feat(doc): add llama4 to liger support

* llama4 support for linearized experts

* clean up fsdp2 sharding to prevent hang

* add yaml config

* cleanup example [skip ci]

.github/workflows/base.yml

@@ -52,6 +52,12 @@ jobs:
             python_version: "3.11"
             pytorch: nightly
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: next
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -73,7 +79,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || './docker/Dockerfile-base' }}
+          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || matrix.pytorch == 'next' && './docker/Dockerfile-base-next' || './docker/Dockerfile-base' }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/multi-gpu-e2e.yml

@@ -24,6 +24,13 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            axolotl_extras: vllm
+            num_gpus: 2
+            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -38,13 +45,6 @@ jobs:
             axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras: vllm
-            num_gpus: 2
-            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:

.github/workflows/tests.yml

@@ -211,7 +211,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.6.0
             num_gpus: 1
             axolotl_extras: vllm
     steps:
@@ -258,7 +258,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.5.1
             num_gpus: 1
             axolotl_extras: vllm
     steps:

_quarto.yml

@@ -231,6 +231,7 @@ website:
             - docs/reward_modelling.qmd
             - docs/lr_groups.qmd
             - docs/lora_optims.qmd
+            - docs/dataset_loading.qmd
 
         - section: "Core Concepts"
           contents:

cicd/multigpu.sh

@@ -2,5 +2,5 @@
 set -e
 
 # only run one test at a time so as not to OOM the GPU
-pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
-pytest -v -n1 /workspace/axolotl/tests/e2e/multigpu/solo/
+pytest -v  --durations=10 -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
+pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/

docker/Dockerfile

@@ -20,9 +20,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile-base

@@ -29,7 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
 

docker/Dockerfile-base-next

@@ -0,0 +1,38 @@
+ARG CUDA_VERSION="12.8.1"
+ARG CUDNN_VERSION="8"
+ARG UBUNTU_VERSION="22.04"
+ARG MAX_JOBS=4
+
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+
+ENV PATH="/root/miniconda3/bin:${PATH}"
+
+ARG PYTHON_VERSION="3.11"
+ARG PYTORCH_VERSION="next"
+ARG CUDA="128"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+
+ENV PYTHON_VERSION=$PYTHON_VERSION
+ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
+
+RUN apt-get update \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
+    && wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && mkdir /root/.conda \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh \
+    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+
+ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+
+WORKDIR /workspace
+
+RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
+    python3 -m pip install --no-cache-dir -U torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
+    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
+    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
+
+RUN git lfs install --skip-repo && \
+    pip3 install awscli && \
+    pip3 install -U --no-cache-dir pydantic==2.10.6

docs/config.qmd

@@ -109,7 +109,7 @@ datasets:
     preprocess_shards: # Optional[int] process dataset in N sequential chunks for memory efficiency (exclusive with `shards`)
 
     name: # Optional[str] name of dataset configuration to load
-    train_on_split: train # Optional[str] name of dataset split to load from
+    split: train # Optional[str] name of dataset split to load from
     revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
     trust_remote_code: # Optional[bool] Trust remote code for untrusted source
 
@@ -165,7 +165,9 @@ datasets:
       content: value
       # ...
 
-    # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
+    # Optional[Dict[str, List]]. Roles mapping in the messages.
+    # The format is {target_role: [source_roles]}. All source roles will be mapped to the target role.
+    # The default is:
     roles:
       user: ["human", "user"]
       assistant: ["gpt", "assistant"]
@@ -510,7 +512,8 @@ train_on_inputs: false
 # Note that training loss may have an oscillating pattern with this enabled.
 group_by_length: false
 
-# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
+# Whether to use gradient checkpointing. Available options are: true, false, "offload".
+# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
 # additional kwargs to pass to the trainer for gradient checkpointing
 # gradient_checkpointing_kwargs:
@@ -686,10 +689,9 @@ ddp_broadcast_buffers:
 # E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
 # subsequences, or set to 4 to split into four equal-sized subsequences.
 # See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
-sequence_parallel_degree: 4  # Set to the number of GPUs to split sequences across
-flash_attention: true  # SP requires flash attention
-micro_batch_size: 1  # SP requires this is set to 1
-# (optional) strides across the key dimension; larger values use more memory but should make training a bit faster
+sequence_parallel_degree:
+# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
+# Must evenly divide the number of KV heads in your model.
 heads_k_stride: 1
 
 # Path to torch distx for optim 'adamw_anyprecision'

docs/dataset-formats/index.qmd

@@ -13,6 +13,13 @@ As there are a lot of available options in Axolotl, this guide aims to provide a
 
 Axolotl supports 3 kinds of training methods: pre-training, supervised fine-tuning, and preference-based post-training (e.g. DPO, ORPO, PRMs). Each method has their own dataset format which are described below.
 
+::: {.callout-tip}
+
+This guide will mainly use JSONL as an introduction. Please refer to the [dataset loading docs](../dataset_loading.qmd) to understand how to load datasets from other sources.
+
+For `pretraining_dataset:` specifically, please refer to the [Pre-training section](#pre-training).
+:::
+
 ## Pre-training
 
 When aiming to train on large corpora of text datasets, pre-training is your go-to choice. Due to the size of these datasets, downloading the entire-datasets before beginning training would be prohibitively time-consuming. Axolotl supports [streaming](https://huggingface.co/docs/datasets/en/stream) to only load batches into memory at a time.

docs/dataset_loading.qmd

@@ -0,0 +1,276 @@
+---
+title: Dataset Loading
+description: Understanding how to load datasets from different sources
+back-to-top-navigation: true
+toc: true
+toc-depth: 5
+---
+
+## Overview
+
+Datasets can be loaded in a number of different ways depending on the how it is saved (the extension of the file) and where it is stored.
+
+## Loading Datasets
+
+We use the `datasets` library to load datasets and a mix of `load_dataset` and `load_from_disk` to load them.
+
+You may recognize the similar named configs between `load_dataset` and the `datasets` section of the config file.
+
+```yaml
+datasets:
+  - path:
+    name:
+    data_files:
+    split:
+    revision:
+    trust_remote_code:
+```
+
+::: {.callout-tip}
+
+Do not feel overwhelmed by the number of options here. A lot of them are optional. In fact, the most common config to use would be `path` and sometimes `data_files`.
+
+:::
+
+This matches the API of [`datasets.load_dataset`](https://github.com/huggingface/datasets/blob/0b5998ac62f08e358f8dcc17ec6e2f2a5e9450b6/src/datasets/load.py#L1838-L1858), so if you're familiar with that, you will feel right at home.
+
+For HuggingFace's guide to load different dataset types, see [here](https://huggingface.co/docs/datasets/loading).
+
+For full details on the config, see [config.qmd](config.qmd).
+
+::: {.callout-note}
+
+You can set multiple datasets in the config file by more than one entry under `datasets`.
+
+```yaml
+datasets:
+  - path: /path/to/your/dataset
+  - path: /path/to/your/other/dataset
+```
+
+:::
+
+### Local dataset
+
+#### Files
+
+Usually, to load a JSON file, you would do something like this:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("json", data_files="data.json")
+```
+
+Which translates to the following config:
+
+```yaml
+datasets:
+  - path: json
+    data_files: /path/to/your/file.jsonl
+```
+
+However, to make things easier, we have added a few shortcuts for loading local dataset files.
+
+You can just point the `path` to the file or directory along with the `ds_type` to load the dataset. The below example shows for a JSON file:
+
+```yaml
+datasets:
+  - path: /path/to/your/file.jsonl
+    ds_type: json
+```
+
+This works for CSV, JSON, Parquet, and Arrow files.
+
+::: {.callout-tip}
+
+If `path` points to a file and `ds_type` is not specified, we will automatically infer the dataset type from the file extension, so you could omit `ds_type` if you'd like.
+
+:::
+
+#### Directory
+
+If you're loading a directory, you can point the `path` to the directory.
+
+Then, you have two options:
+
+##### Loading entire directory
+
+You do not need any additional configs.
+
+We will attempt to load in the following order:
+- datasets saved with `datasets.save_to_disk`
+- loading entire directory of files (such as with parquet/arrow files)
+
+```yaml
+datasets:
+  - path: /path/to/your/directory
+```
+
+##### Loading specific files in directory
+
+Provide `data_files` with a list of files to load.
+
+```yaml
+datasets:
+    # single file
+  - path: /path/to/your/directory
+    ds_type: csv
+    data_files: file1.csv
+
+    # multiple files
+  - path: /path/to/your/directory
+    ds_type: json
+    data_files:
+      - file1.jsonl
+      - file2.jsonl
+
+    # multiple files for parquet
+  - path: /path/to/your/directory
+    ds_type: parquet
+    data_files:
+      - file1.parquet
+      - file2.parquet
+
+```
+
+### HuggingFace Hub
+
+The method you use to load the dataset depends on how the dataset was created, whether a folder was uploaded directly or a HuggingFace Dataset was pushed.
+
+::: {.callout-note}
+
+If you're using a private dataset, you will need to enable the `hf_use_auth_token` flag in the root-level of the config file.
+
+:::
+
+#### Folder uploaded
+
+This would mean that the dataset is a single file or file(s) uploaded to the Hub.
+
+```yaml
+datasets:
+  - path: org/dataset-name
+    data_files:
+      - file1.jsonl
+      - file2.jsonl
+```
+
+#### HuggingFace Dataset
+
+This means that the dataset is created as a HuggingFace Dataset and pushed to the Hub via `datasets.push_to_hub`.
+
+```yaml
+datasets:
+  - path: org/dataset-name
+```
+
+::: {.callout-note}
+
+There are some other configs which may be required like `name`, `split`, `revision`, `trust_remote_code`, etc depending on the dataset.
+
+:::
+
+### Remote Filesystems
+
+Via the `storage_options` config under `load_dataset`, you can load datasets from remote filesystems like S3, GCS, Azure, and OCI.
+
+::: {.callout-warning}
+
+This is currently experimental. Please let us know if you run into any issues!
+
+:::
+
+The only difference between the providers is that you need to prepend the path with the respective protocols.
+
+```yaml
+datasets:
+    # Single file
+  - path: s3://bucket-name/path/to/your/file.jsonl
+
+    # Directory
+  - path: s3://bucket-name/path/to/your/directory
+```
+
+For directory, we load via `load_from_disk`.
+
+#### S3
+
+Prepend the path with `s3://`.
+
+The credentials are pulled in the following order:
+
+- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables
+- from the `~/.aws/credentials` file
+- for nodes on EC2, the IAM metadata provider
+
+::: {.callout-note}
+
+We assume you have credentials setup and not using anonymous access. If you want to use anonymous access, let us know! We may have to open a config option for this.
+
+:::
+
+Other environment variables that can be set can be found in [boto3 docs](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html#using-environment-variables)
+
+#### GCS
+
+Prepend the path with `gs://` or `gcs://`.
+
+The credentials are loaded in the following order:
+
+- gcloud credentials
+- for nodes on GCP, the google metadata service
+- anonymous access
+
+#### Azure
+
+##### Gen 1
+
+Prepend the path with `adl://`.
+
+Ensure you have the following environment variables set:
+
+- `AZURE_STORAGE_TENANT_ID`
+- `AZURE_STORAGE_CLIENT_ID`
+- `AZURE_STORAGE_CLIENT_SECRET`
+
+##### Gen 2
+
+Prepend the path with `abfs://` or `az://`.
+
+Ensure you have the following environment variables set:
+
+- `AZURE_STORAGE_ACCOUNT_NAME`
+- `AZURE_STORAGE_ACCOUNT_KEY`
+
+Other environment variables that can be set can be found in [adlfs docs](https://github.com/fsspec/adlfs?tab=readme-ov-file#setting-credentials)
+
+#### OCI
+
+Prepend the path with `oci://`.
+
+It would attempt to read in the following order:
+
+- `OCIFS_IAM_TYPE`, `OCIFS_CONFIG_LOCATION`, and `OCIFS_CONFIG_PROFILE` environment variables
+- when on OCI resource, resource principal
+
+Other environment variables:
+
+- `OCI_REGION_METADATA`
+
+Please see the [ocifs docs](https://ocifs.readthedocs.io/en/latest/getting-connected.html#Using-Environment-Variables).
+
+### HTTPS
+
+The path should start with `https://`.
+
+```yaml
+datasets:
+  - path: https://path/to/your/dataset/file.jsonl
+```
+
+This must be publically accessible.
+
+## Next steps
+
+Now that you know how to load datasets, you can learn more on how to load your specific dataset format into your target output format [dataset formats docs](dataset-formats).

docs/lora_optims.qmd

@@ -17,6 +17,7 @@ We currently support several common model architectures, including (but not limi
 - `qwen2`
 - `gemma`
 - `gemma2`
+- `gemma3`
 
 <details>
 

docs/multimodal.qmd

@@ -9,6 +9,7 @@ format:
 ## Supported Models
 
 - [Mllama](#sec-mllama)
+- [Llama4](#sec-llama4)
 - [Pixtral](#sec-pixtral)
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
@@ -63,6 +64,14 @@ base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
 chat_template: llama3_2_vision
 ```
 
+### Llama4 {#sec-llama4}
+
+```yaml
+base_model: meta-llama/Llama-4-Scout-17B-16E-Instruct
+
+chat_template: llama4
+```
+
 ### Pixtral {#sec-pixtral}
 
 ```yaml
@@ -155,7 +164,7 @@ Here is an example of a multi-modal dataset:
         {
             "role": "user",
             "content": [
-                {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+                {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
                 {"type": "text", "text": "Describe this image in detail."}
             ]
         },

docs/sequence_parallelism.qmd

@@ -23,10 +23,9 @@ Use sequence parallelism when:
 To enable sequence parallelism, add the following to your configuration file:
 
 ```yaml
-sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
-flash_attention: true  # SP requires flash attention
-micro_batch_size: 1  # SP requires this is set to 1
-# (optional) strides across the key dimension; larger values use more memory but should make training a bit faster
+# Set to a divisor (> 1) of the number of GPUs available
+sequence_parallel_degree: 4  # Split sequences across 4 GPUs
+# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
 ```
 
@@ -67,16 +66,15 @@ sequence_len: 8192
 ...
 
 sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
-flash_attention: true  # SP requires flash attention
-micro_batch_size: 1  # SP requires this is set to 1
-# (optional) strides across the key dimension; larger values use more memory but should make training a bit faster
+flash_attention: true  # Required with sequence parallelism
+# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
 
 ...
 ```
 
-This will train the Llama 3 8B model with 8192 context length, with each sequence split
-into 4 subsequences of length 2048 across 4 GPUs.
+This will train the Llama 3 8B model with 8K context length, with each sequence split
+into 2 subsequences of length 4096 across 2 GPUs.
 
 ## Sample Packing with Sequence Parallelism
 
@@ -88,14 +86,12 @@ Sequence parallelism is compatible with Axolotl's sample packing functionality.
 
 ## Effect on Batch Size
 
-First, note that sequence parallelism supports only the case where `micro_batch_size: 1`.
-
 When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
 
 - Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
 - The number of batches processed per step decreases
 
 For example:
-- With 8 GPUs and no sequence parallelism: 8 different batches are processed per step
+- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
 - With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
-- If your per-GPU `micro_batch_size` is 1, the global batch size decreases from 8 to 2
+- If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4

examples/cerebras/btlm-ft.yml

@@ -8,9 +8,6 @@ tokenizer_type: GPT2Tokenizer
 trust_remote_code: true
 tokenizer_use_fast: true
 tokenizer_legacy: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 push_dataset_to_hub:
 hf_use_auth_token: true
@@ -34,7 +31,6 @@ lora_alpha:
 lora_dropout:
 lora_target_modules:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -58,16 +54,12 @@ learning_rate: 0.000085
 train_on_inputs: true
 group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
 
-xformers_attention:
 flash_attention: true
 sdp_attention:
 flash_optimum:
@@ -80,8 +72,6 @@ evals_per_epoch: 4
 saves_per_epoch: 1
 save_total_limit:
 
-debug:
-deepspeed:
 weight_decay: 0.1
 special_tokens:
   pad_token: "<|endoftext|>"

examples/cerebras/qlora.yml

@@ -22,7 +22,6 @@ lora_target_modules:
   - c_attn
   - c_proj
 lora_target_linear:
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -36,15 +35,10 @@ optimizer: paged_adamw_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
 xformers_attention: true
 flash_attention:
@@ -53,10 +47,6 @@ gptq_model_v1:
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|endoftext|>"

examples/code-llama/13b/lora.yml

@@ -26,7 +26,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -41,29 +40,18 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/code-llama/13b/qlora.yml

@@ -26,9 +26,7 @@ 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:
@@ -43,28 +41,18 @@ optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/code-llama/34b/lora.yml

@@ -26,7 +26,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -41,29 +40,18 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/code-llama/34b/qlora.yml

@@ -26,9 +26,7 @@ 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:
@@ -43,28 +41,18 @@ optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/code-llama/7b/lora.yml

@@ -26,7 +26,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -41,29 +40,18 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/code-llama/7b/qlora.yml

@@ -26,9 +26,7 @@ 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:
@@ -43,28 +41,18 @@ optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/cohere/command-r-7b-qlora.yml

@@ -44,28 +44,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/dbrx/16bit-lora.yaml

@@ -3,9 +3,6 @@ base_model: LnL-AI/dbrx-base-converted-v2
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -48,26 +45,20 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: false  # don't use with fsdp_activation_checkpointing
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
-debug:
+
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/dbrx/8bit-lora.yaml

@@ -48,26 +48,20 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: false  # don't use with fsdp_activation_checkpointing
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
-debug:
+
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/dbrx/fft-ds-zero3.yaml

@@ -3,9 +3,6 @@ base_model: LnL-AI/dbrx-base-converted-v2
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -35,25 +32,19 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
-debug:
+
 weight_decay: 0.0
 deepspeed: deepspeed_configs/zero3_bf16.json

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -2,9 +2,6 @@ base_model: deepseek-ai/DeepSeek-V2-Lite
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -31,27 +28,19 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 2e-5
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 2
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
 special_tokens:
 fsdp:

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -52,27 +52,19 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 2e-5
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 2
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
 special_tokens:
 fsdp:

examples/falcon/config-7b-lora.yml

@@ -25,9 +25,7 @@ max_packed_sequence_len:
 lora_r: 16
 lora_alpha: 32
 lora_dropout: 0.0
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -41,15 +39,10 @@ optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
 xformers_attention: true
 flash_attention:
@@ -58,11 +51,7 @@ gptq_model_v1:
 warmup_steps: 40
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|endoftext|>"
   bos_token: "<|endoftext|>"

examples/falcon/config-7b-qlora.yml

@@ -38,9 +38,7 @@ lora_alpha: 16
 # 0.05 for 33B and 65B models
 lora_dropout: 0.05
 # add LoRA modules on all linear layers of the base model
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -67,10 +65,7 @@ lr_scheduler: cosine
 # - 2e-4 for 7b & 13b
 # - 1e-4 for 33b & 64b
 learning_rate: 0.0002
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 gradient_checkpointing: true
 # stop training after this many evaluation losses have increased in a row
@@ -78,7 +73,6 @@ gradient_checkpointing: true
 early_stopping_patience: 3
 resume_from_checkpoint:
 auto_resume_from_checkpoints: true
-local_rank:
 logging_steps: 1
 xformers_attention: true
 flash_attention:
@@ -87,11 +81,7 @@ gptq_model_v1:
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.000001
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|endoftext|>"
   bos_token: "<|endoftext|>"

examples/falcon/config-7b.yml

@@ -7,9 +7,6 @@ tokenizer_type: AutoTokenizer
 
 # required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 gptq: false
 strict: false
 push_dataset_to_hub:
@@ -25,9 +22,7 @@ max_packed_sequence_len:
 lora_r: 64
 lora_alpha: 32
 lora_dropout: 0.0
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -41,15 +36,10 @@ optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
 xformers_attention: true
 flash_attention:
@@ -58,11 +48,7 @@ gptq_model_v1:
 warmup_steps: 40
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|endoftext|>"
   bos_token: "<|endoftext|>"

examples/gemma/qlora.yml

@@ -42,28 +42,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/gemma2/qlora.yml

@@ -48,28 +48,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/gemma2/reward-model.yaml

@@ -5,9 +5,6 @@ num_labels: 1
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 reward_model: true
@@ -38,8 +35,6 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 fp16:
 tf32: true
@@ -47,21 +42,12 @@ tf32: true
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/gemma3/gemma-3-1b-qlora.yml

@@ -50,30 +50,18 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/gemma3/gemma-3-4b-qlora.yml

@@ -0,0 +1,61 @@
+base_model: google/gemma-3-4b-it
+strict: false
+
+load_in_4bit: true
+
+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
+chat_template: gemma3
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -2,6 +2,8 @@ base_model: google/gemma-3-4b-it
 processor_type: AutoProcessor
 strict: false
 
+load_in_4bit: true
+
 # these 3 lines are needed for now to handle vision chat templates w images
 skip_prepare_dataset: true
 remove_unused_columns: false
@@ -20,7 +22,7 @@ dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./outputs/out
 
-adapter: lora
+adapter: qlora
 lora_model_dir:
 
 sequence_len: 2048
@@ -44,8 +46,6 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 fp16:
 tf32: true
@@ -53,7 +53,6 @@ tf32: true
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-local_rank:
 logging_steps: 1
 flash_attention: true
 eager_attention:
@@ -61,8 +60,4 @@ eager_attention:
 warmup_ratio: 0.1
 evals_per_epoch: 1
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/gptj/qlora.yml

@@ -18,9 +18,7 @@ max_packed_sequence_len:
 lora_r: 8
 lora_alpha: 32
 lora_dropout: 0.05
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -34,15 +32,10 @@ optimizer: paged_adamw_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0001
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
 xformers_attention: true
 flash_attention:
@@ -51,10 +44,6 @@ gptq_model_v1:
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|endoftext|>"

examples/jamba/qlora.yaml

@@ -40,26 +40,18 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.00001
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
 special_tokens:

examples/jamba/qlora_deepspeed.yaml

@@ -39,26 +39,20 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.00001
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
-debug:
+
 deepspeed: deepspeed_configs/zero2.json
 weight_decay: 0.0
 special_tokens:

examples/jamba/qlora_fsdp_large.yaml

@@ -39,8 +39,6 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 0.00001
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 tf32: true
 

examples/jeopardy-bot/config.yml

@@ -33,13 +33,9 @@ optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
 bf16: auto
 tf32: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 5
 xformers_attention: true
 flash_attention:
@@ -48,11 +44,7 @@ gptq_model_v1:
 warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/llama-2/fft_optimized.yml

@@ -4,9 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -26,7 +23,6 @@ lora_r:
 lora_alpha:
 lora_dropout:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -41,18 +37,12 @@ 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
@@ -61,11 +51,8 @@ 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/llama-2/gptq-lora.yml

@@ -10,8 +10,6 @@ gptq_disable_exllama: true
 
 tokenizer_use_fast: true
 tokenizer_legacy: true
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 push_dataset_to_hub:
 hf_use_auth_token: true
@@ -33,7 +31,6 @@ lora_target_modules:
   - q_proj
   - v_proj
 lora_target_linear:
-lora_fan_in_fan_out:
 wandb_project:
 wandb_watch:
 wandb_name:
@@ -50,26 +47,19 @@ torchdistx_path:
 lr_scheduler: cosine
 lr_quadratic_warmup: true
 learning_rate: 0.000017
-train_on_inputs: false
-group_by_length: false
 bf16: false
 fp16: false
 float16: true
 tf32: true
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention:
 sdp_attention:
 flash_optimum:
 warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
 special_tokens:
   bos_token: "<s>"

examples/llama-2/lisa.yml

@@ -4,9 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -26,7 +23,6 @@ lora_r:
 lora_alpha:
 lora_dropout:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 lisa_n_layers: 4
 lisa_step_interval: 20
@@ -45,18 +41,12 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 5e-5 # recommendation from lisa paper for 7b
 
-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
@@ -65,13 +55,8 @@ flash_attn_fuse_mlp: true
 
 warmup_steps: 100
 evals_per_epoch: 4
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/llama-2/loftq.yml

@@ -4,9 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -26,7 +23,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 peft:
   loftq_config:
     loftq_bits: 4
@@ -44,29 +40,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/llama-2/lora.yml

@@ -26,7 +26,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -41,29 +40,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/llama-2/qlora-fsdp.yml

@@ -26,9 +26,7 @@ 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:
@@ -43,28 +41,19 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 0.00001
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/llama-2/qlora.yml

@@ -26,9 +26,7 @@ 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:
@@ -43,27 +41,16 @@ optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/llama-2/relora.yml

@@ -24,9 +24,7 @@ pad_to_sequence_len: true
 lora_r: 8
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 relora_steps: 150
 relora_warmup_steps: 10
@@ -45,28 +43,18 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/llama-3-vision/lora-11b.yaml

@@ -45,14 +45,11 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 fp16:
 tf32: true
 
 gradient_checkpointing: true
-local_rank:
 logging_steps: 1
 flash_attention: true
 eager_attention:
@@ -60,8 +57,4 @@ eager_attention:
 warmup_ratio: 0.1
 evals_per_epoch: 1
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -42,27 +42,19 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 2e-5
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 2
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/llama-3/fft-8b.yaml

@@ -1,9 +1,6 @@
 base_model: NousResearch/Meta-Llama-3.1-8B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -30,29 +27,19 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 2e-5
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 2
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: <|end_of_text|>

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -42,7 +42,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -57,31 +56,15 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
-
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/llama-3/instruct-lora-8b.yml

@@ -37,7 +37,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -52,30 +51,17 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
    pad_token: <|end_of_text|>

examples/llama-3/lora-1b-deduplicate-dpo.yml

@@ -58,7 +58,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -73,28 +72,15 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/llama-3/lora-1b-deduplicate-sft.yml

@@ -31,7 +31,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 lora_modules_to_save:
   - embed_tokens
   - lm_head
@@ -49,30 +48,17 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
    pad_token: <|end_of_text|>

examples/llama-3/lora-1b-kernels.yml

@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -24,7 +21,6 @@ lora_r: 16
 lora_alpha: 32
 # Currently, we don't support dropout with our custom Triton kernels
 # lora_dropout: 0.05
-lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj
   - down_proj
@@ -53,18 +49,12 @@ optimizer: adamw_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
 
 loss_watchdog_threshold: 5.0
@@ -73,10 +63,6 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|end_of_text|>"

examples/llama-3/lora-1b-ray.yml

@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -24,7 +21,6 @@ pad_to_sequence_len: true
 lora_r: 16
 lora_alpha: 32
 lora_dropout: 0.05
-lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj
   - down_proj
@@ -47,18 +43,12 @@ optimizer: adamw_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
 
 loss_watchdog_threshold: 5.0
@@ -67,11 +57,9 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
+
 deepspeed: deepspeed_configs/zero3.json
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|end_of_text|>"
 

examples/llama-3/lora-1b-sample-packing-sequentially.yml

@@ -33,7 +33,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 lora_modules_to_save:
   - embed_tokens
   - lm_head
@@ -51,30 +50,17 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: <|end_of_text|>

examples/llama-3/lora-1b.yml

@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -24,7 +21,6 @@ pad_to_sequence_len: true
 lora_r: 16
 lora_alpha: 32
 lora_dropout: 0.05
-lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj
   - down_proj
@@ -47,18 +43,12 @@ optimizer: adamw_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
 
 loss_watchdog_threshold: 5.0
@@ -67,10 +57,6 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|end_of_text|>"

examples/llama-3/lora-8b.yml

@@ -27,7 +27,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 lora_modules_to_save:
   - embed_tokens
   - lm_head
@@ -45,30 +44,17 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
    pad_token: <|end_of_text|>

examples/llama-3/qlora-1b-kto.yaml

@@ -32,7 +32,6 @@ lora_r: 32
 lora_alpha: 64
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -47,31 +46,19 @@ optimizer: adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 20
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|end_of_text|>"

examples/llama-3/qlora-1b.yml

@@ -24,7 +24,6 @@ pad_to_sequence_len: true
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj
   - down_proj
@@ -47,18 +46,12 @@ 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
 
 loss_watchdog_threshold: 5.0
@@ -66,13 +59,7 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|end_of_text|>"

examples/llama-3/qlora-fsdp-405b.yaml

@@ -24,7 +24,6 @@ pad_to_sequence_len: true
 lora_r: 16
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules:
 lora_target_linear: true
 
 gradient_accumulation_steps: 4
@@ -34,8 +33,6 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 0.00001
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 tf32: true
 

examples/llama-3/qlora-fsdp-70b.yaml

@@ -26,9 +26,7 @@ pad_to_sequence_len: true
 lora_r: 8
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -43,28 +41,19 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 0.00001
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/llama-3/qlora.yml

@@ -26,9 +26,7 @@ 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:
@@ -43,28 +41,17 @@ optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: "<|end_of_text|>"

examples/llama-4/scout-lora.yaml

@@ -0,0 +1,75 @@
+base_model: meta-llama/Llama-4-Scout-17B-16E
+model_type: Llama4ForConditionalGeneration
+  # Automatically upload checkpoint and final model to HF
+  # hub_model_id: username/custom_model_name
+
+strict: false
+
+  # torch_compile: true
+
+adapter: lora
+lora_r: 32
+lora_alpha: 64
+lora_target_modules:
+  - self_attn.q_proj
+  - self_attn.k_proj
+  - self_attn.v_proj
+  - self_attn.o_proj
+lora_modules_to_save:
+  - lm_head
+  - embed_tokens
+
+chat_template: llama4
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+# gradient_checkpointing: true
+# gradient_checkpointing_kwargs:
+#   use_reentrant: false
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - auto_wrap
+  - full_shard
+fsdp_config:
+  fsdp_version: 2
+  fsdp_offload_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_reshard_after_forward: true
+  fsdp_activation_checkpointing: true
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

examples/llama-4/scout-qlora-fsdp1.yaml

@@ -0,0 +1,93 @@
+base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
+model_type: Llama4ForConditionalGeneration
+  # Automatically upload checkpoint and final model to HF
+  # hub_model_id: username/custom_model_name
+
+strict: false
+
+# torch_compile: true
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true
+load_in_4bit: true
+adapter: qlora
+lora_r: 32
+lora_alpha: 64
+lora_target_modules:
+  - self_attn.q_proj
+  - self_attn.k_proj
+  - self_attn.v_proj
+  - self_attn.o_proj
+  - shared_expert.gate_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+    # - experts.gate_projs.[0-9]+$
+    # - experts.up_projs.[0-9]+$
+    # - experts.down_projs.[0-9]+$
+lora_modules_to_save:
+  - lm_head
+  - embed_tokens
+
+chat_template: llama4
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - auto_wrap
+  - full_shard
+fsdp_config:
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_activation_checkpointing: true
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

examples/llava/lora-7b.yaml

@@ -41,14 +41,11 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 fp16:
 tf32: true
 
 gradient_checkpointing: true
-local_rank:
 logging_steps: 1
 flash_attention: true
 eager_attention:
@@ -56,8 +53,4 @@ eager_attention:
 warmup_ratio: 0.1
 evals_per_epoch: 1
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/mamba/config.yml

@@ -5,9 +5,6 @@ tokenizer_type: AutoTokenizer
 tokenizer_config: EleutherAI/gpt-neox-20b
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -38,27 +35,17 @@ train_on_inputs: false
 group_by_length: true
 
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
 tokens:
 save_safetensors: False

examples/mistral/bigstral-ds-zero3.yaml

@@ -6,9 +6,6 @@ tokenizer_type: LlamaTokenizer
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 unfrozen_parameters:
@@ -40,27 +37,19 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0001
 
-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
 
 save_total_limit: 1
 save_steps:
-debug:
+
 deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   eos_token: "<|im_end|>"
 tokens:

examples/mistral/config.yml

@@ -4,9 +4,6 @@ model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -34,28 +31,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.000005
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/mistral/lora-mps.yml

@@ -4,9 +4,6 @@ model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -28,7 +25,6 @@ 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
@@ -51,18 +47,13 @@ optimizer: adamw_torch_fused
 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
 
@@ -71,12 +62,6 @@ 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/mistral/lora.yml

@@ -27,7 +27,6 @@ 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
@@ -50,18 +49,12 @@ 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
 
 loss_watchdog_threshold: 5.0
@@ -69,12 +62,6 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/mistral/mistral-dpo-qlora.yml

@@ -40,7 +40,6 @@ lora_r: 8
 lora_alpha: 16
 lora_dropout: 0.2
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 lora_target_modules:
   - gate_proj
@@ -67,31 +66,18 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0001
 
-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: false
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<|im_start|>"
   eos_token: "<|im_end|>"

examples/mistral/mistral-qlora-fsdp.yml

@@ -32,7 +32,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -47,18 +46,12 @@ optimizer: paged_adamw_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
 
 loss_watchdog_threshold: 5.0
@@ -66,10 +59,8 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
+
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/mistral/mistral-qlora-orpo.yml

@@ -32,7 +32,6 @@ 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
@@ -55,18 +54,12 @@ 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
 
 loss_watchdog_threshold: 5.0
@@ -74,12 +67,6 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/mistral/mistral-small-3.1-24B-lora.yml

@@ -43,14 +43,11 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 fp16:
 tf32: true
 
 gradient_checkpointing: true
-local_rank:
 logging_steps: 1
 flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet.
 eager_attention:
@@ -58,9 +55,5 @@ eager_attention:
 warmup_ratio: 0.1
 evals_per_epoch: 1
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/mistral/mixtral-8x22b-qlora-fsdp.yml

@@ -30,7 +30,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -45,18 +44,12 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 loss_watchdog_threshold: 5.0
@@ -64,10 +57,8 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
+
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/mistral/mixtral-qlora-fsdp.yml

@@ -32,7 +32,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -47,18 +46,12 @@ optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 loss_watchdog_threshold: 5.0
@@ -66,10 +59,8 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
+
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/mistral/mixtral.yml

@@ -41,7 +41,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 #lora_target_modules:
 #  - gate
 #  - q_proj
@@ -65,18 +64,12 @@ 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
 
 loss_watchdog_threshold: 5.0
@@ -84,12 +77,8 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
+
 deepspeed: deepspeed_configs/zero2.json
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/mistral/mixtral_22.yml

@@ -6,9 +6,6 @@ tokenizer_type: LlamaTokenizer
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 unfrozen_parameters:
@@ -38,27 +35,19 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0001
 
-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
 
 save_total_limit: 1
 save_steps:
-debug:
+
 deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   eos_token: "<|im_end|>"
 tokens:

examples/mistral/qlora.yml

@@ -27,7 +27,6 @@ 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
@@ -50,18 +49,12 @@ 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
 
 loss_watchdog_threshold: 5.0
@@ -69,12 +62,6 @@ loss_watchdog_patience: 3
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/mpt-7b/config.yml

@@ -35,26 +35,17 @@ optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0000002
-train_on_inputs: false
-group_by_length: false
 bf16: auto
 tf32: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 5
-xformers_attention:
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0001
-fsdp:
-fsdp_config:
 tokens:
   pad_token: "<|padding|>"
   bos_token: "<|endoftext|>"

examples/openllama-3b/config.yml

@@ -4,9 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 push_dataset_to_hub:
 datasets:
@@ -23,7 +20,6 @@ lora_alpha:
 lora_dropout:
 lora_target_modules:
 lora_target_linear:
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -37,29 +33,20 @@ optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.000003
-train_on_inputs: false
-group_by_length: false
 float16: true
 bf16: false
 fp16: false
 tf32: false
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/openllama-3b/lora.yml

@@ -29,7 +29,6 @@ lora_target_modules:
   - v_proj
   - k_proj
   - o_proj
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -43,29 +42,19 @@ optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
-train_on_inputs: false
-group_by_length: false
 bf16: false
 fp16: true
 tf32: false
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 gptq_groupsize:
-s2_attention:
 gptq_model_v1:
 warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/openllama-3b/qlora.yml

@@ -21,9 +21,7 @@ sample_packing: true
 lora_r: 8
 lora_alpha: 32
 lora_dropout: 0.05
-lora_target_modules:
 lora_target_linear: true
-lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
@@ -37,28 +35,19 @@ optimizer: paged_adamw_32bit
 torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
-train_on_inputs: false
-group_by_length: false
 bf16: false
 fp16: true
 tf32: false
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"

examples/phi/lora-3.5.yaml

@@ -37,7 +37,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -52,28 +51,16 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bfloat16: true
 bf16: true
 fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
-s2_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 4
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/phi/phi-ft.yml

@@ -4,9 +4,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -27,7 +24,6 @@ lora_r:
 lora_alpha:
 lora_dropout:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -45,30 +41,20 @@ max_grad_norm: 1.0
 lr_scheduler: cosine
 learning_rate: 0.000003
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: True
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
   pad_token: "<|endoftext|>"

examples/phi/phi-qlora.yml

@@ -27,7 +27,6 @@ lora_r: 64
 lora_alpha: 32
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -45,30 +44,20 @@ max_grad_norm: 1.0
 lr_scheduler: cosine
 learning_rate: 0.000003
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: True
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
   pad_token: "<|endoftext|>"

examples/phi/phi2-ft.yml

@@ -4,9 +4,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -27,7 +24,6 @@ lora_r:
 lora_alpha:
 lora_dropout:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -45,30 +41,20 @@ max_grad_norm: 1.0
 lr_scheduler: cosine
 learning_rate: 0.000003
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: True
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
   pad_token: "<|endoftext|>"

examples/phi/phi3-ft-fsdp.yml

@@ -4,9 +4,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -28,7 +25,6 @@ lora_r:
 lora_alpha:
 lora_dropout:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 wandb_project: phi3
 wandb_entity:
@@ -46,27 +42,19 @@ max_grad_norm: 1.0
 lr_scheduler: cosine
 learning_rate: 0.000003
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.1
 fsdp:
   - full_shard

examples/phi/phi3-ft.yml

@@ -7,9 +7,6 @@ tokenizer_type: AutoTokenizer
 # hub_model_id: username/custom_model_name
 
 chat_template: phi_3
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -30,7 +27,6 @@ lora_r: 64
 lora_alpha: 32
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 gradient_accumulation_steps: 1
 micro_batch_size: 2
@@ -42,8 +38,6 @@ max_grad_norm: 1.0
 lr_scheduler: cosine
 learning_rate: 5.0e-6
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
 
 gradient_checkpointing: true
@@ -55,9 +49,9 @@ flash_attention: true
 
 eval_steps: 1000
 save_steps: 5000
-eval_table_size: 2
 eval_batch_size: 2
 eval_sample_packing: false
+eval_table_size: 2
 eval_max_new_tokens: 32
 eval_causal_lm_metrics: ["perplexity"]
 do_causal_lm_eval: true

examples/pixtral/lora-12b.yml

@@ -41,14 +41,11 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: true
 fp16:
 tf32: true
 
 gradient_checkpointing: true
-local_rank:
 logging_steps: 1
 flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet
 eager_attention:
@@ -56,10 +53,6 @@ eager_attention:
 warmup_ratio: 0.1
 evals_per_epoch: 1
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
   pad_token: <pad>

examples/pythia-12b/config.yml

@@ -5,9 +5,6 @@ model_type: GPTNeoXForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
 gptq: false
 device_map: auto
 datasets:
@@ -22,7 +19,6 @@ max_packed_sequence_len: 2048
 lora_r: 64
 lora_alpha: 32
 lora_dropout: 0.0
-lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
@@ -37,16 +33,10 @@ num_epochs: 5
 learning_rate: 0.00003
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
-train_on_inputs: false
-group_by_length: false
 bf16: false
 fp16: false
 float16: true
 tf32: true
 flash_optimum: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 gradient_checkpointing: true
-fsdp:
-fsdp_config:

examples/pythia/lora.yml

@@ -28,13 +28,9 @@ gradient_accumulation_steps: 1
 micro_batch_size: 4
 num_epochs: 4
 learning_rate: 0.00001
-train_on_inputs: false
-group_by_length: false
 bf16: auto
 tf32: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 weight_decay: 0.1
 evals_per_epoch: 4
 logging_steps: 1

examples/qwen/lora.yml

@@ -28,7 +28,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -43,28 +42,16 @@ 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: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/qwen/qlora.yml

@@ -28,7 +28,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -43,28 +42,16 @@ 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: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention:
 
 warmup_steps: 10
 evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:

examples/qwen/qwen2-moe-lora.yaml

@@ -3,9 +3,6 @@ base_model: Qwen/Qwen1.5-MoE-A2.7B
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -25,7 +22,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -40,28 +36,18 @@ optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens: