = None
    _eval_dataset = None
    _model_ref = None
-    _peft_config = None

    def __init__(self, cfg, model, tokenizer):
        self.cfg = cfg
@@ -624,26 +488,13 @@ class TrainerBuilderBase(abc.ABC):
    def eval_dataset(self, dataset):
        self._eval_dataset = dataset

-    @property
-    def peft_config(self):
-        return self._peft_config
-
-    @peft_config.setter
-    def peft_config(self, peft_config):
-        self._peft_config = peft_config
-
    @abstractmethod
    def build(self, total_num_steps):
        pass

-    def get_callbacks(self) -> List[TrainerCallback]:
-        callbacks = []
-        if self.cfg.use_wandb:
-            callbacks.append(
-                SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
-            )
-
-        return callbacks
+    @abstractmethod
+    def get_callbacks(self):
+        pass

    @abstractmethod
    def get_post_trainer_create_callbacks(self, trainer):
@@ -651,6 +502,12 @@ class TrainerBuilderBase(abc.ABC):
        Callbacks added after the trainer is created, usually b/c these need access to the trainer
        """

+
+class HFCausalTrainerBuilder(TrainerBuilderBase):
+    """
+    Build the HuggingFace training args/trainer for Causal models
+    """
+
    def hook_pre_create_training_args(self, training_arguments_kwargs):
        # TODO
        return training_arguments_kwargs
@@ -667,16 +524,10 @@ class TrainerBuilderBase(abc.ABC):
        # TODO
        return trainer

-
-class HFCausalTrainerBuilder(TrainerBuilderBase):
-    """
-    Build the HuggingFace training args/trainer for Causal models
-    """
-
    def get_callbacks(self):
-        callbacks = super().get_callbacks()
+        callbacks = []
        callbacks.append(GPUStatsCallback(self.cfg))
-        callbacks.append(EvalFirstStepCallback())
+        callbacks.append(EvalFirstStepCallback)

        if self.cfg.relora_steps:
            callbacks.append(ReLoRACallback(self.cfg))
@@ -685,20 +536,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
            hasattr(self.model, "use_bettertransformer")
            and self.model.use_bettertransformer is True
        ):
-            callbacks.append(SaveBetterTransformerModelCallback())
+            callbacks.append(SaveBetterTransformerModelCallback)

        if self.cfg.use_wandb:
            callbacks.append(
                SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
            )
-        if self.cfg.use_mlflow and is_mlflow_available():
-            from axolotl.utils.callbacks.mlflow_ import (
-                SaveAxolotlConfigtoMlflowCallback,
-            )
-
-            callbacks.append(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
-            )

        if self.cfg.loss_watchdog_threshold is not None:
            callbacks.append(LossWatchDogCallback(self.cfg))
@@ -715,11 +558,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):

        if self.cfg.do_bench_eval:
            callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
-        if self.cfg.do_causal_lm_eval:
-            CausalLMBenchEvalCallback = causal_lm_bench_eval_callback_factory(
-                trainer, self.tokenizer
-            )
-            callbacks.append(CausalLMBenchEvalCallback(self.cfg))

        if self.cfg.early_stopping_patience:
            early_stop_cb = EarlyStoppingCallback(
@@ -774,7 +612,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
            training_arguments_kwargs[
                "gradient_checkpointing"
            ] = self.cfg.gradient_checkpointing
-            if self.cfg.gradient_checkpointing_kwargs is not None:
+            if self.cfg.gradient_checkpointing_kwargs:
                training_arguments_kwargs[
                    "gradient_checkpointing_kwargs"
                ] = self.cfg.gradient_checkpointing_kwargs
@@ -841,7 +679,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
        elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
            training_arguments_kwargs["dataloader_drop_last"] = True

-        if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
+        if self.cfg.val_set_size == 0:
            # no eval set, so don't eval
            training_arguments_kwargs["evaluation_strategy"] = "no"
        elif self.cfg.eval_steps:
@@ -868,8 +706,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
            training_arguments_kwargs["do_bench_eval"] = self.cfg.do_bench_eval
            if self.cfg.bench_dataset:
                training_arguments_kwargs["bench_dataset"] = self.cfg.bench_dataset
-        if self.cfg.do_causal_lm_eval:
-            training_arguments_kwargs["do_causal_lm_eval"] = self.cfg.do_causal_lm_eval
        if self.cfg.metric_for_best_model:
            training_arguments_kwargs[
                "metric_for_best_model"
@@ -909,10 +745,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
        training_arguments_kwargs[
            "per_device_train_batch_size"
        ] = self.cfg.micro_batch_size
-        if self.cfg.eval_batch_size:
-            training_arguments_kwargs[
-                "per_device_eval_batch_size"
-            ] = self.cfg.eval_batch_size
+        training_arguments_kwargs[
+            "per_device_eval_batch_size"
+        ] = self.cfg.eval_batch_size
        training_arguments_kwargs[
            "gradient_accumulation_steps"
        ] = self.cfg.gradient_accumulation_steps
@@ -930,10 +765,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                self.cfg.load_best_model_at_end is not False
                or self.cfg.early_stopping_patience
            )
-            and (
-                (not self.cfg.test_datasets and self.cfg.val_set_size > 0)
-                or (self.cfg.test_datasets and self.cfg.val_set_size == 0)
-            )
+            and self.cfg.val_set_size > 0
            and self.cfg.save_steps
            and self.cfg.eval_steps
            and self.cfg.save_steps % self.cfg.eval_steps == 0
@@ -954,10 +786,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
        training_arguments_kwargs["optim"] = (
            self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
        )
-        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
-        training_arguments_kwargs[
-            "loraplus_lr_embedding"
-        ] = self.cfg.loraplus_lr_embedding
        training_arguments_kwargs["lr_scheduler_type"] = (
            self.cfg.lr_scheduler
            if self.cfg.lr_scheduler
@@ -968,18 +796,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
            self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
        )
        training_arguments_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
-        training_arguments_kwargs[
-            "cosine_constant_lr_ratio"
-        ] = self.cfg.cosine_constant_lr_ratio
        training_arguments_kwargs["weight_decay"] = (
            self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
        )
        training_arguments_kwargs["sample_packing"] = (
            self.cfg.sample_packing if self.cfg.sample_packing else False
        )
-        training_arguments_kwargs["multipack_real_batches"] = (
-            self.cfg.flash_attention is not True
-        )
        training_arguments_kwargs["eval_sample_packing"] = (
            self.cfg.sample_packing
            if self.cfg.eval_sample_packing is not False
@@ -988,20 +810,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
        training_arguments_kwargs[
            "sample_packing_seq_len_multiplier"
        ] = self.cfg.micro_batch_size
-        if self.cfg.relora_steps:
-            training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
-            training_arguments_kwargs[
-                "relora_warmup_steps"
-            ] = self.cfg.relora_warmup_steps
-            if self.cfg.relora_anneal_steps:
-                training_arguments_kwargs[
-                    "relora_anneal_steps"
-                ] = self.cfg.relora_anneal_steps
-            if self.cfg.relora_prune_ratio:
-                training_arguments_kwargs[
-                    "relora_prune_ratio"
-                ] = self.cfg.relora_prune_ratio
-
+        training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
+        training_arguments_kwargs["relora_warmup_steps"] = self.cfg.relora_warmup_steps
        training_arguments_kwargs = self.hook_pre_create_training_args(
            training_arguments_kwargs
        )
@@ -1013,42 +823,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                "neftune_noise_alpha"
            ] = self.cfg.neftune_noise_alpha

-        trainer_kwargs = {}
-
-        if self.cfg.optimizer == "lion_pytorch":
-            from lion_pytorch import Lion
-
-            lion_kwargs = {"lr": training_arguments_kwargs["learning_rate"]}
-            if "weight_decay" in training_arguments_kwargs:
-                lion_kwargs["weight_decay"] = training_arguments_kwargs["weight_decay"]
-
-            if (
-                "adam_beta1" in training_arguments_kwargs
-                and "adam_beta2" in training_arguments_kwargs
-            ):
-                lion_kwargs["betas"] = (
-                    training_arguments_kwargs["adam_beta1"],
-                    training_arguments_kwargs["adam_beta2"],
-                )
-
-            trainer_kwargs["optimizers"] = (
-                Lion(params=self.model.parameters(), **lion_kwargs),
-                None,
-            )
-            # Set default so transformers doesn't throw
-            training_arguments_kwargs["optim"] = "adamw_hf"
-
-        if self.cfg.optimizer == "adamw_anyprecision":
-            if Path(self.cfg.torchdistx_path).exists():
-                sys.path.append(self.cfg.torchdistx_path)
-                importlib.import_module("torchdistx")
-
        training_args = (
            AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
                **training_arguments_kwargs,
            )
        )
        training_args = self.hook_post_create_training_args(training_args)
+        trainer_kwargs = {}
+
+        if self.cfg.optimizer == "adamw_anyprecision":
+            if Path(self.cfg.torchdistx_path).exists():
+                sys.path.append(self.cfg.torchdistx_path)
+                importlib.import_module("torchdistx")

        data_collator_kwargs = {
            "padding": True,  # True/"longest" is the default
@@ -1110,27 +896,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
        if is_eval and training_args.eval_sample_packing:
            use_batch_sampler_collator = True

-        collator: Type[
-            Union[
-                V2BatchSamplerDataCollatorForSeq2Seq,
-                BatchSamplerDataCollatorForSeq2Seq,
-                DataCollatorForSeq2Seq,
-            ]
-        ]
        if use_batch_sampler_collator:
-            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
-                collator = V2BatchSamplerDataCollatorForSeq2Seq
-            elif (
-                self.cfg.model_config_type in ["llama"]
-                and self.cfg.flash_attention is not True
-            ):
-                collator = V2BatchSamplerDataCollatorForSeq2Seq
-            else:
-                collator = BatchSamplerDataCollatorForSeq2Seq
-        else:
-            collator = DataCollatorForSeq2Seq
+            return BatchSamplerDataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **kwargs,
+            )

-        return collator(
+        return DataCollatorForSeq2Seq(
            self.tokenizer,
            return_tensors="pt",
            **kwargs,
@@ -1143,7 +916,7 @@ class HFDPOTrainerBuilder(TrainerBuilderBase):
    """

    def get_callbacks(self):
-        callbacks = super().get_callbacks()
+        callbacks = []
        return callbacks

    def get_post_trainer_create_callbacks(self, trainer):
@@ -1161,82 +934,21 @@ class HFDPOTrainerBuilder(TrainerBuilderBase):
        ]:
            if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
                training_args_kwargs[arg] = getattr(self.cfg, arg)
-
-        if self.cfg.hub_model_id:
-            training_args_kwargs["hub_model_id"] = self.cfg.hub_model_id
-            training_args_kwargs["push_to_hub"] = True
-            training_args_kwargs["hub_private_repo"] = True
-            training_args_kwargs["hub_always_push"] = True
-
-            if self.cfg.hub_strategy:
-                training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
-
-        if self.cfg.save_safetensors is not None:
-            training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
-
-        if self.eval_dataset:
-            training_args_kwargs["evaluation_strategy"] = "steps"
-            training_args_kwargs["eval_steps"] = self.cfg.eval_steps
-        else:
-            training_args_kwargs["evaluation_strategy"] = "no"
-        if self.cfg.bf16 or self.cfg.bfloat16:
-            training_args_kwargs["bf16"] = True
-
-        training_args_kwargs["lr_scheduler_type"] = (
-            self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
-        )
-        training_args_kwargs["lr_scheduler_kwargs"] = (
-            self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
-        )
-        if self.cfg.remove_unused_columns is not None:
-            training_args_kwargs[
-                "remove_unused_columns"
-            ] = self.cfg.remove_unused_columns
-        else:
-            training_args_kwargs["remove_unused_columns"] = False
-
-        if self.cfg.dataloader_pin_memory is not None:
-            training_args_kwargs[
-                "dataloader_pin_memory"
-            ] = self.cfg.dataloader_pin_memory
-        if self.cfg.dataloader_num_workers is not None:
-            training_args_kwargs[
-                "dataloader_num_workers"
-            ] = self.cfg.dataloader_num_workers
-        if self.cfg.dataloader_prefetch_factor is not None:
-            training_args_kwargs[
-                "dataloader_prefetch_factor"
-            ] = self.cfg.dataloader_prefetch_factor
-        if self.cfg.gradient_checkpointing:
-            training_args_kwargs[
-                "gradient_checkpointing"
-            ] = self.cfg.gradient_checkpointing
-            if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_args_kwargs[
-                    "gradient_checkpointing_kwargs"
-                ] = self.cfg.gradient_checkpointing_kwargs
-            else:
-                training_args_kwargs["gradient_checkpointing_kwargs"] = {
-                    "use_reentrant": False
-                }
-
-        # set save_strategy and save_steps
-        if self.cfg.save_steps:
-            training_args_kwargs["save_strategy"] = "steps"
-            training_args_kwargs["save_steps"] = self.cfg.save_steps
-        elif self.cfg.save_strategy:
-            training_args_kwargs["save_strategy"] = self.cfg.save_strategy
-        else:
-            # default to saving each epoch if not defined
-            training_args_kwargs["save_strategy"] = "epoch"
-
        training_args = TrainingArguments(
            per_device_train_batch_size=self.cfg.micro_batch_size,
-            max_steps=self.cfg.max_steps or total_num_steps,
+            max_steps=total_num_steps,
+            remove_unused_columns=False,
            gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
            learning_rate=self.cfg.learning_rate,
+            evaluation_strategy="no",
+            # eval_steps=self.cfg.eval_steps,
+            save_strategy="steps",
+            save_steps=self.cfg.save_steps,
            output_dir=self.cfg.output_dir,
            warmup_steps=self.cfg.warmup_steps,
+            bf16=True,
+            gradient_checkpointing=self.cfg.gradient_checkpointing,
+            gradient_checkpointing_kwargs={"use_reentrant": False},
            logging_first_step=True,
            logging_steps=1,
            optim=self.cfg.optimizer,
@@ -1255,31 +967,22 @@ class HFDPOTrainerBuilder(TrainerBuilderBase):
                dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
        elif self.cfg.rl == "kto_pair":
            dpo_trainer_kwargs["loss_type"] = "kto_pair"
-        if self.eval_dataset:
-            dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
-        if self.cfg.adapter and self.peft_config:
-            dpo_trainer_kwargs["peft_config"] = self.peft_config
-        if self.cfg.precompute_ref_log_probs is not None:
-            dpo_trainer_kwargs[
-                "precompute_ref_log_probs"
-            ] = self.cfg.precompute_ref_log_probs
-        dpo_trainer = AxolotlDPOTrainer(
+
+        dpo_trainer = DPOTrainer(
            self.model,
            self.model_ref,
            args=training_args,
            beta=self.cfg.dpo_beta or 0.1,
            train_dataset=self.train_dataset,
+            # eval_dataset=self.eval_dataset,
+            eval_dataset=None,
            tokenizer=self.tokenizer,
            max_length=self.cfg.sequence_len,
            max_target_length=None,
            max_prompt_length=self.cfg.sequence_len,
            generate_during_eval=True,
-            callbacks=self.get_callbacks(),
            **dpo_trainer_kwargs,
        )
-        dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
-        for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
-            dpo_trainer.add_callback(callback)

        return dpo_trainer

--- a/src/axolotl/datasets.py
+++ b/src/axolotl/datasets.py
@@ -24,30 +24,26 @@ class TokenizedPromptDataset(Dataset):
        Args:
            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for processing the data.
            dataset (dataset.Dataset): Dataset with text files.
-            process_count (int): Number of processes to use for tokenizing.
-            keep_in_memory (bool): Whether to keep the tokenized dataset in memory.
    """

    def __init__(  # pylint: disable=super-init-not-called
        self,
        prompt_tokenizer: PromptTokenizingStrategy,
-        dataset: Dataset,
+        dataset: IterableDataset,
        process_count: Optional[int] = None,
-        keep_in_memory: Optional[bool] = False,
        **kwargs,
    ):
        self.prompt_tokenizer = prompt_tokenizer
        self.process_count = process_count
-        self.keep_in_memory = keep_in_memory
-        super().__init__(
-            self.process(dataset).data,
-            **kwargs,
-        )
+        super().__init__(self.process(dataset).data, **kwargs)

    def process(self, dataset):
        features = dataset.features.keys()
-        num_proc = min(64, self.process_count if self.process_count else os.cpu_count())
-
+        num_proc = (
+            min(64, self.process_count)
+            if self.process_count
+            else min(64, os.cpu_count())
+        )
        map_kwargs = {}
        if self.prompt_tokenizer.supports_batched:
            map_kwargs["batched"] = True
@@ -56,8 +52,6 @@ class TokenizedPromptDataset(Dataset):
            self.prompt_tokenizer.tokenize_prompt,
            num_proc=num_proc,
            remove_columns=features,
-            keep_in_memory=self.keep_in_memory,
-            desc="Tokenizing Prompts",
            **map_kwargs,
        )

--- a/src/axolotl/loraplus.py
+++ b/src/axolotl/loraplus.py
@@ -1,133 +0,0 @@
-"""Module for LoRA+"""
-
-# MIT License
-#
-# Copyright (c) 2024 nikhil-ghosh-berkeley
-# https://github.com/nikhil-ghosh-berkeley/loraplus
-
-import logging
-from functools import reduce
-
-from peft.tuners import lora
-from torch import nn
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.trainer_pt_utils import get_parameter_names
-
-LOG = logging.getLogger("axolotl.loraplus")
-
-
-def get_module(name, opt_model):
-    """
-    Retrieve a module from a model using its parameter name.
-    Args:
-        name (str): Full name of the parameter, typically including module path.
-        opt_model (torch.nn.Module): The model from which to retrieve the module.
-
-    Returns:
-        Module corresponding to the given name.
-    """
-    parent_idx = 2 if "lora" in name else 1
-    module_names = name.split(sep=".")[:-parent_idx]
-    module = reduce(getattr, module_names, opt_model)
-    return module
-
-
-def create_loraplus_optimizer(
-    opt_model,
-    optimizer_cls,
-    optimizer_kwargs,
-    loraplus_lr_ratio,
-    loraplus_lr_embedding=None,
-):
-    """
-    Creates an optimizer for the given model, applying LoRA-specific learning rate adjustments to different parameter groups.
-
-    Args:
-        opt_model (torch.nn.Module): The model for which the optimizer is being created.
-        optimizer_cls (class): The class of the optimizer to be used (e.g., torch.optim.Adam).
-        optimizer_kwargs (dict): A dictionary of keyword arguments for the optimizer's initialization.
-        loraplus_lr_ratio (float): The learning rate ratio to be applied to LoRA parameters.
-        loraplus_lr_embedding (float, optional): A specific learning rate for embedding parameters, with a default value if not provided.
-
-    Returns:
-        An instance of the specified optimizer class configured with the model's parameters organized into groups with custom learning rates.
-    """
-
-    assert loraplus_lr_ratio is not None, "loraplus_lr_ratio must be provided."
-
-    if loraplus_lr_embedding is None:
-        loraplus_lr_embedding = 1e-6
-
-    decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
-    param_groups = {
-        "groupA": {},
-        "groupB": {},
-        "groupB_no_decay": {},
-        "embedding": {},
-    }
-
-    for name, param in opt_model.named_parameters():
-        if not param.requires_grad:
-            continue
-
-        module = get_module(name, opt_model)
-        if isinstance(module, lora.Embedding):
-            param_groups["embedding"][name] = param
-        elif "lora_B" in name or param.ndim == 1:
-            if name in decay_parameters:
-                param_groups["groupB"][name] = param
-            else:
-                param_groups["groupB_no_decay"][name] = param
-        else:
-            param_groups["groupA"][name] = param
-
-    assigned_param_groups = ""
-    for group, group_params in param_groups.items():
-        assigned_param_groups += f"{group}\n {list(group_params.keys())}\n\n"
-    LOG.info(assigned_param_groups)
-
-    lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-    weight_decay = optimizer_kwargs.get("weight_decay", 0.0)
-
-    optimizer_grouped_parameters = [
-        {
-            "params": list(param_groups["groupA"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr,
-        },
-        {
-            "params": list(param_groups["embedding"].values()),
-            "weight_decay": weight_decay,
-            "lr": loraplus_lr_embedding,
-        },
-        {
-            "params": list(param_groups["groupB"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr * loraplus_lr_ratio,
-        },
-        {
-            "params": list(param_groups["groupB_no_decay"].values()),
-            "weight_decay": 0.0,
-            "lr": lr * loraplus_lr_ratio,
-        },
-    ]
-
-    optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-    if optimizer_cls.__name__ == "Adam8bit":
-        import bitsandbytes
-
-        manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-        skipped = 0
-        for module in opt_model.modules():
-            if isinstance(module, nn.Embedding):
-                skipped += sum(
-                    {p.data_ptr(): p.numel() for p in module.parameters()}.values()
-                )
-                LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                manager.register_module_override(module, "weight", {"optim_bits": 32})
-                LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-        LOG.info(f"skipped: {skipped/2**20}M params")
-
-    return optimizer
--- a/src/axolotl/models/phi/init.py
+++ b/src/axolotl/models/phi/init.py
@@ -0,0 +1,8 @@
+"""
+MixFormers model architecture used for phi models
+"""
+
+from .configuration_mixformer_sequential import MixFormerSequentialConfig  # noqa
+from .configuration_phi import PhiConfig  # noqa
+from .modeling_mixformer_sequential import MixFormerSequentialForCausalLM  # noqa
+from .modeling_phi import PhiForCausalLM  # noqa
--- a/src/axolotl/models/phi/configuration_mixformer_sequential.py
+++ b/src/axolotl/models/phi/configuration_mixformer_sequential.py
@@ -0,0 +1,63 @@
+# pylint: skip-file
+
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import math
+from typing import Any, Dict, List, Optional, Union
+
+from transformers import PretrainedConfig
+
+
+class MixFormerSequentialConfig(PretrainedConfig):
+    """MixFormer (sequential for DeepSpeed) configuration."""
+
+    model_type = "mixformer-sequential"
+
+    attribute_map = {
+        "max_position_embeddings": "n_positions",
+        "hidden_size": "n_embd",
+        "num_attention_heads": "n_head",
+        "num_hidden_layers": "n_layer",
+        "input_emb_layer": "embd_layer",  # `input_emb_layer` key is for backward compatibility
+        "blocks": "architecture",  # `blocks` key is for backward compatibility
+    }
+
+    def __init__(
+        self,
+        vocab_size: Optional[int] = 50304,
+        n_positions: Optional[int] = 2048,
+        n_embd: Optional[int] = 1024,
+        n_layer: Optional[int] = 20,
+        n_inner: Optional[int] = None,
+        n_head: Optional[int] = 16,
+        rotary_dim: Optional[int] = 32,
+        activation_function: Optional[str] = "gelu_new",
+        embd_layer: Optional[str] = "default",
+        architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
+        embd_pdrop: Optional[float] = 0.0,
+        resid_pdrop: Optional[float] = 0.0,
+        layer_norm_epsilon: Optional[float] = 1e-5,
+        initializer_range: Optional[float] = 0.02,
+        tie_word_embeddings: Optional[bool] = False,
+        pad_vocab_size_multiple: Optional[int] = 64,
+        **kwargs
+    ) -> None:
+        self.vocab_size = int(
+            math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
+        )
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_inner = n_inner
+        self.n_head = n_head
+        self.rotary_dim = min(rotary_dim, n_embd // n_head)
+        self.activation_function = activation_function
+        self.embd_layer = embd_layer
+        self.architecture = architecture
+        self.embd_pdrop = embd_pdrop
+        self.resid_pdrop = resid_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
--- a/src/axolotl/models/phi/configuration_phi.py
+++ b/src/axolotl/models/phi/configuration_phi.py
@@ -0,0 +1,65 @@
+# pylint: skip-file
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import math
+from typing import Optional
+
+from transformers import PretrainedConfig
+
+
+class PhiConfig(PretrainedConfig):
+    """Phi configuration."""
+
+    model_type = "phi"
+    attribute_map = {
+        "max_position_embeddings": "n_positions",
+        "hidden_size": "n_embd",
+        "num_attention_heads": "n_head",
+        "num_hidden_layers": "n_layer",
+    }
+
+    def __init__(
+        self,
+        vocab_size: int = 50304,
+        n_positions: int = 2048,
+        n_embd: int = 1024,
+        n_layer: int = 20,
+        n_inner: Optional[int] = None,
+        n_head: int = 16,
+        n_head_kv: Optional[int] = None,
+        rotary_dim: Optional[int] = 32,
+        activation_function: Optional[str] = "gelu_new",
+        flash_attn: bool = False,
+        flash_rotary: bool = False,
+        fused_dense: bool = False,
+        attn_pdrop: float = 0.0,
+        embd_pdrop: float = 0.0,
+        resid_pdrop: float = 0.0,
+        layer_norm_epsilon: float = 1e-5,
+        initializer_range: float = 0.02,
+        tie_word_embeddings: bool = False,
+        pad_vocab_size_multiple: int = 64,
+        **kwargs
+    ) -> None:
+        self.vocab_size = int(
+            math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
+        )
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_inner = n_inner
+        self.n_head = n_head
+        self.n_head_kv = n_head_kv
+        self.rotary_dim = min(rotary_dim, n_embd // n_head)
+        self.activation_function = activation_function
+        self.flash_attn = flash_attn
+        self.flash_rotary = flash_rotary
+        self.fused_dense = fused_dense
+        self.attn_pdrop = attn_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.resid_pdrop = resid_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
--- a/src/axolotl/models/phi/modeling_mixformer_sequential.py
+++ b/src/axolotl/models/phi/modeling_mixformer_sequential.py
@@ -0,0 +1,930 @@
+# pylint: skip-file
+
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# BSD 3-Clause License
+#
+# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from __future__ import annotations
+
+import copy
+import inspect
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from flash_attn.flash_attn_interface import (
+    flash_attn_kvpacked_func,
+    flash_attn_qkvpacked_func,
+    flash_attn_varlen_qkvpacked_func,
+)
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from ...monkeypatch.utils import get_cu_seqlens_from_pos_ids
+from .configuration_mixformer_sequential import MixFormerSequentialConfig
+
+
+@dataclass
+class InferenceParams:
+    """Inference parameters that are passed to the main model in order
+    to efficienly calculate and store the context during inference.
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
+
+    max_sequence_len: int
+    max_batch_size: int
+    sequence_len_offset: int = 0
+    batch_size_offset: int = 0
+    key_value_memory_dict: dict = field(default_factory=dict)
+    fused_ft_kernel: bool = False
+    lengths_per_sample: Optional[torch.Tensor] = None
+
+
+class Embedding(nn.Module):
+    """Token embedding with dropout."""
+
+    def __init__(self, config: PretrainedConfig) -> None:
+        super().__init__()
+
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.drop = nn.Dropout(config.embd_pdrop)
+
+    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        hidden_states = self.wte(input_ids)
+        hidden_states = self.drop(hidden_states)
+
+        return hidden_states
+
+
+class RotaryEmbedding(nn.Module):
+    """PyTorch implementation of `flash-attn` RotaryEmbedding layer.
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
+
+    def __init__(
+        self,
+        dim: int,
+        base: Optional[int] = 10000,
+        scale_base: Optional[float] = None,
+        device: Optional[str] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        if scale_base is not None:
+            raise NotImplementedError
+
+        # Generate and save the inverse frequency buffer (non-trainable)
+        self.dim = dim
+        self.base = base
+        self.scale_base = scale_base
+        self.device = device
+
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
+        )
+        self.register_buffer("inv_freq", inv_freq)
+
+        scale = (
+            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
+            / (1.4 * dim)
+            if scale_base is not None
+            else None
+        )
+        self.register_buffer("scale", scale)
+
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+        self._cos_k_cached = None
+        self._sin_k_cached = None
+
+    def _update_cos_sin_cache(
+        self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0
+    ) -> None:
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        seqlen = x.shape[1] + seqlen_offset
+
+        # Re-generate the inverse frequency buffer if it's not fp32
+        # (for instance if model.half() was called)
+        if self.inv_freq.dtype != "torch.float32":
+            self.inv_freq = 1.0 / (
+                self.base
+                ** (
+                    torch.arange(
+                        0, self.dim, 2, device=self.device, dtype=torch.float32
+                    )
+                    / self.dim
+                )
+            )
+
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached.device != x.device
+            or self._cos_cached.dtype != x.dtype
+        ):
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=x.device, dtype=torch.float32)
+
+            # Don't do einsum, it converts fp32 to fp16
+            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            freqs = torch.outer(
+                t, self.inv_freq.to(device=t.device, dtype=torch.float32)
+            )
+            if self.scale is None:
+                self._cos_cached = torch.cos(freqs).to(x.dtype)
+                self._sin_cached = torch.sin(freqs).to(x.dtype)
+            else:
+                power = (
+                    torch.arange(
+                        seqlen, dtype=self.scale.dtype, device=self.scale.device
+                    )
+                    - seqlen // 2
+                ) / self.scale_base
+                scale = self.scale.to(device=power.device) ** rearrange(
+                    power, "s -> s 1"
+                )
+
+                # We want the multiplication by scale to happen in fp32
+                self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
+                self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
+                self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
+                self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
+
+    def apply_rotary_emb_qkv(
+        self,
+        qkv: torch.FloatTensor,
+        sin: torch.FloatTensor,
+        cos: torch.FloatTensor,
+        sin_k: Optional[torch.FloatTensor] = None,
+        cos_k: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        _, seqlen, three, _, headdim = qkv.shape
+        assert three == 3
+
+        rotary_seqlen, rotary_dim = cos.shape
+        rotary_dim *= 2
+        assert rotary_dim <= headdim
+        assert seqlen <= rotary_seqlen
+
+        cos_k = cos if cos_k is None else cos_k
+        sin_k = sin if sin_k is None else sin_k
+        assert (
+            sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
+        )
+
+        q_rot = qkv[:, :, 0, :, :rotary_dim]
+        q_pass = qkv[:, :, 0, :, rotary_dim:]
+
+        k_rot = qkv[:, :, 1, :, :rotary_dim]
+        k_pass = qkv[:, :, 1, :, rotary_dim:]
+
+        # Splits the queries and keys in half
+        q1, q2 = q_rot.chunk(2, dim=-1)
+        k1, k2 = k_rot.chunk(2, dim=-1)
+        c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(
+            sin[:seqlen], "s d -> s 1 d"
+        )
+
+        # Casts to fp32 are necessary to prevent fp16 overflow issues
+        q1, q2, k1, k2, c, s = [
+            t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]
+        ]
+
+        # Computes the new keys and queries, recasting to original dtype
+        q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
+
+        k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
+
+        return torch.cat(
+            [
+                torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
+                torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
+                qkv[:, :, 2:3, :, :],
+            ],
+            axis=2,
+        )
+
+    def forward(
+        self, qkv: torch.Tensor, seqlen_offset: int = 0
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Perform the forward pass.
+
+        Args:
+            qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
+            seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.
+
+        Returns:
+            New `qkv` and the cached sinusoids.
+
+        """
+
+        self._update_cos_sin_cache(qkv, seqlen_offset)
+
+        return self.apply_rotary_emb_qkv(
+            qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:]
+        )
+
+
+def _update_kv_cache(kv, inference_params, layer_idx):
+    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
+    # Pre-allocate memory for key-values for inference.
+    num_heads, head_dim = kv.shape[-2:]
+    if layer_idx not in inference_params.key_value_memory_dict:
+        kv_cache = torch.empty(
+            inference_params.max_batch_size,
+            inference_params.max_sequence_len,
+            2,
+            num_heads,
+            head_dim,
+            dtype=kv.dtype,
+            device=kv.device,
+        )
+        inference_params.key_value_memory_dict[layer_idx] = kv_cache
+    else:
+        kv_cache = inference_params.key_value_memory_dict[layer_idx]
+
+    # Adjust key and value for inference
+    batch_start = inference_params.batch_size_offset
+    batch_end = batch_start + kv.shape[0]
+    sequence_start = inference_params.sequence_len_offset
+    sequence_end = sequence_start + kv.shape[1]
+    assert batch_end <= (
+        kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0]  # noqa
+    )
+    assert sequence_end <= (
+        kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2]  # noqa
+    )
+
+    assert kv_cache is not None
+    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
+    kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
+    return kv
+
+
+class MLP(nn.Module):
+    """Multi-Layer Perceptron.
+
+    Reference:
+        Attention Is All You Need.
+        https://arxiv.org/pdf/1706.03762.pdf.
+
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        n_inner: Optional[int] = None,
+        act_fn: Optional[str] = None,
+    ) -> None:
+        super().__init__()
+
+        act_fn = config.activation_function if act_fn is None else act_fn
+        assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
+
+        n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
+        n_inner = n_inner if n_inner is not None else 4 * config.n_embd
+
+        self.fc1 = nn.Linear(config.n_embd, n_inner)
+        self.fc2 = nn.Linear(n_inner, config.n_embd)
+        self.act = ACT2FN[act_fn]
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        old_keys = [
+            prefix + "fc_in.weight",
+            prefix + "fc_out.weight",
+            prefix + "fc_in.bias",
+            prefix + "fc_out.bias",
+        ]
+        new_keys = [
+            prefix + "fc1.weight",
+            prefix + "fc2.weight",
+            prefix + "fc1.bias",
+            prefix + "fc2.bias",
+        ]
+
+        if all(k in state_dict for k in old_keys) and not all(
+            k in state_dict for k in new_keys
+        ):
+            # Older version of `MLP` saved with different key names.
+            for old_key, new_key in zip(old_keys, new_keys):
+                state_dict[new_key] = state_dict.pop(old_key)
+
+        return super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+
+        return hidden_states
+
+
+class FusedMLP(nn.Module):
+    """Fused Multi-Layer Perceptron from `flash-attn`.
+
+    Reference:
+        https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.
+
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        n_inner: Optional[int] = None,
+        act_fn: Optional[str] = None,
+        raise_on_missing: bool = False,
+    ) -> None:
+        super().__init__()
+
+        act_fn = config.activation_function if act_fn is None else act_fn
+        assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
+
+        n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
+        n_inner = n_inner if n_inner is not None else 4 * config.n_embd
+
+        gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"]  # noqa
+        activation = "gelu_approx" if act_fn in gelu_activations else "relu"  # noqa
+
+        self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        return self.mlp(hidden_states)
+
+
+class SelfAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Adapted from https://github.com/Dao-AILab/flash-attention.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    def forward(
+        self, qkv, causal=None, key_padding_mask=None, cu_seqlens=None, max_seqlen=None
+    ):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
+            causal: if passed, will override self.causal
+            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
+                False means to mask out. (B, S)
+        """
+        causal = self.causal if causal is None else causal
+        if cu_seqlens is not None:
+            return flash_attn_varlen_qkvpacked_func(
+                qkv.squeeze(0),
+                cu_seqlens,
+                max_seqlen,
+                dropout_p=self.drop.p,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+            )
+        else:
+            return flash_attn_qkvpacked_func(
+                qkv,
+                dropout_p=self.drop.p,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+            )
+
+
+class CrossAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Adapted from https://github.com/Dao-AILab/flash-attention.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    def forward(self, q, kv, causal=None, key_padding_mask=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q: The tensor containing the query. (B, Sq, H, D)
+            kv: The tensor containing the key and value. (B, Sk, 2, H, D)
+            causal: if passed, will override self.causal
+            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
+                False means to mask out. (B, Sk)
+        """
+        causal = self.causal if causal is None else causal
+        return flash_attn_kvpacked_func(
+            q,
+            kv,
+            dropout_p=self.drop.p,
+            softmax_scale=self.softmax_scale,
+            causal=causal,
+        )
+
+
+def find_mha_dims(
+    config: PretrainedConfig,
+    n_head: Optional[int] = None,
+    head_dim: Optional[int] = None,
+) -> Tuple[int, int]:
+    """Validate and return the number of heads and head dimension for multi-head attention.
+
+    Args:
+        config: Model configuration.
+        n_head: Number of heads.
+        head_dim: Head dimension.
+
+    Returns:
+        Number of heads and head dimension.
+
+    """
+
+    assert all(
+        hasattr(config, attr) for attr in ["n_embd", "n_head"]
+    ), "`config` must have `n_embd` and `n_head` attributes."
+
+    if head_dim is None:
+        assert (
+            config.n_embd % config.n_head == 0
+        ), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
+
+    if n_head is None and head_dim is None:
+        head_dim = config.n_embd // config.n_head
+        n_head = config.n_head
+    elif n_head is None or head_dim is None:
+        raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
+
+    return n_head, head_dim
+
+
+class MHA(nn.Module):
+    """Multi-head attention layer.
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        rotary_dim: Optional[int] = None,
+        n_head: Optional[int] = None,
+        head_dim: Optional[int] = None,
+        bias: Optional[bool] = True,
+        dropout: Optional[float] = 0.0,
+        softmax_scale: Optional[float] = None,
+        causal: Optional[bool] = True,
+        layer_idx: Optional[int] = None,
+        rotary_emb_scale_base: Optional[float] = None,
+        return_residual: Optional[bool] = False,
+        checkpointing: Optional[bool] = False,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        fused_dense: Optional[bool] = True,
+        flash_attn: Optional[bool] = True,
+        cutlass_attn: Optional[bool] = False,
+        flash_rotary: Optional[bool] = True,
+        raise_on_missing: Optional[bool] = False,
+    ) -> None:
+        super().__init__()
+
+        factory_kwargs = {"device": device, "dtype": dtype}
+        n_head, head_dim = find_mha_dims(config, n_head, head_dim)
+
+        self.hidden_size = config.n_embd
+        self.n_head = n_head
+        self.head_dim = head_dim
+        self.op_size = n_head * head_dim
+
+        self.causal = causal
+        self.layer_idx = layer_idx
+        self.rotary_emb_dim = (
+            rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
+        )
+        self.fused_dense = fused_dense
+        self.flash_attn = flash_attn
+        self.cutlass_attn = cutlass_attn
+        self.flash_rotary = flash_rotary
+        self.return_residual = return_residual
+        self.checkpointing = checkpointing
+
+        if self.rotary_emb_dim > 0:
+            rotary_kwargs = {"device": device}
+            if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
+                rotary_kwargs["scale_base"] = rotary_emb_scale_base
+
+            self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
+        else:
+            pass
+
+        self.Wqkv = nn.Linear(
+            self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs
+        )
+        self.out_proj = nn.Linear(
+            self.op_size, self.hidden_size, bias=bias, **factory_kwargs
+        )
+
+        self.inner_attn = SelfAttention(
+            causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
+        )
+        self.inner_cross_attn = CrossAttention(
+            causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
+        )
+
+    def _update_kv_cache(
+        self, kv: torch.FloatTensor, inference_params: InferenceParams
+    ) -> None:
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
+        Adapted from https://github.com/Dao-AILab/flash-attention."""
+
+        assert (
+            self.layer_idx is not None
+        ), "Generation requires layer_idx in the constructor"
+
+        return _update_kv_cache(kv, inference_params, self.layer_idx)
+
+    def forward(
+        self,
+        x: torch.FloatTensor,
+        x_kv: Optional[torch.FloatTensor] = None,
+        key_padding_mask: Optional[torch.BoolTensor] = None,
+        cu_seqlens: Optional[torch.LongTensor] = None,
+        max_seqlen: Optional[int] = None,
+        mixer_subset: Optional[torch.LongTensor] = None,
+        past_cache: Optional[InferenceParams] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+        """Perform the forward pass.
+
+        Args:
+            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
+                cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
+                is the is the sum of the sequence lengths in the batch.
+            x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
+            key_padding_mask: boolean mask, True means to keep, False means to mask out.
+                (batch, seqlen). Only applicable when not using FlashAttention.
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into x. Only applicable when using
+                FlashAttention.
+            max_seqlen: int. Maximum sequence length in the batch.
+            mixer_subset: for cross-attention only. If not None, will take a subset of x
+                before applying the query projection. Useful for e.g., ViT where we only care
+                about the CLS token in the last layer.
+            past_cache: For generation only.
+
+        Returns:
+            (batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
+                else (total, hidden_dim) where total is the is the sum of the sequence lengths
+                in the batch.
+
+        """
+
+        if cu_seqlens is not None:
+            assert max_seqlen is not None
+            assert key_padding_mask is None
+            assert self.flash_attn
+            # assert self.rotary_emb_dim == 0
+
+        if key_padding_mask is not None:
+            assert cu_seqlens is None
+            assert max_seqlen is None
+            assert not self.flash_attn
+
+        if past_cache is not None:
+            assert key_padding_mask is None
+            assert cu_seqlens is None and max_seqlen is None
+
+        attn_kwargs = {"key_padding_mask": key_padding_mask}
+
+        assert x_kv is None and mixer_subset is None
+
+        qkv = self.Wqkv(x)
+        qkv = rearrange(
+            qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim
+        )
+
+        if past_cache is None:
+            if self.rotary_emb_dim > 0:
+                qkv = self.rotary_emb(qkv)
+                context = self.inner_attn(
+                    qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, **attn_kwargs
+                )
+
+        else:
+            if self.rotary_emb_dim > 0:
+                qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
+            q = qkv[:, :, 0]
+            kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
+            # If we're processing the prompt, causal=None (use self.causal).
+            # If we're decoding, then causal=False.
+            causal = None if past_cache.sequence_len_offset == 0 else False
+            context = self.inner_cross_attn(q, kv, causal=causal)
+
+        out = rearrange(context, "... h d -> ... (h d)")
+        out = self.out_proj(out)
+
+        return out if not self.return_residual else (out, x)
+
+
+class ParallelBlock(nn.Module):
+    """Parallel block.
+
+    This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
+
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        mixer: Optional[Dict[str, Any]] = None,
+        mlp: Optional[Dict[str, Any]] = None,
+        block_idx: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+
+        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+        self.block_idx = block_idx
+
+        self.mixer = MHA(config, layer_idx=block_idx)
+        self.mlp = MLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        past_cache: Optional[torch.FloatTensor] = None,
+        cu_seqlens: Optional[torch.LongTensor] = None,
+        max_seqlen: Optional[int] = None,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+        hidden_states = self.ln(hidden_states)
+
+        attn_outputs = self.mixer(
+            hidden_states,
+            past_cache=past_cache,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        if isinstance(attn_outputs, tuple):
+            attn_outputs = attn_outputs[0]
+
+        attn_outputs = self.resid_dropout(attn_outputs)
+        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
+
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+
+        return hidden_states
+
+
+class CausalLMHead(nn.Module):
+    """Causal Language Modeling head.
+
+    Reference:
+        Improving Language Understanding by Generative Pre-Training.
+        https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
+
+    """
+
+    def __init__(self, config: PretrainedConfig) -> None:
+        super().__init__()
+
+        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.linear = nn.Linear(config.n_embd, config.vocab_size)
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        hidden_states = self.ln(hidden_states)
+        logits = self.linear(hidden_states).to(torch.float32)
+
+        return logits
+
+
+class CausalLMLoss(nn.Module):
+    """Causal Language Modeling loss.
+
+    Reference:
+        Improving Language Understanding by Generative Pre-Training.
+        https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
+
+    """
+
+    def __init__(self, shift_labels: Optional[bool] = True) -> None:
+        super().__init__()
+
+        self.shift_labels = shift_labels
+        self.loss_fct = nn.CrossEntropyLoss()
+
+    def forward(
+        self, logits: torch.FloatTensor, labels: torch.LongTensor
+    ) -> torch.FloatTensor:
+        if self.shift_labels:
+            logits = logits[..., :-1, :].contiguous()
+            labels = labels[..., 1:].contiguous()
+
+        loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
+
+        return loss
+
+
+class MixFormerSequentialPreTrainedModel(PreTrainedModel):
+    """MixFormer (sequential for DeepSpeed) pre-trained model."""
+
+    config_class = MixFormerSequentialConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+
+    def __init__(self, *inputs, **kwargs) -> None:
+        super().__init__(*inputs, **kwargs)
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, **kwargs
+    ) -> Dict[str, Any]:
+        if "use_cache" in kwargs and not kwargs["use_cache"]:
+            return {"input_ids": input_ids}
+
+        if past_key_values is None or not (
+            isinstance(past_key_values, InferenceParams)
+        ):
+            past_key_values = InferenceParams(
+                max_batch_size=input_ids.shape[0],
+                max_sequence_len=self.config.n_positions,
+                sequence_len_offset=0,
+                batch_size_offset=0,
+                fused_ft_kernel=False,
+                key_value_memory_dict={},
+            )
+        else:
+            # assume past_key_values has cached all but last token in input_ids
+            past_key_values.sequence_len_offset = len(input_ids[0]) - 1
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+
+        return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}
+
+
+class PackedSequential(nn.Sequential):
+    def forward(
+        self,
+        input,
+        cu_seqlens: Optional[torch.LongTensor] = None,
+        max_seqlen: Optional[int] = None,
+    ):
+        for module in self:
+            sig = inspect.signature(module.forward)
+            if "cu_seqlens" in sig.parameters:
+                input = module(input, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
+            else:
+                input = module(input)
+        return input
+
+
+class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
+    """MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
+
+    _keys_to_ignore_on_load_missing = [""]
+    _keys_to_ignore_on_load_unexpected = [
+        r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"
+    ]
+    _no_split_modules = ["ParallelBlock"]
+
+    def __init__(self, config: MixFormerSequentialConfig) -> None:
+        super().__init__(config)
+
+        modules = [Embedding(config)]
+        block_config = config.architecture
+
+        if not isinstance(block_config, list):
+            block_config = [block_config for _ in range(config.n_layer)]
+
+        if config.n_layer != len(block_config):
+            config.n_layer = len(block_config)
+
+        for block_idx, block in enumerate(block_config):
+            # `block_cls` with `legacy` value is for backward compatibility
+            # `path` key is for backward compatibility
+            block = copy.deepcopy(block) or {"block_cls": "parallel"}
+            block.pop("path", None) or block.pop("block_cls", None)
+
+            block["block_idx"] = block_idx
+            modules.append(ParallelBlock(config, **block))
+
+        modules.append(CausalLMHead(config))
+
+        self.layers = PackedSequential(*modules)
+        self.loss = CausalLMLoss()
+
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.layers[0].wte
+
+    def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
+        self.layers[0].wte = new_embeddings
+
+    def get_output_embeddings(self) -> nn.Linear:
+        return self.layers[-1].linear
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
+        self.layers[-1].linear = new_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        labels: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> CausalLMOutputWithPast:
+        cu_seqlens: Optional[torch.LongTensor] = None
+        max_seqlen: Optional[int] = None
+        if position_ids is not None:
+            batch_size, seq_length = input_ids.shape
+            position_ids = position_ids.view(-1, seq_length).long()
+            cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
+            cu_seqlens = cu_seqlens.squeeze()
+
+        if not past_key_values:
+            lm_logits = self.layers(
+                input_ids, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
+            )
+        else:
+            hidden_layer = self.layers[0](input_ids)
+            for module in self.layers[1:-1]:
+                hidden_layer = module(
+                    hidden_layer,
+                    past_cache=past_key_values,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                )
+            lm_logits = self.layers[-1](hidden_layer)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss(lm_logits, labels)
+
+        return CausalLMOutputWithPast(
+            loss=loss, logits=lm_logits, past_key_values=past_key_values
+        )
--- a/src/axolotl/models/phi/modeling_phi.py
+++ b/src/axolotl/models/phi/modeling_phi.py
--- a/src/axolotl/monkeypatch/data/init.py
+++ b/src/axolotl/monkeypatch/data/init.py
--- a/Show More
+++ b/Show More