WIP

* add docs

* add docs

* run linter

.github/ISSUE_TEMPLATE/bug-report.yaml

@@ -59,6 +59,7 @@ body:
       label: Config yaml
       description: |
         Please attach the config yaml!
+      render: yaml
 
   - type: textarea
     id: possible-solution

.github/workflows/lint.yml

@@ -17,6 +17,6 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.9"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.0

.github/workflows/main.yml

@@ -18,6 +18,7 @@ jobs:
             python_version: "3.10"
             pytorch: 2.1.2
             axolotl_extras:
+            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
             is_latest: true
           - cuda: 121
             cuda_version: 12.1.0
@@ -54,6 +55,7 @@ jobs:
             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: |

.github/workflows/tests.yml

@@ -23,7 +23,7 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.9"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.0
 
@@ -33,7 +33,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.9", "3.10", "3.11"]
+        python_version: ["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, gpu, docker]
-    timeout-minutes: 30
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 60
     needs: [pre-commit, pytest]
 
     strategy:
@@ -70,43 +70,31 @@ jobs:
             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
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.10"
             pytorch: 2.1.2
+            num_gpus: 1
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v5
+      - name: Install Python
+        uses: actions/setup-python@v5
         with:
-          images: winglian/axolotl-tests
-      - name: Build Docker image
+          python-version: "3.10"
+      - name: Install Modal
         run: |
-          # 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
+          python -m pip install --upgrade pip
+          pip install modal jinja2
+      - name: Update env vars
         run: |
-          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
+          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
         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 --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/
-      - name: Prune image from docker
-        if: github.ref != 'refs/heads/main'
-        run: |
-          docker rmi -f ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+          modal run cicd.tests

.gitignore

@@ -167,3 +167,8 @@ cython_debug/
 # WandB
 # wandb creates a folder to store logs for training runs
 wandb
+
+# Runs
+lora-out/*
+qlora-out/*
+mlruns/*

.mypy.ini

@@ -1,5 +1,5 @@
 [mypy]
-
+plugins = pydantic.mypy
 exclude = venv
 
 [mypy-alpaca_lora_4bit.*]

.pre-commit-config.yaml

@@ -31,6 +31,7 @@ repos:
       additional_dependencies:
         [
             'types-PyYAML',
+            'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
     rev: 1.7.5

README.md

@@ -22,7 +22,7 @@ Features:
 - [Introduction](#axolotl)
 - [Supported Features](#axolotl-supports)
 - [Quickstart](#quickstart-)
-- [Installation](#installation)
+- [Environment](#environment)
   - [Docker](#docker)
   - [Conda/Pip venv](#condapip-venv)
   - [Cloud GPU](#cloud-gpu) - Latitude.sh, RunPod
@@ -87,15 +87,17 @@ 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 >=3.9 and Pytorch >=2.0.
-
-`pip3 install "axolotl[flash-attn,deepspeed] @ git+https://github.com/OpenAccess-AI-Collective/axolotl"`
+**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
 
 ### For developers
 ```bash
@@ -103,9 +105,18 @@ 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
@@ -127,13 +138,14 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
 accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/examples/openllama-3b/lora.yml
 ```
 
-## Installation
+## Advanced Setup
 
 ### Environment
 
 #### Docker
+
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -152,7 +164,7 @@ 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 ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
   ```
 
   It additionally:
@@ -167,7 +179,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
   </details>
 
 #### Conda/Pip venv
-  1. Install python >=**3.9**
+  1. Install python >=**3.10**
 
   2. Install pytorch stable https://pytorch.org/get-started/locally/
 
@@ -200,11 +212,11 @@ For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud
   1. Install python
   ```bash
   sudo apt update
-  sudo apt install -y python3.9
+  sudo apt install -y python3.10
 
-  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
+  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
 
   ```
 
@@ -242,15 +254,18 @@ 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
@@ -260,32 +275,33 @@ 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):
 
-- `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
-    ```
+#### 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": "..."}
+  ```
+
 <details>
 
 <summary>See other formats</summary>
@@ -362,14 +378,37 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
   ```
-- `pygmalion`: pygmalion
-  ```json
-  {"conversations": [{"role": "...", "value": "..."}]}
-  ```
 - `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": "..."}]}
+  ```
 - `sharegpt.load_role`: conversations where `role` is used instead of `from`
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
@@ -385,6 +424,8 @@ Have dataset(s) in one of the following format (JSONL recommended):
 
 </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:
@@ -406,12 +447,16 @@ datasets:
       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
 
@@ -425,22 +470,18 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 
 - dataset
   ```yaml
-  sequence_len: 2048 # max token length for prompt
-
-  # huggingface repo
   datasets:
+      # huggingface repo
     - path: vicgalle/alpaca-gpt4
-      type: alpaca # format from earlier
+      type: alpaca
 
-  # huggingface repo with specific configuration/subset
-  datasets:
+      # huggingface repo with specific configuration/subset
     - 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
-  datasets:
+      # huggingface repo with multiple named configurations/subsets
     - path: bigcode/commitpackft
       name:
         - ruby
@@ -448,34 +489,29 @@ 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
-  datasets:
+      # fastchat conversation
+      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
       type: sharegpt
-      conversation: chatml
+      conversation: chatml # default: vicuna_v1.1
 
-  # local
-  datasets:
+      # local
     - path: data.jsonl # or json
       ds_type: json # see other options below
       type: alpaca
 
-  # dataset with splits, but no train split
-  dataset:
+      # dataset with splits, but no train split
     - 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
-  dataset:
+      # loading from s3 or gcs
+      # s3 creds will be loaded from the system default and gcs only supports public access
     - 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.
-  dataset:
+      # 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.
   ```
@@ -484,9 +520,11 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
   ```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
   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
   ```
@@ -494,7 +532,7 @@ 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
@@ -503,9 +541,9 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
     - v_proj
   ```
 
-<details>
+<details id="all-yaml-options">
 
-<summary>All yaml options (click me)</summary>
+<summary>All yaml options (click to expand)</summary>
 
 ```yaml
 # This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
@@ -517,8 +555,8 @@ 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
-model_revision:
-# Optional tokenizer configuration override in case you want to use a different tokenizer
+revision_of_model:
+# Optional tokenizer configuration path 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
@@ -535,15 +573,16 @@ 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
-model_config:
+overrides_of_model_config:
   # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
   rope_scaling:
     type: # linear | dynamic
@@ -560,8 +599,6 @@ 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
@@ -635,7 +672,7 @@ test_datasets:
     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
@@ -655,7 +692,7 @@ dataset_processes: # defaults to os.cpu_count() if not set
 # Only needed if cached dataset is taking too much storage
 dataset_keep_in_memory:
 # push checkpoints to hub
-hub_model_id: # repo path to push finetuned model
+hub_model_id: # private 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:
@@ -751,6 +788,7 @@ 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
@@ -819,10 +857,6 @@ cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosin
 # 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
@@ -1106,7 +1140,7 @@ 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"
@@ -1167,7 +1201,7 @@ 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.
+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.
 
 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.
@@ -1214,11 +1248,20 @@ 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.

cicd/Dockerfile.jinja

@@ -0,0 +1,39 @@
+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

cicd/cicd.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+
+pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest /workspace/axolotl/tests/e2e/patched/
+pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/

cicd/tests.py

@@ -0,0 +1,75 @@
+"""
+ 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()

docker/Dockerfile

@@ -3,9 +3,10 @@ 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="2.0.1"
+ARG PYTORCH_VERSION="2.1.2"
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
@@ -20,9 +21,9 @@ 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]; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm]; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

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

docker/Dockerfile-tests

@@ -3,9 +3,10 @@ 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="2.0.1"
+ARG PYTORCH_VERSION="2.1.2"
 ARG GITHUB_REF="main"
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
@@ -24,9 +25,9 @@ RUN git fetch origin +$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]; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm]; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docs/debugging.md

@@ -74,7 +74,6 @@ 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
 

docs/input_output.md

@@ -0,0 +1,260 @@
+# 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>"
+            }
+        ]
+    }
+```

docs/mac.md

@@ -0,0 +1,18 @@
+# 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

examples/gemma/qlora.yml

@@ -21,8 +21,8 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: false
+sample_packing: true
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/mistral/lora-mps.yml

@@ -0,0 +1,79 @@
+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:

examples/stablelm-2/1.6b/fft.yml

@@ -0,0 +1,69 @@
+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:

examples/stablelm-2/1.6b/lora.yml

@@ -0,0 +1,66 @@
+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:

examples/stablelm-2/README.md

@@ -0,0 +1,36 @@
+# 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
+```

examples/starcoder2/qlora.yml

@@ -0,0 +1,69 @@
+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:

examples/tiny-llama/lora.yml

@@ -15,6 +15,7 @@ output_dir: ./lora-out
 
 sequence_len: 4096
 sample_packing: true
+eval_sample_packing: false
 pad_to_sequence_len: true
 
 adapter: lora

requirements.txt

@@ -1,11 +1,12 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft @ git+https://github.com/huggingface/peft.git
-transformers @ git+https://github.com/huggingface/transformers.git@ae49b218c3d718df90d8e4a109016450fb8f0632
+peft==0.9.0
+transformers==4.38.2
 tokenizers==0.15.0
 bitsandbytes>=0.41.1
 accelerate==0.26.1
-deepspeed>=0.13.1
+deepspeed==0.13.1
+pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
@@ -21,14 +22,13 @@ hf_transfer
 colorama
 numba
 numpy>=1.24.4
-mlflow
 # qlora things
 evaluate==0.4.1
 scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat==0.2.34
+fschat==0.2.36
 gradio==3.50.2
 tensorboard
 

setup.py

@@ -18,6 +18,7 @@ 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
@@ -73,7 +74,7 @@ setup(
             "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==0.13.1",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [
@@ -82,5 +83,11 @@ setup(
         "auto-gptq": [
             "auto-gptq==0.5.1",
         ],
+        "mlflow": [
+            "mlflow",
+        ],
+        "lion-pytorch": [
+            "lion-pytorch==0.1.2",
+        ],
     },
 )

src/axolotl/cli/__init__.py

@@ -13,7 +13,6 @@ from threading import Thread
 from typing import Any, Dict, List, Optional, Union
 from urllib.parse import urlparse
 
-import gradio as gr
 import requests
 import torch
 import yaml
@@ -24,6 +23,7 @@ from art import text2art
 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
@@ -214,6 +214,8 @@ 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>"}
@@ -328,7 +330,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
     # 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()
@@ -341,7 +342,22 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
             else:
                 cfg[k] = kwargs[k]
 
-    validate_config(cfg)
+    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,
+        },
+    )
 
     prepare_optim_env(cfg)
 

src/axolotl/core/trainer_builder.py

@@ -5,6 +5,7 @@ Builder for the training args and trainer
 
 import abc
 import importlib
+import importlib.util
 import logging
 import math
 import sys
@@ -26,15 +27,16 @@ from transformers import (
     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,
     GPUStatsCallback,
     LossWatchDogCallback,
-    SaveAxolotlConfigtoMlflowCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
@@ -54,6 +56,9 @@ from axolotl.utils.schedulers import (
     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:
@@ -62,6 +67,10 @@ 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]
@@ -175,6 +184,13 @@ class AxolotlTrainingArguments(TrainingArguments):
             "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):
@@ -199,6 +215,33 @@ 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
     ):
@@ -648,7 +691,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
-        if self.cfg.use_mlflow:
+        if self.cfg.use_mlflow and is_mlflow_available():
+            from axolotl.utils.callbacks.mlflow_ import (
+                SaveAxolotlConfigtoMlflowCallback,
+            )
+
             callbacks.append(
                 SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
             )
@@ -694,7 +741,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return AxolotlTrainer
 
     def build(self, total_num_steps):
-        warmup_steps = None
         if self.cfg.warmup_steps is not None:
             warmup_steps = self.cfg.warmup_steps
         elif self.cfg.warmup_ratio is not None:
@@ -907,6 +953,10 @@ 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
@@ -962,18 +1012,42 @@ 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

src/axolotl/loraplus.py

@@ -0,0 +1,133 @@
+"""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

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -106,7 +106,7 @@ def get_turns(  # pylint: disable=too-many-return-statements
         if self.system_message:
             contains_sys_msg = True
             if self.messages:
-                # There is no clear guidance on how to handle system messages in Mistral so we just prepend it to the first human instruction seperated by a newline
+                # There is no clear guidance on how to handle system messages in Mistral so we just prepend it to the first human instruction separated by a newline
                 first_role, first_msg = self.messages[0]
                 if first_role == self.roles[0]:
                     system_prompt = self.system_template.format(

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -44,6 +44,18 @@ except ImportError:
 LOG = logging.getLogger("axolotl")
 
 
+def is_xformers_swiglu_available() -> bool:
+    from xformers.ops.common import get_xformers_operator
+
+    try:
+        get_xformers_operator("swiglu_packedw")()
+        return True
+    except RuntimeError as exc:
+        if "No such operator xformers::swiglu_packedw " in str(exc):
+            return False
+        return True
+
+
 def replace_llama_mlp_with_swiglu(model):
     for name, module in model.named_modules():
         if isinstance(module, LlamaMLP):

src/axolotl/monkeypatch/multipack.py

@@ -6,7 +6,14 @@ from transformers.integrations import is_deepspeed_zero3_enabled
 from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
-SUPPORTED_MULTIPACK_MODEL_TYPES = ["mixtral", "qwen2", "falcon", "phi", "gemma"]
+SUPPORTED_MULTIPACK_MODEL_TYPES = [
+    "mixtral",
+    "qwen2",
+    "falcon",
+    "phi",
+    "gemma",
+    "starcoder2",
+]
 
 
 def patch_for_multipack(model_type):
@@ -32,3 +39,7 @@ def patch_for_multipack(model_type):
         transformers.models.gemma.modeling_gemma._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
+    elif model_type == "starcoder2":
+        transformers.models.starcoder2.modeling_starcoder2._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )

src/axolotl/monkeypatch/relora.py

@@ -267,7 +267,7 @@ class ReLoRAScheduler(LRScheduler):
         original = self.inner_schedule.get_lr()
         step = self.last_epoch
 
-        if step < self.relora_steps:
+        if step < self.relora_steps - self.warmup_steps:
             scale = 1
         else:
             per_relora_progress = step % self.relora_steps

src/axolotl/prompt_strategies/chat_template.py

@@ -0,0 +1,78 @@
+"""
+HF Chat Templates prompt strategy
+"""
+from typing import Any, Dict, Optional
+
+from axolotl.prompt_tokenizers import PromptTokenizingStrategy
+from axolotl.prompters import Prompter
+from axolotl.utils.chat_templates import chat_templates
+
+
+class ChatTemplatePrompter(Prompter):
+    """prompter for HF chat templates"""
+
+    def __init__(self, tokenizer, chat_template=None, max_length=2048):
+        self.tokenizer = tokenizer
+        self.chat_template = chat_template
+        self.max_length = max_length
+
+    def build_prompt(self, conversation, add_generation_prompt=False):
+        return self.tokenizer.apply_chat_template(
+            conversation,
+            truncation=True,
+            max_length=self.max_length,
+            add_generation_prompt=add_generation_prompt,
+            chat_template=self.chat_template,
+        )
+
+
+class ChatTemplateStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for instruction-based prompts.
+    """
+
+    def tokenize_prompt(self, prompt):
+        turns = self.get_conversation_thread(prompt)
+        prompt_ids = self.prompter.build_prompt([turns[0]], add_generation_prompt=True)
+        input_ids = self.prompter.build_prompt(turns)
+
+        if not self.train_on_inputs:
+            user_prompt_len = len(prompt_ids)
+            labels = [-100] * user_prompt_len + input_ids[user_prompt_len:]
+        else:
+            labels = input_ids
+
+        tokenized_prompt = {
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": [1] * len(input_ids),
+        }
+
+        return tokenized_prompt
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap roles - allow for assistant turn
+        role_map = {
+            "human": "user",
+            "user": "user",
+            "assistant": "assistant",
+            "gpt": "assistant",
+        }
+        turns = [
+            {"role": role_map[t["from"]], "content": t["value"]} for t in conversations
+        ]
+        return turns
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    chat_template = (
+        ds_cfg["chat_template"] if ds_cfg and "chat_template" in ds_cfg else "chatml"
+    )
+    strategy = ChatTemplateStrategy(
+        ChatTemplatePrompter(tokenizer, chat_templates(chat_template)),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+    return strategy

src/axolotl/prompt_strategies/dpo/__init__.py

@@ -8,14 +8,13 @@ import logging
 LOG = logging.getLogger("axolotl")
 
 
-def load(strategy, cfg):
+def load(strategy, cfg, **kwargs):
     try:
         load_fn = strategy.split(".")[-1]
         strategy = ".".join(strategy.split(".")[:-1])
         mod = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies.dpo")
         func = getattr(mod, load_fn)
-        load_kwargs = {}
-        return func(cfg, **load_kwargs)
+        return func(cfg, **kwargs)
     except Exception:  # pylint: disable=broad-exception-caught
         LOG.warning(f"unable to load strategy {strategy}")
         return None

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -5,6 +5,7 @@ DPO strategies for chatml
 
 def argilla(
     cfg,
+    **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
         if "system" in sample and sample["system"]:
@@ -25,6 +26,7 @@ def argilla(
 
 def icr(
     cfg,
+    **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     """
     chatml transforms for datasets with system, input, chosen, rejected
@@ -48,7 +50,7 @@ def icr(
     return transform_fn
 
 
-def intel(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
     """
     For Intel Orca DPO Pairs
     """
@@ -70,7 +72,9 @@ def intel(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
     return transform_fn
 
 
-def prompt_pairs(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+def prompt_pairs(
+    cfg, **kwargs
+):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
@@ -88,7 +92,7 @@ def prompt_pairs(cfg):  # pylint: disable=possibly-unused-variable,unused-argume
     return transform_fn
 
 
-def ultra(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
     """
     for ultrafeedback binarized conversations
     """

src/axolotl/prompt_strategies/dpo/user_defined.py

@@ -0,0 +1,41 @@
+"""
+User-defined DPO strategies
+"""
+
+
+def default(cfg, dataset_idx=0, **kwargs):  # pylint: disable=unused-argument
+    ds_cfg = cfg["datasets"][dataset_idx]["type"]
+    if not isinstance(ds_cfg, dict):
+        raise ValueError(
+            f"User-defined dataset type must be a dictionary. Got: {ds_cfg}"
+        )
+    field_prompt = ds_cfg.get("field_prompt", "prompt")
+    field_system = ds_cfg.get("field_system", "system")
+    field_chosen = ds_cfg.get("field_chosen", "chosen")
+    field_rejected = ds_cfg.get("field_rejected", "rejected")
+    prompt_format = ds_cfg.get("prompt_format")
+    if not prompt_format:
+        prompt_format = "{" + field_prompt + "}"
+    chosen_format = ds_cfg.get("chosen_format")
+    if not chosen_format:
+        chosen_format = "{" + field_chosen + "}"
+    rejected_format = ds_cfg.get("rejected_format")
+    if not rejected_format:
+        rejected_format = "{" + field_rejected + "}"
+
+    def transform_fn(sample):
+        if (
+            "{" + field_system + "}" in prompt_format
+            and field_system in sample
+            and sample[field_system]
+        ):
+            sample["prompt"] = prompt_format.format(
+                system=sample[field_system], prompt=sample[field_prompt]
+            )
+        else:
+            sample["prompt"] = prompt_format.format(prompt=sample["prompt"])
+        sample["chosen"] = chosen_format.format(chosen=sample[field_chosen])
+        sample["rejected"] = rejected_format.format(rejected=sample[field_rejected])
+        return sample
+
+    return transform_fn

src/axolotl/prompt_strategies/dpo/zephyr.py

@@ -3,7 +3,7 @@ DPO strategies for zephyr
 """
 
 
-def nectar(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+def nectar(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
         data = {}
         data["prompt"] = (

src/axolotl/prompt_strategies/input_output.py

@@ -0,0 +1,54 @@
+"""Module for plain input/output prompt pairs"""
+from typing import Generator, Tuple
+
+from axolotl.prompt_tokenizers import PromptTokenizingStrategy
+from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
+
+
+class RawInputOutputStrategy(PromptTokenizingStrategy):
+    """Prompt Strategy class for input/output pairs"""
+
+    def __init__(self, *args, eos_token=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.eos_token = eos_token
+        if not eos_token:
+            self.eos_token = self.tokenizer.eos_token
+
+    def tokenize_prompt(self, prompt):
+        # pylint: disable=duplicate-code
+        input_ids = []
+        labels = []
+        for label, text in self.prompter.build_prompt(prompt["segments"]):
+            tokenized_output = self.tokenizer(
+                text, add_special_tokens=False, return_tensors=None
+            )["input_ids"]
+            input_ids += tokenized_output
+            if label or self.train_on_inputs:
+                labels += tokenized_output
+            else:
+                labels += [IGNORE_TOKEN_ID] * len(tokenized_output)
+
+        tokenized_prompt = {
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": [1] * len(input_ids),
+        }
+
+        return tokenized_prompt
+
+
+class RawInputOutputPrompter(Prompter):
+    """prompter for raw i/o data"""
+
+    def build_prompt(self, source) -> Generator[Tuple[bool, str], None, None]:
+        for segment in source:
+            yield segment["label"], segment["text"]
+
+
+def load(tokenizer, cfg):
+    return RawInputOutputStrategy(
+        RawInputOutputPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )

src/axolotl/prompt_strategies/sharegpt.py

@@ -82,7 +82,7 @@ class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
     basic sharegpt strategy to grab conversations from the sample row
     """
 
-    _strict = True
+    _strict = False
 
     @property
     def strict(self):
@@ -96,10 +96,25 @@ class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
         conversations = prompt["conversations"]
         if self.strict:
             return conversations
-        # remap roles - allow for assistant turn
-        role_map = {"human": "human", "assistant": "gpt", "gpt": "gpt"}
+        role_key = "from"
+        if "role" in conversations[0].keys():
+            role_key = "role"
+        value_key = "value"
+        if "text" in conversations[0].keys():
+            value_key = "text"
+        elif "content" in conversations[0].keys():
+            value_key = "content"
+        # remap roles - allow for assistant turn"
+        role_map = {
+            "user": "human",
+            "human": "human",
+            "assistant": "gpt",
+            "gpt": "gpt",
+            "system": "system",
+        }
         turns = [
-            {"from": role_map[t["from"]], "value": t["value"]} for t in conversations
+            {"from": role_map[t[role_key]], "value": t[value_key]}
+            for t in conversations
         ]
         return turns
 

src/axolotl/train.py

@@ -11,7 +11,7 @@ import torch
 import transformers.modelcard
 from accelerate.logging import get_logger
 from datasets import Dataset
-from peft import PeftModel
+from peft import PeftModel, PeftModelForCausalLM
 from pkg_resources import get_distribution  # type: ignore
 from transformers import PreTrainedModel, PreTrainedTokenizer
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
@@ -207,6 +207,20 @@ def train(
 
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
+    if cfg.adapter and isinstance(model, (PeftModel, PeftModelForCausalLM)):
+        model.to("cpu")
+        model = model.merge_and_unload()
+
+        if cfg.local_rank == 0:
+            LOG.info(f"saving merged model to: {str(Path(cfg.output_dir) / 'merged')}")
+            model.save_pretrained(
+                str(Path(cfg.output_dir) / "merged"),
+                safe_serialization=safe_serialization,
+                progressbar=True,
+            )
+            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+
+
     if not cfg.hub_model_id:
         try:
             trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))

src/axolotl/utils/callbacks/__init__.py

@@ -9,7 +9,6 @@ from tempfile import NamedTemporaryFile
 from typing import TYPE_CHECKING, Dict, List
 
 import evaluate
-import mlflow
 import numpy as np
 import pandas as pd
 import torch
@@ -42,8 +41,8 @@ from axolotl.utils.distributed import (
 if TYPE_CHECKING:
     from axolotl.core.trainer_builder import AxolotlTrainingArguments
 
-LOG = logging.getLogger("axolotl.callbacks")
 IGNORE_INDEX = -100
+LOG = logging.getLogger("axolotl.callbacks")
 
 
 class EvalFirstStepCallback(
@@ -756,31 +755,3 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
         return control
-
-
-class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
-    """Callback to save axolotl config to mlflow"""
-
-    def __init__(self, axolotl_config_path):
-        self.axolotl_config_path = axolotl_config_path
-
-    def on_train_begin(
-        self,
-        args: AxolotlTrainingArguments,  # pylint: disable=unused-argument
-        state: TrainerState,  # pylint: disable=unused-argument
-        control: TrainerControl,
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        if is_main_process():
-            try:
-                with NamedTemporaryFile(
-                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
-                ) as temp_file:
-                    copyfile(self.axolotl_config_path, temp_file.name)
-                    mlflow.log_artifact(temp_file.name, artifact_path="")
-                    LOG.info(
-                        "The Axolotl config has been saved to the MLflow artifacts."
-                    )
-            except (FileNotFoundError, ConnectionError) as err:
-                LOG.warning(f"Error while saving Axolotl config to MLflow: {err}")
-        return control

src/axolotl/utils/callbacks/mlflow_.py

@@ -0,0 +1,44 @@
+"""MLFlow module for trainer callbacks"""
+import logging
+from shutil import copyfile
+from tempfile import NamedTemporaryFile
+from typing import TYPE_CHECKING
+
+import mlflow
+from transformers import TrainerCallback, TrainerControl, TrainerState
+
+from axolotl.utils.distributed import is_main_process
+
+if TYPE_CHECKING:
+    from axolotl.core.trainer_builder import AxolotlTrainingArguments
+
+LOG = logging.getLogger("axolotl.callbacks")
+
+
+class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
+    # pylint: disable=duplicate-code
+    """Callback to save axolotl config to mlflow"""
+
+    def __init__(self, axolotl_config_path):
+        self.axolotl_config_path = axolotl_config_path
+
+    def on_train_begin(
+        self,
+        args: "AxolotlTrainingArguments",  # pylint: disable=unused-argument
+        state: TrainerState,  # pylint: disable=unused-argument
+        control: TrainerControl,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        if is_main_process():
+            try:
+                with NamedTemporaryFile(
+                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+                ) as temp_file:
+                    copyfile(self.axolotl_config_path, temp_file.name)
+                    mlflow.log_artifact(temp_file.name, artifact_path="")
+                    LOG.info(
+                        "The Axolotl config has been saved to the MLflow artifacts."
+                    )
+            except (FileNotFoundError, ConnectionError) as err:
+                LOG.warning(f"Error while saving Axolotl config to MLflow: {err}")
+        return control

src/axolotl/utils/chat_templates.py

@@ -22,6 +22,7 @@ def chat_templates(user_choice: str):
         "alpaca": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '### Instruction: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ '### Response: ' + message['content'] + eos_token}}{% endif %}{% endfor %}",
         "inst": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # I don't know what this one is called. Used by Mistral/Mixtral.
         "chatml": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful assistant.' %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{{'<|im_start|>system\n' + system_message + '<|im_end|>\n'}}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+        "gemma": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
     }
 
     if user_choice in templates:

src/axolotl/utils/config/__init__.py

@@ -3,11 +3,16 @@ import json
 import logging
 import os
 from pathlib import Path
+from typing import Optional
 
 import torch
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlConfigWCapabilities,
+    AxolotlInputConfig,
+)
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model_config
 
@@ -119,7 +124,7 @@ def normalize_config(cfg):
         (hasattr(model_config, "model_type") and model_config.model_type == "llama")
         or cfg.is_llama_derived_model
         or "llama" in cfg.base_model.lower()
-        or (cfg.model_type and "llama" in cfg.model_type.lower())
+        or (cfg.type_of_model and "llama" in cfg.type_of_model.lower())
     )
 
     # figure out if the model is falcon
@@ -135,7 +140,7 @@ def normalize_config(cfg):
         )
         or cfg.is_falcon_derived_model
         or "falcon" in cfg.base_model.lower()
-        or (cfg.model_type and "rwforcausallm" in cfg.model_type.lower())
+        or (cfg.type_of_model and "rwforcausallm" in cfg.type_of_model.lower())
     )
 
     cfg.is_mistral_derived_model = (
@@ -148,7 +153,7 @@ def normalize_config(cfg):
         )
         or cfg.is_mistral_derived_model
         or "mistral" in cfg.base_model.lower().split("/")[-1]
-        or (cfg.model_type and "mistral" in cfg.model_type.lower())
+        or (cfg.type_of_model and "mistral" in cfg.type_of_model.lower())
     )
 
     cfg.is_qwen_derived_model = (
@@ -159,9 +164,6 @@ def normalize_config(cfg):
         ]
     ) or cfg.is_qwen_derived_model
 
-    if isinstance(cfg.learning_rate, str):
-        cfg.learning_rate = float(cfg.learning_rate)
-
     if isinstance(cfg.pretraining_dataset, dict):
         cfg.pretraining_dataset = [cfg.pretraining_dataset]
 
@@ -191,7 +193,21 @@ def normalize_cfg_datasets(cfg):
                     cfg.datasets[idx].conversation = "chatml"
 
 
-def validate_config(cfg):
+def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
+    if capabilities:
+        return DictDefault(
+            dict(
+                AxolotlConfigWCapabilities(
+                    **cfg.to_dict(), capabilities=capabilities
+                ).model_dump(exclude_unset=True)
+            )
+        )
+    return DictDefault(
+        dict(AxolotlInputConfig(**cfg.to_dict()).model_dump(exclude_unset=True))
+    )
+
+
+def legacy_validate_config(cfg):
     """
     This is a "pre-validation" step that handles the yaml configuration before we have any
     information about the model architecture
@@ -363,11 +379,11 @@ def validate_config(cfg):
             "hub_model_id is set without any models being saved. To save a model, set either save_steps or saves_per_epoch."
         )
 
-    if cfg.gptq and cfg.model_revision:
+    if cfg.gptq and cfg.revision_of_model:
         raise ValueError(
-            "model_revision is not supported for GPTQ models. "
+            "revision_of_model is not supported for GPTQ models. "
             + "Please download the model from HuggingFace Hub manually for correct branch, "
-            + "point to its path, and remove model_revision from the config."
+            + "point to its path, and remove revision_of_model from the config."
         )
 
     # if cfg.sample_packing and cfg.sdp_attention:
@@ -480,9 +496,6 @@ def validate_config(cfg):
     if cfg.rope_scaling:
         LOG.warning("`rope_scaling` should now be be a key under `model_config`")
 
-    if cfg.warmup_steps and cfg.warmup_ratio:
-        raise ValueError("warmup_steps and warmup_ratio are mutually exclusive")
-
     if cfg.wandb_run_id and not cfg.wandb_name:
         cfg.wandb_name = cfg.wandb_run_id
 

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -0,0 +1,991 @@
+"""
+Module for pydantic models for configuration
+"""
+
+import logging
+import os
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Union
+
+from pydantic import BaseModel, Field, conlist, field_validator, model_validator
+from transformers import SchedulerType
+from transformers.training_args import OptimizerNames
+
+from axolotl.utils.config.models.internals import GPUCapabilities
+
+LOG = logging.getLogger("axolotl.utils.config.models.input")
+
+
+class DeprecatedParameters(BaseModel):
+    """configurations that are deprecated"""
+
+    max_packed_sequence_len: Optional[int] = None
+    rope_scaling: Optional[Any] = None
+    noisy_embedding_alpha: Optional[float] = None
+
+    @field_validator("max_packed_sequence_len")
+    @classmethod
+    def validate_max_packed_sequence_len(cls, max_packed_sequence_len):
+        if max_packed_sequence_len:
+            raise DeprecationWarning("`max_packed_sequence_len` is no longer supported")
+        return max_packed_sequence_len
+
+    @field_validator("rope_scaling")
+    @classmethod
+    def validate_rope_scaling(cls, rope_scaling):
+        if rope_scaling:
+            raise DeprecationWarning(
+                "`rope_scaling` is no longer supported, it should now be be a key under `model_config`"
+            )
+        return rope_scaling
+
+    @field_validator("noisy_embedding_alpha")
+    @classmethod
+    def validate_noisy_embedding_alpha(cls, noisy_embedding_alpha):
+        if noisy_embedding_alpha:
+            LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
+        return noisy_embedding_alpha
+
+
+class RemappedParameters(BaseModel):
+    """parameters that have been remapped to other names"""
+
+    overrides_of_model_config: Optional[Dict[str, Any]] = Field(
+        default=None, alias="model_config"
+    )
+    type_of_model: Optional[str] = Field(default=None, alias="model_type")
+    revision_of_model: Optional[str] = Field(default=None, alias="model_revision")
+
+
+class PretrainingDataset(BaseModel):
+    """pretraining dataset configuration subset"""
+
+    path: Optional[str] = None
+
+
+class UserDefinedPrompterType(BaseModel):
+    """structure for user defined prompt types"""
+
+    system_prompt: Optional[str] = None
+    system_format: Optional[str] = None
+    field_system: Optional[str] = None
+    field_instruction: Optional[str] = None
+    field_input: Optional[str] = None
+    field_output: Optional[str] = None
+
+    format: Optional[str] = None
+    no_input_format: Optional[str] = None
+    field: Optional[str] = None
+
+
+class SFTDataset(BaseModel):
+    """SFT configuration subset"""
+
+    path: Optional[str] = None
+    split: Optional[str] = None
+    type: Optional[Union[str, UserDefinedPrompterType]] = None
+    shards: Optional[int] = None
+    conversation: Optional[str] = None
+    chat_template: Optional[str] = None
+    data_files: Optional[Union[str, List[str]]] = None
+    name: Optional[str] = None
+    ds_type: Optional[str] = None
+    train_on_split: Optional[str] = None
+
+    field_human: Optional[str] = None
+    field_model: Optional[str] = None
+
+
+class UserDefinedDPOType(BaseModel):
+    """User defined typing for DPO"""
+
+    field_system: Optional[str] = None
+    field_prompt: Optional[str] = None
+    field_chosen: Optional[str] = None
+    field_rejected: Optional[str] = None
+    prompt_format: Optional[str] = None
+    chosen_format: Optional[str] = None
+    rejected_format: Optional[str] = None
+
+
+class DPODataset(BaseModel):
+    """DPO configuration subset"""
+
+    path: Optional[str] = None
+    split: Optional[str] = None
+    type: Optional[Union[UserDefinedDPOType, str]] = None
+    data_files: Optional[List[str]] = None
+
+
+class RLType(str, Enum):
+    """RL trainer type configuration subset"""
+
+    dpo = "dpo"  # pylint: disable=invalid-name
+    ipo = "ipo"  # pylint: disable=invalid-name
+    kto_pair = "kto_pair"  # pylint: disable=invalid-name
+
+
+class ChatTemplate(str, Enum):
+    """Chat templates configuration subset"""
+
+    chatml = "chatml"  # pylint: disable=invalid-name
+    inst = "inst"  # pylint: disable=invalid-name
+
+
+class LoftQConfig(BaseModel):
+    """LoftQ configuration subset"""
+
+    loftq_bits: int = Field(default=4, metadata={"help": "Quantization bits for LoftQ"})
+    # loftq_iter: int = Field(default=1, metadata={"help": "Alternating iterations for LoftQ"})
+
+
+class PeftConfig(BaseModel):
+    """peftq configuration subset"""
+
+    loftq_config: Optional[LoftQConfig] = None
+
+
+class AutoType(str, Enum):
+    """auto type string configuration subset - used for bf16"""
+
+    AUTO = "auto"
+
+
+class SpecialTokensConfig(BaseModel):
+    """Special tokens configuration subset"""
+
+    bos_token: Optional[str] = None
+    eos_token: Optional[str] = None
+    pad_token: Optional[str] = None
+    unk_token: Optional[str] = None
+    additional_special_tokens: Optional[List[str]] = None
+
+
+class LoraConfig(BaseModel):
+    """Peft / LoRA configuration subset"""
+
+    load_in_8bit: Optional[bool] = Field(default=False)
+    load_in_4bit: Optional[bool] = Field(default=False)
+
+    adapter: Optional[str] = None
+    lora_model_dir: Optional[str] = None
+    lora_r: Optional[int] = None
+    lora_alpha: Optional[int] = None
+    lora_fan_in_fan_out: Optional[bool] = None
+    lora_target_modules: Optional[List[str]] = None
+    lora_target_linear: Optional[bool] = None
+    lora_modules_to_save: Optional[List[str]] = None
+    lora_dropout: Optional[float] = None
+    peft_layers_to_transform: Optional[List[int]] = None
+    peft: Optional[PeftConfig] = None
+    peft_use_dora: Optional[bool] = None
+
+    lora_on_cpu: Optional[bool] = None
+    gptq: Optional[bool] = None
+    bnb_config_kwargs: Optional[Dict[str, Any]] = None
+
+    loraplus_lr_ratio: Optional[float] = Field(
+        default=None,
+        metadata={
+            "help": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
+        },
+    )
+    loraplus_lr_embedding: Optional[float] = Field(
+        default=1e-6,
+        metadata={"help": "loraplus learning rate for lora embedding layers."},
+    )
+
+    merge_lora: Optional[bool] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_adapter(cls, data):
+        if not data.get("adapter") and (
+            data.get("load_in_8bit") or data.get("load_in_4bit")
+        ):
+            raise ValueError(
+                "load_in_8bit and load_in_4bit are not supported without setting an adapter."
+                "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
+            )
+        return data
+
+    @model_validator(mode="after")
+    def validate_qlora(self):
+        if self.adapter == "qlora":
+            if self.merge_lora:
+                # can't merge qlora if loaded in 8bit or 4bit
+                if self.load_in_8bit:
+                    raise ValueError("Can't merge qlora if loaded in 8bit")
+
+                if self.gptq:
+                    raise ValueError("Can't merge qlora if gptq")
+
+                if self.load_in_4bit:
+                    raise ValueError("Can't merge qlora if loaded in 4bit")
+
+            else:
+                if self.load_in_8bit:
+                    raise ValueError("Can't load qlora in 8bit")
+
+                if self.gptq:
+                    raise ValueError("Can't load qlora if gptq")
+
+                if not self.load_in_4bit:
+                    raise ValueError("Require cfg.load_in_4bit to be True for qlora")
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_quantized_dora(cls, data):
+        if data.get("peft_use_dora") and (
+            data.get("load_in_8bit") or data.get("load_in_4bit")
+        ):
+            raise ValueError(
+                "`peft_use_dora` is not currently compatible with quantized weights."
+            )
+        return data
+
+
+class ReLoRAConfig(BaseModel):
+    """ReLoRA configuration subset"""
+
+    relora_steps: Optional[int] = None
+    relora_warmup_steps: Optional[int] = None
+    relora_anneal_steps: Optional[int] = None
+    relora_prune_ratio: Optional[float] = None
+    relora_cpu_offload: Optional[bool] = None
+
+
+class ModelInputConfig(BaseModel):
+    """model to train on configuration subset"""
+
+    base_model: str
+    base_model_config: Optional[str] = None
+    tokenizer_config: Optional[str] = None
+    tokenizer_use_fast: Optional[bool] = None
+    tokenizer_legacy: Optional[bool] = None
+    tokenizer_type: Optional[str] = Field(
+        default=None, metadata={"help": "transformers tokenizer class"}
+    )
+    trust_remote_code: Optional[bool] = None
+
+    @field_validator("trust_remote_code")
+    @classmethod
+    def hint_trust_remote_code(cls, trust_remote_code):
+        if trust_remote_code:
+            LOG.warning(
+                "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
+            )
+        return trust_remote_code
+
+
+class HyperparametersConfig(BaseModel):
+    """training hyperparams configuration subset"""
+
+    gradient_accumulation_steps: Optional[int] = Field(default=1)
+    micro_batch_size: Optional[int] = Field(
+        default=1,
+        metadata={"help": "per gpu micro batch size for training"},
+    )
+    batch_size: Optional[int] = Field(
+        default=None,
+        metadata={
+            "help": "Total batch size, we do not recommended setting this manually"
+        },
+    )
+    eval_batch_size: Optional[int] = Field(
+        default=None,
+        metadata={
+            "help": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
+        },
+    )
+
+    train_on_inputs: Optional[bool] = None
+    group_by_length: Optional[bool] = None
+
+    learning_rate: Union[str, float]
+    weight_decay: Optional[float] = None
+    optimizer: Optional[Union[OptimizerNames, Literal["lion_pytorch"]]] = None
+    torchdistx_path: Optional[str] = None
+    lr_scheduler: Optional[SchedulerType] = None
+    lr_scheduler_kwargs: Optional[Dict[str, Any]] = None
+    lr_quadratic_warmup: Optional[bool] = None
+    cosine_min_lr_ratio: Optional[float] = None
+    cosine_constant_lr_ratio: Optional[float] = None
+    lr_div_factor: Optional[float] = None
+
+    adam_epsilon: Optional[float] = None
+    adam_beta1: Optional[float] = None
+    adam_beta2: Optional[float] = None
+    max_grad_norm: Optional[float] = None
+    num_epochs: int = Field(default=1)
+
+    @field_validator("batch_size")
+    @classmethod
+    def hint_batch_size_set(cls, batch_size):
+        if batch_size:
+            LOG.warning(
+                "%s\n%s",
+                "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
+                "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
+            )
+        return batch_size
+
+    @field_validator("learning_rate")
+    @classmethod
+    def convert_learning_rate(cls, learning_rate):
+        if learning_rate and isinstance(learning_rate, str):
+            learning_rate = float(learning_rate)
+        return learning_rate
+
+
+class ModelOutputConfig(BaseModel):
+    """model save configuration subset"""
+
+    output_dir: str = Field(default="./model-out")
+    hub_model_id: Optional[str] = None
+    hub_strategy: Optional[str] = None
+    save_safetensors: Optional[bool] = None
+
+
+class MLFlowConfig(BaseModel):
+    """mlflow configuration subset"""
+
+    use_mlflow: Optional[bool] = None
+    mlflow_tracking_uri: Optional[str] = None
+    mlflow_experiment_name: Optional[str] = None
+    hf_mlflow_log_artifacts: Optional[bool] = None
+
+
+class WandbConfig(BaseModel):
+    """wandb configuration subset"""
+
+    use_wandb: Optional[bool] = None
+    wandb_name: Optional[str] = None
+    wandb_run_id: Optional[str] = None
+    wandb_mode: Optional[str] = None
+    wandb_project: Optional[str] = None
+    wandb_entity: Optional[str] = None
+    wandb_watch: Optional[str] = None
+    wandb_log_model: Optional[str] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_wandb_run(cls, data):
+        if data.get("wandb_run_id") and not data.get("wandb_name"):
+            data["wandb_name"] = data.get("wandb_run_id")
+
+            LOG.warning(
+                "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
+            )
+
+        return data
+
+
+# pylint: disable=too-many-public-methods,too-many-ancestors
+class AxolotlInputConfig(
+    ModelInputConfig,
+    ModelOutputConfig,
+    LoraConfig,
+    ReLoRAConfig,
+    HyperparametersConfig,
+    WandbConfig,
+    MLFlowConfig,
+    RemappedParameters,
+    DeprecatedParameters,
+    BaseModel,
+):
+    """wrapper of all config options"""
+
+    class Config:
+        """Config for alias"""
+
+        populate_by_name = True
+
+    strict: Optional[bool] = Field(default=False)
+    resume_from_checkpoint: Optional[str] = None
+    auto_resume_from_checkpoints: Optional[bool] = None
+    resize_token_embeddings_to_32x: Optional[bool] = None
+
+    rl: Optional[RLType] = None
+
+    datasets: Optional[conlist(Union[SFTDataset, DPODataset], min_length=1)] = None  # type: ignore
+    test_datasets: Optional[conlist(Union[SFTDataset, DPODataset], min_length=1)] = None  # type: ignore
+    dataset_prepared_path: Optional[str] = None
+    dataset_shard_num: Optional[int] = None
+    dataset_shard_idx: Optional[int] = None
+
+    pretraining_dataset: Optional[  # type: ignore
+        conlist(Union[SFTDataset, PretrainingDataset], min_length=1)
+    ] = Field(
+        default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
+    )
+    dataset_processes: Optional[int] = Field(default=os.cpu_count())
+    dataset_keep_in_memory: Optional[bool] = None
+    dataloader_pin_memory: Optional[bool] = None
+    dataloader_num_workers: Optional[int] = None
+    dataloader_prefetch_factor: Optional[int] = None
+    dataloader_drop_last: Optional[bool] = None
+
+    push_dataset_to_hub: Optional[str] = None
+    hf_use_auth_token: Optional[bool] = None
+
+    device: Optional[Any] = None
+    device_map: Optional[Any] = None
+    world_size: Optional[int] = None
+    local_rank: Optional[int] = None
+    ddp: Optional[bool] = None
+
+    seed: Optional[int] = None
+    ddp_timeout: Optional[int] = None
+    ddp_bucket_cap_mb: Optional[int] = None
+    ddp_broadcast_buffers: Optional[bool] = None
+    ddp_find_unused_parameters: Optional[bool] = None
+
+    eval_table_size: Optional[int] = None
+    eval_max_new_tokens: Optional[int] = None
+    do_causal_lm_eval: Optional[bool] = None
+    eval_causal_lm_metrics: Optional[List[str]] = None
+    do_bench_eval: Optional[bool] = None
+    bench_dataset: Optional[str] = None
+    metric_for_best_model: Optional[str] = None
+    greater_is_better: Optional[bool] = None
+
+    loss_watchdog_threshold: Optional[float] = None
+    loss_watchdog_patience: Optional[int] = None
+
+    bf16: Optional[Union[AutoType, bool]] = AutoType.AUTO
+    fp16: Optional[bool] = None
+    bfloat16: Optional[bool] = None  # for non-AMP cases
+    float16: Optional[bool] = None  # for non-AMP cases
+    tf32: Optional[bool] = None
+    float32: Optional[bool] = None
+
+    # torch_dtype: Optional[torch.dtype]
+
+    gradient_checkpointing: Optional[bool] = Field(default=False)
+    gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
+
+    unfrozen_parameters: Optional[List[str]] = None
+
+    sequence_len: int = Field(default=1024)
+    sample_packing: Optional[bool] = None
+    eval_sample_packing: Optional[bool] = None
+    pad_to_sequence_len: Optional[bool] = None
+
+    xformers_attention: Optional[bool] = None
+    sdp_attention: Optional[bool] = None
+    s2_attention: Optional[bool] = None
+    flash_attention: Optional[bool] = None
+    flash_attn_cross_entropy: Optional[bool] = None
+    flash_attn_rms_norm: Optional[bool] = None
+    flash_attn_fuse_qkv: Optional[bool] = None
+    flash_attn_fuse_mlp: Optional[bool] = None
+    flash_optimum: Optional[bool] = None
+
+    deepspeed: Optional[Union[str, Dict[str, Any]]] = None
+    fsdp: Optional[List[str]] = None
+    fsdp_config: Optional[Dict[str, Any]] = None
+
+    val_set_size: Optional[float] = Field(default=0.0)
+
+    special_tokens: Optional[SpecialTokensConfig] = None
+    tokens: Optional[List[str]] = None
+
+    torch_compile: Optional[bool] = None
+    torch_compile_backend: Optional[str] = None
+
+    max_steps: Optional[int] = None
+    warmup_steps: Optional[int] = None
+    warmup_ratio: Optional[float] = None
+    eval_steps: Optional[Union[int, float]] = None
+    evals_per_epoch: Optional[Union[int]] = None
+    evaluation_strategy: Optional[str] = None
+    save_steps: Optional[Union[int, float]] = None
+    saves_per_epoch: Optional[int] = None
+    save_strategy: Optional[str] = None
+    save_total_limit: Optional[int] = None
+    logging_steps: Optional[int] = None
+    early_stopping_patience: Optional[int] = None
+    load_best_model_at_end: Optional[bool] = False
+
+    neftune_noise_alpha: Optional[float] = None
+
+    max_memory: Optional[Union[int, str]] = None
+    gpu_memory_limit: Optional[Union[int, str]] = None
+
+    chat_template: Optional[Union[Literal["chatml", "inst"], ChatTemplate]] = None
+    default_system_message: Optional[str] = None
+
+    # INTERNALS - document for now, generally not set externally
+    is_preprocess: Optional[bool] = None
+
+    total_num_tokens: Optional[int] = None
+    total_supervised_tokens: Optional[int] = None
+    sample_packing_eff_est: Optional[float] = None
+    axolotl_config_path: Optional[str] = None
+
+    is_falcon_derived_model: Optional[bool] = Field(default=False)
+    is_llama_derived_model: Optional[bool] = Field(default=False)
+    is_mistral_derived_model: Optional[bool] = Field(default=False)
+    is_qwen_derived_model: Optional[bool] = Field(default=False)
+
+    @field_validator("datasets", mode="before")
+    @classmethod
+    def fix_sharegpt_datasets(cls, datasets):
+        for idx, ds_cfg in enumerate(datasets):
+            if not ds_cfg["type"]:
+                continue
+            if ds_cfg["type"] == "sharegpt:chat":
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                datasets[idx]["type"] = "sharegpt"
+            if "sharegpt_simple" in ds_cfg["type"]:
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                datasets[idx]["type"] = datasets[idx]["type"].replace(
+                    "sharegpt_simple", "sharegpt"
+                )
+        return datasets
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_batch_size_fields(cls, data):
+        fields = ("micro_batch_size", "gradient_accumulation_steps", "batch_size")
+        non_empty_count = sum(1 for field in fields if data.get(field))
+
+        if non_empty_count < 2:
+            raise ValueError(f"At least two of {', '.join(fields)} must be set")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_pretraining_w_max_steps(cls, data):
+        if data.get("pretraining_dataset") and not data.get("max_steps"):
+            raise ValueError(
+                "max_steps must be set when using iterable pretraining_dataset, Trainer can't infer length and schedule optimizer/learning rate without it!"
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_pretraining_w_group_by_length(cls, data):
+        if data.get("pretraining_dataset") and data.get("group_by_length"):
+            LOG.warning(
+                "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_gptq_w_revision(cls, data):
+        if data.get("gptq") and data.get("revision_of_model"):
+            raise ValueError(
+                "revision_of_model is not supported for GPTQ models. "
+                + "Please download the model from HuggingFace Hub manually for correct branch, "
+                + "point to its path, and remove revision_of_model from the config."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_sample_packing_w_xformers(cls, data):
+        if data.get("sample_packing") and data.get("xformers_attention"):
+            raise ValueError(
+                "sample_packing not compatible with xformers_attention. Use flash_attention"
+            )
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_sample_packing_w_rl(cls, data):
+        if data.get("sample_packing") and data.get("rl"):
+            raise ValueError("`sample_packing: true` does not work with RLHF training")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def hint_sample_packing_padding(cls, data):
+        if data.get("sample_packing") and not data.get("pad_to_sequence_len"):
+            LOG.warning(
+                "`pad_to_sequence_len: true` is recommended when using sample_packing"
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_gas_bsz(cls, data):
+        if data.get("gradient_accumulation_steps") and data.get("batch_size"):
+            raise ValueError(
+                "please set only one of gradient_accumulation_steps or batch_size"
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def hint_eval_train_mbsz(cls, data):
+        if (
+            data.get("eval_batch_size")
+            and data.get("micro_batch_size")
+            and data.get("eval_batch_size") != data.get("micro_batch_size")
+        ):
+            LOG.warning(
+                "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_push_ds_auth(cls, data):
+        if (
+            data.get("push_dataset_to_hub")
+            and data.get("hf_use_auth_token") is not True
+        ):
+            raise ValueError(
+                "Require cfg.hf_use_auth_token to be True for push_dataset_to_hub"
+            )
+        return data
+
+    @model_validator(mode="after")
+    def check_falcon_fsdp(self):
+        if (self.base_model and "falcon" in self.base_model.lower()) and self.fsdp:
+            raise ValueError("FSDP is not supported for falcon models")
+        return self
+
+    @model_validator(mode="after")
+    def check_mpt_checkpointing(self):
+        if (
+            self.base_model and "mpt" in self.base_model.lower()
+        ) and self.gradient_checkpointing:
+            raise ValueError("gradient_checkpointing is not supported for MPT models")
+        return self
+
+    @model_validator(mode="after")
+    def check_better_transformers(self):
+        if self.flash_optimum is True:
+            if self.adapter:
+                LOG.warning(
+                    "BetterTransformers probably doesn't work with PEFT adapters"
+                )
+            if self.fp16 or self.bf16:
+                raise ValueError("AMP is not supported with BetterTransformer")
+            if self.float16 is not True and self.bfloat16 is not True:
+                LOG.warning(
+                    "You should probably set bfloat16 or float16 to true to "
+                    "load the model in float16 for BetterTransformers"
+                )
+        return self
+
+    @model_validator(mode="after")
+    def check_adamw_optimizer_params(self):
+        if any([self.adam_beta1, self.adam_beta2, self.adam_epsilon]) and (
+            not self.optimizer or "adamw" not in self.optimizer.value
+        ):
+            LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_saves(cls, data):
+        if (
+            data.get("save_strategy")
+            and data.get("save_steps")
+            and data.get("save_strategy") != "steps"
+        ):
+            raise ValueError(
+                "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
+            )
+        if data.get("saves_per_epoch") and data.get("save_steps"):
+            raise ValueError(
+                "save_steps and saves_per_epoch are mutually exclusive and cannot be used together."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_push_save(cls, data):
+        if data.get("hub_model_id") and not (
+            data.get("save_steps") or data.get("saves_per_epoch")
+        ):
+            LOG.warning(
+                "hub_model_id is set without any models being saved. To save a model, set either save_steps or saves_per_epoch."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_evals(cls, data):
+        if (
+            data.get("evaluation_strategy")
+            and data.get("eval_steps")
+            and data.get("evaluation_strategy") != "steps"
+        ):
+            raise ValueError(
+                "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
+            )
+
+        if (
+            data.get("val_set_size") == 0
+            and (data.get("eval_steps") or data.get("evaluation_strategy"))
+            and not data.get("test_datasets")
+        ):
+            raise ValueError(
+                "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
+            )
+        if data.get("evals_per_epoch") and data.get("eval_steps"):
+            raise ValueError(
+                "eval_steps and evals_per_epoch are mutually exclusive and cannot be used together."
+            )
+        if (
+            data.get("evals_per_epoch")
+            and data.get("evaluation_strategy")
+            and data.get("evaluation_strategy") != "steps"
+        ):
+            raise ValueError(
+                "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
+            )
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_eval_packing(cls, data):
+        if (
+            data.get("sample_packing")
+            and data.get("eval_table_size")
+            and data.get("eval_sample_packing") is not False
+        ):
+            raise ValueError(
+                "eval_table_size and eval_sample_packing are not supported together with sample_packing. Please set 'eval_sample_packing' to false."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_warmup(cls, data):
+        if data.get("warmup_steps") and data.get("warmup_ratio"):
+            raise ValueError("warmup_steps and warmup_ratio are mutually exclusive")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_neftune(cls, data):
+        if data.get("noisy_embedding_alpha") and not data.get("neftune_noise_alpha"):
+            data["neftune_noise_alpha"] = data["noisy_embedding_alpha"]
+            del data["noisy_embedding_alpha"]
+        elif data.get("noisy_embedding_alpha") and not data.get("neftune_noise_alpha"):
+            raise ValueError(
+                "noisy_embedding_alpha is deprecated, use neftune_noise_alpha; both are set, please remove the deprecated noisy_embedding_alpha setting"
+            )
+        return data
+
+    @field_validator("neftune_noise_alpha")
+    @classmethod
+    def validate_neftune_noise_alpha(cls, neftune_noise_alpha):
+        if neftune_noise_alpha is not None and neftune_noise_alpha <= 0.0:
+            raise ValueError("neftune_noise_alpha must be > 0.0")
+        return neftune_noise_alpha
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_frozen(cls, data):
+        if (
+            data.get("adapter")
+            and data.get("peft_layers_to_transform")
+            and data.get("unfrozen_parameters")
+        ):
+            raise ValueError(
+                "`unfrozen_parameters` used with `peft_layers_to_transform` can have unexpected behavior."
+            )
+
+        return data
+
+    @model_validator(mode="after")
+    def check_fft_possible_bad_config(self):
+        if (
+            # pylint: disable=too-many-boolean-expressions
+            not (self.bf16 or self.bfloat16)
+            and (self.fp16 or self.float16)
+            and not self.adapter
+            and not self.flash_attention
+            and self.sample_packing
+        ):
+            LOG.warning(
+                "Full fine tune w/o FA2 w/ sample packing and fp16/float16 is likely to raise errors. Try LoRA."
+            )
+            # ValueError: Attempting to unscale FP16 gradients.
+            # OR
+            # RuntimeError: expected mat1 and mat2 to have the same dtype, but got: float != c10::Half
+        return self
+
+    @model_validator(mode="after")
+    def check_fused_lora(self):
+        if self.adapter in ["lora", "qlora"] and (
+            self.flash_attn_fuse_qkv or self.flash_attn_fuse_mlp
+        ):
+            raise ValueError("Fused modules are not supported with LoRA/QLoRA")
+        return self
+
+    @model_validator(mode="after")
+    def hint_lora_8bit(self):
+        loftq = (
+            self.peft and self.peft.loftq_config and self.peft.loftq_config.loftq_bits
+        )
+        if not self.load_in_8bit and self.adapter == "lora" and not loftq:
+            LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
+        return self
+
+    @model_validator(mode="after")
+    def check_early_stopping(self):
+        if self.early_stopping_patience:
+            if not self.save_steps or not self.eval_steps:
+                raise ValueError(
+                    "`early_stopping_patience` requires save_steps and eval_steps to be set. eval_steps should evenly divide save_steps."
+                )
+            if self.save_steps % self.eval_steps != 0:
+                raise ValueError(
+                    "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
+                )
+        return self
+
+    @model_validator(mode="after")
+    def check_relora(self):
+        if self.relora_steps:
+            if self.adapter not in ("lora", "qlora"):
+                raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")
+
+            if self.fsdp:
+                raise ValueError("fsdp not supported with ReLoRA")
+
+            if self.deepspeed:
+                raise ValueError("deepspeed not supported with ReLoRA")
+
+            if self.lr_scheduler == "one_cycle":
+                raise ValueError(
+                    "ReLoRA is not compatible with the one_cycle scheduler"
+                )
+
+            if self.flash_attn_fuse_qkv or self.flash_attn_fuse_mlp:
+                raise ValueError("Fused modules are not supported with ReLoRA")
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_mem_mismatch(cls, data):
+        if (
+            data.get("max_memory") is not None
+            and data.get("gpu_memory_limit") is not None
+        ):
+            raise ValueError(
+                "max_memory and gpu_memory_limit are mutually exclusive and cannot be used together."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_use_reentrant_mismatch(cls, data):
+        if (
+            data.get("unfrozen_parameters")
+            and data.get("gradient_checkpointing_kwargs")
+            and data.get("gradient_checkpointing_kwargs", {}).get("use_reentrant")
+            is True
+        ):
+            # https://github.com/huggingface/transformers/issues/21381
+            raise ValueError(
+                "`use_reentrant` must be false when used with partially frozen model."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_val_w_test_datasets(cls, data):
+        if data.get("test_datasets") and data.get("val_set_size"):
+            raise ValueError(
+                "non-zero val_set_size should not be used with test_datasets configuration"
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_w_8bit_optimizer(cls, data):
+        if data.get("fsdp") and "bnb" in data.get("optimizer", ""):
+            raise ValueError(f"FSDP not compatible with {data.get('optimizer')}")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_causal_lm_evals(cls, data):
+        if data.get("do_causal_lm_eval") and data.get("eval_sample_packing"):
+            raise ValueError(
+                "do_causal_lm_eval is enabled, eval_sample_packing must be set to False"
+            )
+
+        if data.get("eval_causal_lm_metrics"):
+            supported_metrics = ["sacrebleu", "comet", "ter", "chrf"]
+            if not isinstance(data.get("eval_causal_lm_metrics"), list):
+                raise ValueError("eval_causal_lm_metrics must be a list")
+            # only ["sacrebleu", "comet", "ter", "chrf"] supported
+            if set(data.get("eval_causal_lm_metrics")) - set(supported_metrics):
+                raise ValueError(
+                    f"eval_causal_lm_metrics must be one of {supported_metrics}"
+                )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_dataset_or_pretraining_dataset(cls, data):
+        if data.get("datasets") is None and data.get("pretraining_dataset") is None:
+            raise ValueError("either datasets or pretraining_dataset is required")
+        return data
+
+
+class AxolotlConfigWCapabilities(AxolotlInputConfig):
+    """wrapper to valdiate gpu capabilities with the configured options"""
+
+    capabilities: GPUCapabilities
+
+    @model_validator(mode="after")
+    def check_bf16(self):
+        if self.capabilities.bf16:
+            if not self.bf16 and not self.bfloat16:
+                LOG.info(
+                    "bf16 support detected, but not enabled for this configuration."
+                )
+        else:
+            if (
+                not self.merge_lora
+                and not self.is_preprocess
+                and (self.bf16 is True or self.bfloat16 is True)
+            ):
+                raise ValueError(
+                    "bf16 requested, but AMP is not supported on this GPU. Requires Ampere series or above."
+                )
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_sample_packing_w_sdpa_bf16(cls, data):
+        is_sm_90: bool = (
+            data["capabilities"]
+            and data["capabilities"].get("compute_capability") == "sm_90"
+        )
+        if (
+            data.get("sample_packing")
+            and data.get("sdp_attention")
+            and (data.get("bfloat16") or data.get("bf16"))
+            and not is_sm_90
+        ):
+            # https://github.com/pytorch/pytorch/blob/1b03423526536b5f3d35bdfa95ccc6197556cf9b/test/test_transformers.py#L2440-L2450
+            LOG.warning(
+                "sample_packing & torch sdpa with bf16 is unsupported may results in 0.0 loss. "
+                "This may work on H100s."
+            )
+
+        return data

src/axolotl/utils/config/models/internals/__init__.py

@@ -0,0 +1,14 @@
+"""module for gpu capabilities"""
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class GPUCapabilities(BaseModel):
+    """model to manage the gpu capabilities statically"""
+
+    bf16: bool = Field(default=False)
+    fp8: bool = Field(default=False)
+    n_gpu: int = Field(default=1)
+    n_node: int = Field(default=1)
+    compute_capability: Optional[str] = Field(default=None)

src/axolotl/utils/data.py

@@ -114,7 +114,9 @@ def prepare_dataset(cfg, tokenizer):
         total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
         if total_eval_steps == 0:
             raise ValueError(
-                "eval dataset split is too small for sample_packing. You should set `eval_sample_packing: False`. "
+                "eval dataset split is too small for sample_packing. "
+                "You should set `eval_sample_packing: False` "
+                "or decrease the value of `eval_batch_size`. "
             )
 
     if cfg.max_steps:
@@ -937,7 +939,9 @@ def load_prepare_dpo_datasets(cfg):
         for i, data_set in enumerate(split_datasets):
             _type = dataset_cfgs[i]["type"]
             if _type:
-                ds_transform_fn = load_dpo(_type, _cfg)
+                if isinstance(_type, DictDefault):
+                    _type = "user_defined.default"
+                ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
                 split_datasets[i] = data_set.map(
                     ds_transform_fn,
                     desc="Mapping RL Dataset",

src/axolotl/utils/dict.py

@@ -12,4 +12,4 @@ class DictDefault(Dict):
         return None
 
     def __or__(self, other):
-        return DictDefault(super().__or__(other))
+        return DictDefault(super().__ror__(other))

src/axolotl/utils/mlflow_.py

@@ -7,7 +7,7 @@ from axolotl.utils.dict import DictDefault
 
 def setup_mlflow_env_vars(cfg: DictDefault):
     for key in cfg.keys():
-        if key.startswith("mlflow_"):
+        if key.startswith("mlflow_") or key.startswith("hf_mlflow_"):
             value = cfg.get(key, "")
 
             if value and isinstance(value, str) and len(value) > 0:

src/axolotl/utils/models.py

@@ -86,8 +86,8 @@ def load_model_config(cfg):
         model_config_name = cfg.tokenizer_config
     trust_remote_code = cfg.trust_remote_code is True
     config_kwargs = {}
-    if cfg.model_revision:
-        config_kwargs["revision"] = cfg.model_revision
+    if cfg.revision_of_model:
+        config_kwargs["revision"] = cfg.revision_of_model
 
     try:
         model_config = AutoConfig.from_pretrained(
@@ -104,8 +104,8 @@ def load_model_config(cfg):
             )
         raise err
 
-    if cfg.model_config:
-        for key, val in cfg.model_config.items():
+    if cfg.overrides_of_model_config:
+        for key, val in cfg.overrides_of_model_config.items():
             setattr(model_config, key, val)
 
     check_model_config(cfg, model_config)
@@ -272,7 +272,7 @@ def load_model(
     Load a model for a given configuration and tokenizer.
     """
     base_model = cfg.base_model
-    model_type = cfg.model_type
+    model_type = cfg.type_of_model
     model_config = load_model_config(cfg)
 
     # TODO refactor as a kwarg
@@ -426,8 +426,8 @@ def load_model(
     if is_deepspeed_zero3_enabled():
         del model_kwargs["device_map"]
 
-    if cfg.model_revision:
-        model_kwargs["revision"] = cfg.model_revision
+    if cfg.revision_of_model:
+        model_kwargs["revision"] = cfg.revision_of_model
     if cfg.gptq:
         if not hasattr(model_config, "quantization_config"):
             LOG.warning("model config does not contain quantization_config information")
@@ -512,11 +512,12 @@ def load_model(
 
             if cfg.flash_attention and not inference:
                 from axolotl.monkeypatch.llama_attn_hijack_flash import (
+                    is_xformers_swiglu_available,
                     replace_llama_mlp_with_swiglu,
                     replace_llama_qkv_with_fused,
                 )
 
-                if cfg.flash_attn_fuse_mlp:
+                if cfg.flash_attn_fuse_mlp and is_xformers_swiglu_available():
                     LOG.info("patching with SwiGLU")
                     replace_llama_mlp_with_swiglu(model)
 
@@ -829,6 +830,8 @@ def load_lora(model, cfg, inference=False, config_only=False):
     if loftq_bits:
         lora_config_kwargs["loftq_config"] = LoftQConfig(loftq_bits=loftq_bits)
         lora_config_kwargs["init_lora_weights"] = "loftq"
+    if cfg.peft_use_dora:
+        lora_config_kwargs["use_dora"] = cfg.peft_use_dora
 
     lora_config = LoraConfig(
         r=cfg.lora_r,

src/axolotl/utils/samplers/multipack.py

@@ -5,7 +5,7 @@ Multipack Batch Sampler
 import logging
 import math
 import os
-from typing import Any, Iterable, List, Union
+from typing import Any, Iterable, List, Union, Optional
 
 import numba
 import numpy as np
@@ -115,12 +115,14 @@ class MultipackBatchSampler(BatchSampler):
         batch_max_len: int,
         lengths: np.ndarray,
         packing_efficiency_estimate: float = 1.0,
+        consistent_length: Optional[bool] = False,
     ):
         super().__init__(sampler, batch_size, drop_last)
         self.batch_size = batch_size
         self.batch_max_len = batch_max_len
         self.lengths: np.ndarray = lengths
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
+        self.consistent_length = consistent_length
 
         assert isinstance(self.lengths, np.ndarray)
 
@@ -164,11 +166,18 @@ class MultipackBatchSampler(BatchSampler):
 
     def __iter__(self):
         batches = self.generate_batches(set_stats=True)
-        return iter(batches)
+        if self.consistent_length:
+            length = self._len_est()
+            return iter(batches[:length])
+        else:
+            return iter(batches)
 
     def num_batches(self):
         batches = self.generate_batches(set_stats=True)
-        return len(batches)
+        if self.consistent_length:
+            return self._len_est()
+        else:
+            return len(batches)
 
     def efficiency(self):
         return self.eff_total_used / self.eff_total_slots

src/axolotl/utils/trainer.py

@@ -255,7 +255,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 train_dataset.remove_columns(["length"]),
                 batch_sampler=sampler,
             )
-            data_loader_len = len(data_loader) // batch_size
+            data_loader_len = len(data_loader) // cfg.batch_size
             actual_eff = sampler.efficiency()
             LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True)
             # FIXME: is there a bug here somewhere? the total num steps depends
@@ -277,7 +277,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 calc_sample_packing_eff_est,
             )
             sample_packing_eff_est = (
-                math.ceil(sample_packing_actual_eff_all * 100.0) / 100.0
+                math.ceil(sample_packing_actual_eff_all * 10000.0) / 10000.0
             )
             if update:
                 cfg.sample_packing_eff_est = sample_packing_eff_est

tests/e2e/patched/test_fused_llama.py

@@ -57,9 +57,9 @@ class TestFusedLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 10,
+                "save_steps": 5,
+                "eval_steps": 5,
             }
         )
         if is_torch_bf16_gpu_available():

tests/e2e/patched/test_lora_llama_multipack.py

@@ -43,7 +43,7 @@ class TestLoraLlama(unittest.TestCase):
                 "lora_alpha": 64,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.1,
+                "val_set_size": 0.2,
                 "special_tokens": {
                     "unk_token": "<unk>",
                     "bos_token": "<s>",

tests/e2e/test_dpo.py

@@ -7,6 +7,8 @@ import os
 import unittest
 from pathlib import Path
 
+import pytest
+
 from axolotl.cli import load_rl_datasets
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
@@ -19,6 +21,7 @@ LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
+@pytest.mark.skip(reason="doesn't seem to work on modal")
 class TestDPOLlamaLora(unittest.TestCase):
     """
     Test case for DPO Llama models using LoRA

tests/e2e/test_phi.py

@@ -7,6 +7,8 @@ import os
 import unittest
 from pathlib import Path
 
+import pytest
+
 from axolotl.cli import load_datasets
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
@@ -19,6 +21,7 @@ LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
+@pytest.mark.skip(reason="doesn't seem to work on modal")
 class TestPhi(unittest.TestCase):
     """
     Test case for Phi2 models

tests/prompt_strategies/test_raw_io.py

@@ -0,0 +1,116 @@
+"""
+Test module for raw i/o data for prompts
+"""
+import pytest
+from datasets import Dataset
+from tokenizers import AddedToken
+from transformers import AutoTokenizer
+
+from axolotl.datasets import TokenizedPromptDataset
+from axolotl.prompt_strategies.input_output import (
+    RawInputOutputPrompter,
+    RawInputOutputStrategy,
+)
+
+
+@pytest.fixture(name="segments_dataset")
+def fixture_sharegpt_dataset():
+    return Dataset.from_list(
+        [
+            {
+                "segments": [
+                    {
+                        "label": False,
+                        "text": "<s>hello ",
+                    },
+                    {
+                        "label": True,
+                        "text": "hi there.<eot>",
+                    },
+                    {
+                        "label": False,
+                        "text": "goodbye ",
+                    },
+                    {
+                        "label": True,
+                        "text": "farewell<eot>",
+                    },
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="tokenizer")
+def fixture_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+    tokenizer.add_tokens(
+        [
+            AddedToken("<eot>", rstrip=False, lstrip=False, normalized=False),
+        ]
+    )
+
+    return tokenizer
+
+
+class TestRawInputOutputPrompts:
+    """
+    Test class for raw i/o prompter
+    """
+
+    def test_segment_prompts(self, segments_dataset, tokenizer):
+        strategy = RawInputOutputStrategy(
+            RawInputOutputPrompter(),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, segments_dataset, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+        labels = dataset_wrapper[0]["labels"]
+
+        assert (
+            tokenizer.decode(input_ids)
+            == "<s> hello  hi there.<eot> goodbye  farewell<eot>"
+        )
+        # fmt: off
+        assert input_ids == [
+            1,  # <s>
+            6312,  # hell
+            28709,  # o
+            28705,  #
+            12014,  # hi
+            736,  # there
+            28723,  # .
+            32000,  # <eot>
+            1179,  # good
+            17664,  # bye
+            28705,  #
+            19111,  # fare
+            5458,  # well
+            32000,  # <eot>
+        ]
+        # fmt: on
+
+        # fmt: off
+        assert labels == [
+            -100,  # <s>
+            -100,  # hell
+            -100,  # o
+            -100,  #
+            12014,  # hi
+            736,  # there
+            28723,  # .
+            32000,  # <eot>
+            -100,  # good
+            -100,  # bye
+            -100,  #
+            19111,  # fare
+            5458,  # well
+            32000,  # <eot>
+        ]
+        # fmt: on

tests/test_dict.py

@@ -39,7 +39,9 @@ class DictDefaultTest(unittest.TestCase):
         ), "DictDefault should support in operator for existing keys in list"
 
     def test_dict_or_operator(self):
-        cfg = DictDefault(
+        cfg = DictDefault({"key_a": {"key_b": "value_b"}, "key_f": "value_g"})
+
+        cfg = cfg | DictDefault(  # pylint: disable=unsupported-binary-operation
             {
                 "key_a": {"key_b": "value_a"},
                 "key_c": "value_c",
@@ -48,10 +50,6 @@ class DictDefaultTest(unittest.TestCase):
             }
         )
 
-        cfg = cfg | DictDefault(  # pylint: disable=unsupported-binary-operation
-            {"key_a": {"key_b": "value_b"}, "key_f": "value_g"}
-        )
-
         assert (
             cfg.key_a.key_b == "value_b"
         ), "DictDefault should support OR operator for existing nested keys"

tests/test_normalize_config.py

@@ -25,20 +25,6 @@ class NormalizeConfigTestCase(unittest.TestCase):
             }
         )
 
-    def test_lr_as_float(self):
-        cfg = (
-            self._get_base_cfg()
-            | DictDefault(  # pylint: disable=unsupported-binary-operation
-                {
-                    "learning_rate": "5e-5",
-                }
-            )
-        )
-
-        normalize_config(cfg)
-
-        assert cfg.learning_rate == 0.00005
-
     def test_base_model_config_set_when_empty(self):
         cfg = self._get_base_cfg()
         del cfg.base_model_config

tests/test_prompt_tokenizers.py

@@ -204,13 +204,13 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
         # fmt: off
         # System message, multi-turn conversations
         mt_ids = tokenize(test_data['multi_turn_sys'])
-        assert decode(mt_ids) == '<s> [INST] lorem\nabc [/INST] ipsum</s> [INST] 123 [/INST] sit</s>'
-        assert mt_ids == [1, 518, 25580, 29962, 301, 3668, 13, 10736, 518, 29914, 25580, 29962, 23421, 2, 518, 25580, 29962, 29871, 29896, 29906, 29941, 518, 29914, 25580, 29962, 7845, 2]
+        assert decode(mt_ids) == '<s> [INST]  lorem\nabc [/INST] ipsum</s> [INST] 123 [/INST] sit</s>'
+        assert mt_ids == [1, 518, 25580, 29962, 29871, 301, 3668, 13, 10736, 518, 29914, 25580, 29962, 23421, 2, 518, 25580, 29962, 29871, 29896, 29906, 29941, 518, 29914, 25580, 29962, 7845, 2]
 
         # System message, single-turn conversations
         st_ids = tokenize(test_data['single_turn_sys'])
-        assert decode(st_ids) == '<s> [INST] lorem\nabc [/INST] ipsum</s>'
-        assert st_ids == [1, 518, 25580, 29962, 301, 3668, 13, 10736, 518, 29914, 25580, 29962, 23421, 2]
+        assert decode(st_ids) == '<s> [INST]  lorem\nabc [/INST] ipsum</s>'
+        assert st_ids == [1, 518, 25580, 29962, 29871, 301, 3668, 13, 10736, 518, 29914, 25580, 29962, 23421, 2]
 
         # No system message, single-turn
         ns_ids = tokenize(test_data['single_turn_no_sys'])