validate config to set defaults

Add: line about further optimizations using llmcompressor
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
2025-04-26 13:11:25 -04:00 · 2025-04-24 14:06:25 -04:00 · 2025-04-24 12:37:14 -05:00 · 2025-04-24 13:36:09 -04:00 · 2025-04-24 12:45:57 -04:00 · 2025-04-24 12:45:57 -04:00
42 changed files with 790 additions and 2250 deletions
--- a/.github/workflows/multi-gpu-e2e.yml
+++ b/.github/workflows/multi-gpu-e2e.yml
@@ -8,7 +8,6 @@ on:
      - 'setup.py'
      - 'pyproject.toml'
      - '.github/workflows/multi-gpu-e2e.yml'
-      - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
  workflow_dispatch:
  schedule:
    - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
--- a/.runpod/.gitignore
+++ b/.runpod/.gitignore
@@ -1,161 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-pod/scripts/config.yaml
--- a/.runpod/Dockerfile
+++ b/.runpod/Dockerfile
@@ -1,18 +0,0 @@
-FROM runpod/pytorch:3.10-2.0.0-117
-
-COPY .runpod/requirements.txt /requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install --upgrade pip && \
-    python3 -m pip install --upgrade -r /requirements.txt
-
-
-# Environment settings
-ARG BASE_VOLUME="/runpod-volume"
-ENV BASE_VOLUME=$BASE_VOLUME
-ENV HF_DATASETS_CACHE="${BASE_VOLUME}/huggingface-cache/datasets"
-ENV HUGGINGFACE_HUB_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
-ENV TRANSFORMERS_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
-
-COPY .runpod/src /src
-
-CMD ["python3", "/src/handler.py"]
--- a/.runpod/README.md
+++ b/.runpod/README.md
@@ -1,335 +0,0 @@
-<h1>LLM Post Training- Full fine-tune, LoRA, QLoRa etc. Llama/Mistral/Gemma and more</h1>
-
-# Configuration Options
-
-This document outlines all available configuration options for training models. The configuration can be provided as a JSON request.
-
-## Usage
-
-You can use these configuration Options:
-
-1. As a JSON request body:
-
-```json
-{
-  "input": {
-    "user_id": "user",
-    "model_id": "model-name",
-    "run_id": "run-id",
-    "credentials": {
-      "wandb_api_key": "", # add your Weights & biases key. TODO:  you will be able to set this in Enviornment variables.
-      "hf_token": "", # add your HF_token. TODO:  you will be able to set this in Enviornment variables.
-    },
-    "args": {
-      "base_model": "NousResearch/Llama-3.2-1B",
-      // ... other options
-    }
-  }
-}
-```
-
-## Configuration Options
-
-### Model Configuration
-
-| Option              | Description                                                                                   | Default              |
-| ------------------- | --------------------------------------------------------------------------------------------- | -------------------- |
-| `base_model`        | Path to the base model (local or HuggingFace)                                                 | Required             |
-| `base_model_config` | Configuration path for the base model                                                         | Same as base_model   |
-| `revision_of_model` | Specific model revision from HuggingFace hub                                                  | Latest               |
-| `tokenizer_config`  | Custom tokenizer configuration path                                                           | Optional             |
-| `model_type`        | Type of model to load                                                                         | AutoModelForCausalLM |
-| `tokenizer_type`    | Type of tokenizer to use                                                                      | AutoTokenizer        |
-| `hub_model_id`      | Repository ID where the model will be pushed on Hugging Face Hub (format: username/repo-name) | Optional             |
-
-## Model Family Identification
-
-| Option                     | Default | Description                    |
-| -------------------------- | ------- | ------------------------------ |
-| `is_falcon_derived_model`  | `false` | Whether model is Falcon-based  |
-| `is_llama_derived_model`   | `false` | Whether model is LLaMA-based   |
-| `is_qwen_derived_model`    | `false` | Whether model is Qwen-based    |
-| `is_mistral_derived_model` | `false` | Whether model is Mistral-based |
-
-## Model Configuration Overrides
-
-| Option                                          | Default    | Description                        |
-| ----------------------------------------------- | ---------- | ---------------------------------- |
-| `overrides_of_model_config.rope_scaling.type`   | `"linear"` | RoPE scaling type (linear/dynamic) |
-| `overrides_of_model_config.rope_scaling.factor` | `1.0`      | RoPE scaling factor                |
-
-### Model Loading Options
-
-| Option         | Description                   | Default |
-| -------------- | ----------------------------- | ------- |
-| `load_in_8bit` | Load model in 8-bit precision | false   |
-| `load_in_4bit` | Load model in 4-bit precision | false   |
-| `bf16`         | Use bfloat16 precision        | false   |
-| `fp16`         | Use float16 precision         | false   |
-| `tf32`         | Use tensor float 32 precision | false   |
-
-## Memory and Device Settings
-
-| Option             | Default   | Description             |
-| ------------------ | --------- | ----------------------- |
-| `gpu_memory_limit` | `"20GiB"` | GPU memory limit        |
-| `lora_on_cpu`      | `false`   | Load LoRA on CPU        |
-| `device_map`       | `"auto"`  | Device mapping strategy |
-| `max_memory`       | `null`    | Max memory per device   |
-
-## Training Hyperparameters
-
-| Option                        | Default   | Description                 |
-| ----------------------------- | --------- | --------------------------- |
-| `gradient_accumulation_steps` | `1`       | Gradient accumulation steps |
-| `micro_batch_size`            | `2`       | Batch size per GPU          |
-| `eval_batch_size`             | `null`    | Evaluation batch size       |
-| `num_epochs`                  | `4`       | Number of training epochs   |
-| `warmup_steps`                | `100`     | Warmup steps                |
-| `warmup_ratio`                | `0.05`    | Warmup ratio                |
-| `learning_rate`               | `0.00003` | Learning rate               |
-| `lr_quadratic_warmup`         | `false`   | Quadratic warmup            |
-| `logging_steps`               | `null`    | Logging frequency           |
-| `eval_steps`                  | `null`    | Evaluation frequency        |
-| `evals_per_epoch`             | `null`    | Evaluations per epoch       |
-| `save_strategy`               | `"epoch"` | Checkpoint saving strategy  |
-| `save_steps`                  | `null`    | Saving frequency            |
-| `saves_per_epoch`             | `null`    | Saves per epoch             |
-| `save_total_limit`            | `null`    | Maximum checkpoints to keep |
-| `max_steps`                   | `null`    | Maximum training steps      |
-
-### Dataset Configuration
-
-```yaml
-datasets:
-  - path: vicgalle/alpaca-gpt4 # HuggingFace dataset or TODO: You will be able to add the local path.
-    type: alpaca # Format type (alpaca, gpteacher, oasst, etc.)
-    ds_type: json # Dataset type
-    data_files: path/to/data # Source data files
-    train_on_split: train # Dataset split to use
-```
-
-## Chat Template Settings
-
-| Option                   | Default                          | Description            |
-| ------------------------ | -------------------------------- | ---------------------- |
-| `chat_template`          | `"tokenizer_default"`            | Chat template type     |
-| `chat_template_jinja`    | `null`                           | Custom Jinja template  |
-| `default_system_message` | `"You are a helpful assistant."` | Default system message |
-
-## Dataset Processing
-
-| Option                        | Default                    | Description                       |
-| ----------------------------- | -------------------------- | --------------------------------- |
-| `dataset_prepared_path`       | `"data/last_run_prepared"` | Path for prepared dataset         |
-| `push_dataset_to_hub`         | `""`                       | Push dataset to HF hub            |
-| `dataset_processes`           | `4`                        | Number of preprocessing processes |
-| `dataset_keep_in_memory`      | `false`                    | Keep dataset in memory            |
-| `shuffle_merged_datasets`     | `true`                     | Shuffle merged datasets           |
-| `dataset_exact_deduplication` | `true`                     | Deduplicate datasets              |
-
-## LoRA Configuration
-
-| Option                     | Default                | Description                    |
-| -------------------------- | ---------------------- | ------------------------------ |
-| `adapter`                  | `"lora"`               | Adapter type (lora/qlora)      |
-| `lora_model_dir`           | `""`                   | Directory with pretrained LoRA |
-| `lora_r`                   | `8`                    | LoRA attention dimension       |
-| `lora_alpha`               | `16`                   | LoRA alpha parameter           |
-| `lora_dropout`             | `0.05`                 | LoRA dropout                   |
-| `lora_target_modules`      | `["q_proj", "v_proj"]` | Modules to apply LoRA          |
-| `lora_target_linear`       | `false`                | Target all linear modules      |
-| `peft_layers_to_transform` | `[]`                   | Layers to transform            |
-| `lora_modules_to_save`     | `[]`                   | Modules to save                |
-| `lora_fan_in_fan_out`      | `false`                | Fan in/out structure           |
-
-## Optimization Settings
-
-| Option                    | Default | Description                |
-| ------------------------- | ------- | -------------------------- |
-| `train_on_inputs`         | `false` | Train on input prompts     |
-| `group_by_length`         | `false` | Group by sequence length   |
-| `gradient_checkpointing`  | `false` | Use gradient checkpointing |
-| `early_stopping_patience` | `3`     | Early stopping patience    |
-
-## Learning Rate Scheduling
-
-| Option                     | Default    | Description          |
-| -------------------------- | ---------- | -------------------- |
-| `lr_scheduler`             | `"cosine"` | Scheduler type       |
-| `lr_scheduler_kwargs`      | `{}`       | Scheduler parameters |
-| `cosine_min_lr_ratio`      | `null`     | Minimum LR ratio     |
-| `cosine_constant_lr_ratio` | `null`     | Constant LR ratio    |
-| `lr_div_factor`            | `null`     | LR division factor   |
-
-## Optimizer Settings
-
-| Option                 | Default      | Description         |
-| ---------------------- | ------------ | ------------------- |
-| `optimizer`            | `"adamw_hf"` | Optimizer choice    |
-| `optim_args`           | `{}`         | Optimizer arguments |
-| `optim_target_modules` | `[]`         | Target modules      |
-| `weight_decay`         | `null`       | Weight decay        |
-| `adam_beta1`           | `null`       | Adam beta1          |
-| `adam_beta2`           | `null`       | Adam beta2          |
-| `adam_epsilon`         | `null`       | Adam epsilon        |
-| `max_grad_norm`        | `null`       | Gradient clipping   |
-
-## Attention Implementations
-
-| Option                     | Default | Description                   |
-| -------------------------- | ------- | ----------------------------- |
-| `flash_optimum`            | `false` | Use better transformers       |
-| `xformers_attention`       | `false` | Use xformers                  |
-| `flash_attention`          | `false` | Use flash attention           |
-| `flash_attn_cross_entropy` | `false` | Flash attention cross entropy |
-| `flash_attn_rms_norm`      | `false` | Flash attention RMS norm      |
-| `flash_attn_fuse_qkv`      | `false` | Fuse QKV operations           |
-| `flash_attn_fuse_mlp`      | `false` | Fuse MLP operations           |
-| `sdp_attention`            | `false` | Use scaled dot product        |
-| `s2_attention`             | `false` | Use shifted sparse attention  |
-
-## Tokenizer Modifications
-
-| Option           | Default | Description                  |
-| ---------------- | ------- | ---------------------------- |
-| `special_tokens` | -       | Special tokens to add/modify |
-| `tokens`         | `[]`    | Additional tokens            |
-
-## Distributed Training
-
-| Option                  | Default | Description           |
-| ----------------------- | ------- | --------------------- |
-| `fsdp`                  | `null`  | FSDP configuration    |
-| `fsdp_config`           | `null`  | FSDP config options   |
-| `deepspeed`             | `null`  | Deepspeed config path |
-| `ddp_timeout`           | `null`  | DDP timeout           |
-| `ddp_bucket_cap_mb`     | `null`  | DDP bucket capacity   |
-| `ddp_broadcast_buffers` | `null`  | DDP broadcast buffers |
-
-<details>
-<summary><h3>Example Configuration Request:</h3></summary>
-
-Here's a complete example for fine-tuning a LLaMA model using LoRA:
-
-```json
-{
-  "input": {
-    "user_id": "user",
-    "model_id": "llama-test",
-    "run_id": "test-run",
-    "credentials": {
-      "wandb_api_key": "",
-      "hf_token": ""
-    },
-    "args": {
-      "base_model": "NousResearch/Llama-3.2-1B",
-      "load_in_8bit": false,
-      "load_in_4bit": false,
-      "strict": false,
-      "datasets": [
-        {
-          "path": "teknium/GPT4-LLM-Cleaned",
-          "type": "alpaca"
-        }
-      ],
-      "dataset_prepared_path": "last_run_prepared",
-      "val_set_size": 0.1,
-      "output_dir": "./outputs/lora-out",
-      "adapter": "lora",
-      "sequence_len": 2048,
-      "sample_packing": true,
-      "eval_sample_packing": true,
-      "pad_to_sequence_len": true,
-      "lora_r": 16,
-      "lora_alpha": 32,
-      "lora_dropout": 0.05,
-      "lora_target_modules": [
-        "gate_proj",
-        "down_proj",
-        "up_proj",
-        "q_proj",
-        "v_proj",
-        "k_proj",
-        "o_proj"
-      ],
-      "gradient_accumulation_steps": 2,
-      "micro_batch_size": 2,
-      "num_epochs": 1,
-      "optimizer": "adamw_8bit",
-      "lr_scheduler": "cosine",
-      "learning_rate": 0.0002,
-      "train_on_inputs": false,
-      "group_by_length": false,
-      "bf16": "auto",
-      "tf32": false,
-      "gradient_checkpointing": true,
-      "logging_steps": 1,
-      "flash_attention": true,
-      "loss_watchdog_threshold": 5,
-      "loss_watchdog_patience": 3,
-      "warmup_steps": 10,
-      "evals_per_epoch": 4,
-      "saves_per_epoch": 1,
-      "weight_decay": 0,
-      "hub_model_id": "runpod/llama-fr-lora",
-      "wandb_name": "test-run-1",
-      "wandb_project": "test-run-1",
-      "wandb_entity": "axo-test",
-      "special_tokens": {
-        "pad_token": "<|end_of_text|>"
-      }
-    }
-  }
-}
-```
-
-</details>
-
-### Advanced Features
-
-#### Wandb Integration
-
- `wandb_project`: Project name for Weights & Biases
- `wandb_entity`: Team name in W&B
- `wandb_watch`: Monitor model with W&B
- `wandb_name`: Name of the W&B run
- `wandb_run_id`: ID for the W&B run
-
-#### Performance Optimization
-
- `sample_packing`: Enable efficient sequence packing
- `eval_sample_packing`: Use sequence packing during evaluation
- `torch_compile`: Enable PyTorch 2.0 compilation
- `flash_attention`: Use Flash Attention implementation
- `xformers_attention`: Use xFormers attention implementation
-
-### Available Optimizers
-
-The following optimizers are supported:
-
- `adamw_hf`: HuggingFace's AdamW implementation
- `adamw_torch`: PyTorch's AdamW
- `adamw_torch_fused`: Fused AdamW implementation
- `adamw_torch_xla`: XLA-optimized AdamW
- `adamw_apex_fused`: NVIDIA Apex fused AdamW
- `adafactor`: Adafactor optimizer
- `adamw_anyprecision`: Anyprecision AdamW
- `adamw_bnb_8bit`: 8-bit AdamW from bitsandbytes
- `lion_8bit`: 8-bit Lion optimizer
- `lion_32bit`: 32-bit Lion optimizer
- `sgd`: Stochastic Gradient Descent
- `adagrad`: Adagrad optimizer
-
-## Notes
-
- Set `load_in_8bit: true` or `load_in_4bit: true` for memory-efficient training
- Enable `flash_attention: true` for faster training on modern GPUs
- Use `gradient_checkpointing: true` to reduce memory usage
- Adjust `micro_batch_size` and `gradient_accumulation_steps` based on your GPU memory
-
-For more detailed information, please refer to the [documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html).
-
-### Errors:
-
- if you face any issues with the Flash Attention-2, Delete yoor worker and Re-start.
--- a/.runpod/hub.json
+++ b/.runpod/hub.json
@@ -1,93 +0,0 @@
-{
-  "title": "Axolotl Fine-Tuning",
-  "description": "Serverless fine-tuning of open-source LLMs with Axolotl. Supports LoRA, QLoRA, DPO, and more using Hugging Face models and datasets.",
-  "type": "serverless",
-  "category": "language",
-  "iconUrl": "https://avatars.githubusercontent.com/u/167502477",
-  "config": {
-    "runsOn": "GPU",
-    "containerDiskInGb": 200,
-    "gpuCount": 1,
-    "allowedCudaVersions": [
-      "12.8",
-      "12.7",
-      "12.6",
-      "12.5",
-      "12.4"
-    ],
-    "presets": [],
-    "env": [
-      {
-        "key": "TOKENIZER",
-        "input": {
-          "name": "Tokenizer",
-          "type": "string",
-          "description": "Name or path of the Hugging Face tokenizer to use.",
-          "default": "",
-          "advanced": true
-        }
-      },
-      {
-        "key": "MAX_NUM_SEQS",
-        "input": {
-          "name": "Max Num Seqs",
-          "type": "number",
-          "description": "Maximum number of sequences per iteration.",
-          "default": 256,
-          "advanced": true
-        }
-      },
-      {
-        "key": "DISABLE_LOG_STATS",
-        "input": {
-          "name": "Disable Log Stats",
-          "type": "boolean",
-          "description": "Disable logging statistics.",
-          "default": false,
-          "trueValue": "true",
-          "falseValue": "false"
-        }
-      },
-      {
-        "key": "LOAD_FORMAT",
-        "input": {
-          "name": "Load Format",
-          "type": "string",
-          "description": "The format of the model weights to load.",
-          "default": "auto",
-          "options": [
-            {
-              "label": "auto",
-              "value": "auto"
-            },
-            {
-              "label": "pt",
-              "value": "pt"
-            },
-            {
-              "label": "safetensors",
-              "value": "safetensors"
-            },
-            {
-              "label": "npcache",
-              "value": "npcache"
-            },
-            {
-              "label": "dummy",
-              "value": "dummy"
-            },
-            {
-              "label": "tensorizer",
-              "value": "tensorizer"
-            },
-            {
-              "label": "bitsandbytes",
-              "value": "bitsandbytes"
-            }
-          ],
-          "advanced": true
-        }
-      }
-    ]
-  }
-}
--- a/.runpod/requirements.txt
+++ b/.runpod/requirements.txt
@@ -1,15 +0,0 @@
-# Required Python packages get listed here, one per line.
-# Reccomended to lock the version number to avoid unexpected changes.
-
-# You can also install packages from a git repository, e.g.:
-# git+https://github.com/runpod/runpod-python.git
-# To learn more, see https://pip.pypa.io/en/stable/reference/requirements-file-format/
-runpod~=1.7.0
-huggingface_hub
-typing-extensions
-pydantic
-pydantic-settings
-hf-transfer
-setuptools
-numpy==2.0.0
-axolotl[flash-attn,deepspeed]
--- a/.runpod/src/config/config.yaml
+++ b/.runpod/src/config/config.yaml
@@ -1,577 +0,0 @@
-# # This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
-# # This can also be a relative path to a model on disk
-# base_model: ./llama-7b-hf
-# # You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
-# base_model_ignore_patterns:
-# # If the base_model repo on hf hub doesn't include configuration .json files,
-# # 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
-# # 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
-# model_type: AutoModelForCausalLM
-# # Corresponding tokenizer for the model AutoTokenizer is a good choice
-# tokenizer_type: AutoTokenizer
-# # Trust remote code for untrusted source
-# trust_remote_code:
-# # use_fast option for tokenizer loading from_pretrained, default to True
-# tokenizer_use_fast:
-# # Whether to use the legacy tokenizer setting, defaults to True
-# tokenizer_legacy:
-# # Resize the model embeddings when new tokens are added to multiples of 32
-# # This is reported to improve training speed on some models
-# resize_token_embeddings_to_32x:
-
-# # Used to identify which the model is based on
-# is_falcon_derived_model:
-# is_llama_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:
-#   # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
-#   rope_scaling:
-#     type: # linear | dynamic
-#     factor: # float
-
-
-# # 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
-# # Use bitsandbytes 4 bit
-# load_in_4bit:
-
-# # Use CUDA bf16
-# bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
-# # Use CUDA fp16
-# fp16: true
-# # Use CUDA tf32
-# tf32: true # require >=ampere
-
-# # No AMP (automatic mixed precision)
-# bfloat16: true # require >=ampere
-# float16: true
-
-# # A list of one or more datasets to finetune the model with
-# datasets:
-#   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
-#   - path: vicgalle/alpaca-gpt4
-#   # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
-#     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
-#     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
-#     data_files: # Optional[str] path to source data files
-#     shards: # Optional[int] number of shards to split data into
-#     name: # Optional[str] name of dataset configuration to load
-#     train_on_split: train # Optional[str] name of dataset split to load from
-
-#     # Optional[str] fastchat conversation type, only used with type: sharegpt
-#     conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-#     field_human: # Optional[str]. Human key to use for conversation.
-#     field_model: # Optional[str]. Assistant key to use for conversation.
-
-#   # Custom user prompt
-#   - path: repo
-#     type:
-#       # The below are defaults. only set what's needed.
-#       system_prompt: ""
-#       system_format: "{system}"
-#       field_system: system
-#       field_instruction: instruction
-#       field_input: input
-#       field_output: output
-
-#       # Customizable to be single line or multi-line
-#       # 'format' can include {input}
-#       format: |-
-#         User: {instruction} {input}
-#         Assistant:
-#       # 'no_input_format' cannot include {input}
-#       no_input_format: "{instruction} "
-
-#       # For `completion` datsets only, uses the provided field instead of `text` column
-#       field:
-
-# # Axolotl attempts to save the dataset as an arrow after packing the data together so
-# # subsequent training attempts load faster, relative path
-# dataset_prepared_path: data/last_run_prepared
-# # Push prepared dataset to hub
-# push_dataset_to_hub: # repo path
-# # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
-# # if not set.
-# dataset_processes: # defaults to os.cpu_count() if not set
-# # push checkpoints to hub
-# hub_model_id: # repo path to push finetuned model
-# # how to push checkpoints to hub
-# # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
-# hub_strategy:
-# # Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
-# # Required to be true when used in combination with `push_dataset_to_hub`
-# hf_use_auth_token: # boolean
-# # How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
-# val_set_size: 0.04
-# # Num shards for whole dataset
-# dataset_shard_num:
-# # Index of shard to use for whole dataset
-# dataset_shard_idx:
-
-# # The maximum length of an input to train with, this should typically be less than 2048
-# # as most models have a token/context limit of 2048
-# sequence_len: 2048
-# # Pad inputs so each step uses constant sized buffers
-# # This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
-# pad_to_sequence_len:
-# # Max sequence length to concatenate training samples together up to
-# # Inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
-# # FutureWarning: This will soon be DEPRECATED
-# max_packed_sequence_len: 1024
-# # Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
-# sample_packing:
-# # Set to 'false' if getting errors during eval with sample_packing on.
-# eval_sample_packing:
-# # You can set these packing optimizations AFTER starting a training at least once.
-# # The trainer will provide recommended values for these values.
-# sample_packing_eff_est:
-# total_num_tokens:
-
-# # If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
-# adapter: lora
-# # If you already have a lora model trained that you want to load, put that here.
-# # This means after training, if you want to test the model, you should set this to the value of `lora_out_dir`.
-# lora_model_dir:
-
-# # LoRA hyperparameters
-# # For more details about the following options, see:
-# # https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
-# lora_r: 8
-# lora_alpha: 16
-# lora_dropout: 0.05
-# lora_target_modules:
-#   - q_proj
-#   - v_proj
-# #  - k_proj
-# #  - o_proj
-# #  - gate_proj
-# #  - down_proj
-# #  - up_proj
-# lora_target_linear: # If true, will target all linear layers
-
-# # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
-# # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
-# # `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
-# # https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
-# lora_modules_to_save:
-# #  - embed_tokens
-# #  - lm_head
-
-# # Once you complete training, the model will be saved to the following directory.
-# # If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
-# # Make sure `lora_model_dir` points to this directory if you want to use the trained model.
-# lora_out_dir:
-# lora_fan_in_fan_out: false
-
-# # ReLoRA configuration
-# # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
-# relora_steps: # Number of steps per ReLoRA restart
-# relora_warmup_steps: # Number of per-restart warmup steps
-# relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
-
-# # wandb configuration if you're using it
-# wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
-# wandb_project: # Your wandb project name
-# wandb_entity: # A wandb Team name if using a Team
-# wandb_watch:
-# wandb_run_id: # Set the name of your wandb run
-# wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
-
-# # Where to save the full-finetuned model to
-# output_dir: ./completed-model
-
-# # Whether to use torch.compile and which backend to use
-# torch_compile:  # bool
-# torch_compile_backend:  # Optional[str]
-
-# # Training hyperparameters
-
-# # If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
-# gradient_accumulation_steps: 1
-# # The number of samples to include in each batch. This is the number of samples sent to each GPU.
-# micro_batch_size: 2
-# eval_batch_size:
-# num_epochs: 4
-# warmup_steps: 100  # cannot use with warmup_ratio
-# warmup_ratio: 0.05  # cannot use with warmup_steps
-# learning_rate: 0.00003
-# lr_quadratic_warmup:
-# logging_steps:
-# save_strategy: # Set to `no` to skip checkpoint saves
-# save_steps: # Leave empty to save at each epoch
-# eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
-# save_total_limit: # Checkpoints saved at a time
-# # Maximum number of iterations to train for. It precedes num_epochs which means that
-# # if both are set, num_epochs will not be guaranteed.
-# # e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
-# max_steps:
-
-# eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
-# eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-
-# # Save model as safetensors (require safetensors package)
-# save_safetensors:
-
-# # Whether to mask out or include the human's prompt from the training labels
-# train_on_inputs: false
-# # Group similarly sized data to minimize padding.
-# # May be slower to start, as it must download and sort the entire dataset.
-# # 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
-# gradient_checkpointing: false
-
-# # Stop training after this many evaluation losses have increased in a row
-# # https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
-# early_stopping_patience: 3
-
-# # Specify a scheduler and kwargs to use with the optimizer
-# lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
-# lr_scheduler_kwargs:
-
-# # 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
-# #
-# # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
-# # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
-# # in the examples/ for your model and fine-tuning use case.
-# #
-# # Valid values for 'optimizer' include:
-# # - adamw_hf
-# # - adamw_torch
-# # - adamw_torch_fused
-# # - adamw_torch_xla
-# # - adamw_apex_fused
-# # - adafactor
-# # - adamw_anyprecision
-# # - sgd
-# # - adagrad
-# # - adamw_bnb_8bit
-# # - lion_8bit
-# # - lion_32bit
-# # - paged_adamw_32bit
-# # - paged_adamw_8bit
-# # - paged_lion_32bit
-# # - paged_lion_8bit
-# optimizer:
-# # Specify weight decay
-# weight_decay:
-# # adamw hyperparams
-# adam_beta1:
-# adam_beta2:
-# adam_epsilon:
-# # Gradient clipping max norm
-# max_grad_norm:
-
-# # Augmentation techniques
-# # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
-# # currently only supported on Llama and Mistral
-# noisy_embedding_alpha:
-
-# # Whether to bettertransformers
-# flash_optimum:
-# # Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
-# xformers_attention:
-# # Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
-# flash_attention:
-# flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
-# flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
-# flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
-# flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
-# # Whether to use scaled-dot-product attention
-# # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
-# sdp_attention:
-# # Landmark attention (only llama)
-# landmark_attention:
-# # xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
-# # LLaMA only
-# xpos_rope:
-
-# # Resume from a specific checkpoint dir
-# resume_from_checkpoint:
-# # If resume_from_checkpoint isn't set and you simply want it to start where it left off.
-# # Be careful with this being turned on between different models.
-# auto_resume_from_checkpoints: false
-
-# # Don't mess with this, it's here for accelerate and torchrun
-# local_rank:
-
-# # Add or change special tokens.
-# # If you add tokens here, you don't need to add them to the `tokens` list.
-# special_tokens:
-#   # bos_token: "<s>"
-#   # eos_token: "</s>"
-#   # unk_token: "<unk>"
-
-# # Add extra tokens.
-# tokens:
-
-# # FSDP
-# fsdp:
-# fsdp_config:
-
-# # Deepspeed config path. e.g., deepspeed/zero3.json
-# deepspeed:
-
-# # Advanced DDP Arguments
-# ddp_timeout:
-# ddp_bucket_cap_mb:
-# ddp_broadcast_buffers:
-
-# # Path to torch distx for optim 'adamw_anyprecision'
-# torchdistx_path:
-
-# # Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
-# pretraining_dataset:
-
-# # Debug mode
-# debug:
-
-# # Seed
-# seed:
-
-# # Allow overwrite yml config using from cli
-# strict:
-
-
-
-base_model: ${BASE_MODEL}
-base_model_ignore_patterns: ${BASE_MODEL_IGNORE_PATTERNS}
-base_model_config: ${BASE_MODEL_CONFIG}
-revision_of_model: ${REVISION_OF_MODEL}
-tokenizer_config: ${TOKENIZER_CONFIG}
-model_type: ${MODEL_TYPE}
-tokenizer_type: ${TOKENIZER_TYPE}
-trust_remote_code: ${TRUST_REMOTE_CODE}
-tokenizer_use_fast: ${TOKENIZER_USE_FAST}
-tokenizer_legacy: ${TOKENIZER_LEGACY}
-resize_token_embeddings_to_32x: ${RESIZE_TOKEN_EMBEDDINGS_TO_32X}
-
-is_falcon_derived_model: ${IS_FALCON_DERIVED_MODEL}
-is_llama_derived_model: ${IS_LLAMA_DERIVED_MODEL}
-is_qwen_derived_model: ${IS_QWEN_DERIVED_MODEL}
-is_mistral_derived_model: ${IS_MISTRAL_DERIVED_MODEL}
-
-overrides_of_model_config:
-  rope_scaling:
-    type: ${ROPE_SCALING_TYPE}
-    factor: ${ROPE_SCALING_FACTOR}
-
-bnb_config_kwargs:
-  llm_int8_has_fp16_weight: ${BNB_LLM_INT8_HAS_FP16_WEIGHT}
-  bnb_4bit_quant_type: ${BNB_4BIT_QUANT_TYPE}
-  bnb_4bit_use_double_quant: ${BNB_4BIT_USE_DOUBLE_QUANT}
-
-gptq: ${GPTQ}
-load_in_8bit: ${LOAD_IN_8BIT}
-load_in_4bit: ${LOAD_IN_4BIT}
-bf16: ${BF16}
-fp16: ${FP16}
-tf32: ${TF32}
-bfloat16: ${BFLOAT16}
-float16: ${FLOAT16}
-
-gpu_memory_limit: ${GPU_MEMORY_LIMIT}
-lora_on_cpu: ${LORA_ON_CPU}
-
-datasets:
-  - path: ${DATASET_PATH}
-    type: ${DATASET_TYPE}
-    ds_type: ${DATASET_DS_TYPE}
-    data_files: ${DATASET_DATA_FILES}
-    shards: ${DATASET_SHARDS}
-    name: ${DATASET_NAME}
-    train_on_split: ${DATASET_TRAIN_ON_SPLIT}
-    revision: ${DATASET_REVISION}
-    trust_remote_code: ${DATASET_TRUST_REMOTE_CODE}
-
-rl: ${RL}
-dpo_use_weighting: ${DPO_USE_WEIGHTING}
-
-chat_template: ${CHAT_TEMPLATE}
-chat_template_jinja: ${CHAT_TEMPLATE_JINJA}
-default_system_message: ${DEFAULT_SYSTEM_MESSAGE}
-dataset_prepared_path: ${DATASET_PREPARED_PATH}
-push_dataset_to_hub: ${PUSH_DATASET_TO_HUB}
-dataset_processes: ${DATASET_PROCESSES}
-dataset_keep_in_memory: ${DATASET_KEEP_IN_MEMORY}
-hub_model_id: ${HUB_MODEL_ID}
-hub_strategy: ${HUB_STRATEGY}
-hf_use_auth_token: ${HF_USE_AUTH_TOKEN}
-val_set_size: ${VAL_SET_SIZE}
-dataset_shard_num: ${DATASET_SHARD_NUM}
-dataset_shard_idx: ${DATASET_SHARD_IDX}
-
-sequence_len: ${SEQUENCE_LEN}
-pad_to_sequence_len: ${PAD_TO_SEQUENCE_LEN}
-sample_packing: ${SAMPLE_PACKING}
-eval_sample_packing: ${EVAL_SAMPLE_PACKING}
-sample_packing_eff_est: ${SAMPLE_PACKING_EFF_EST}
-total_num_tokens: ${TOTAL_NUM_TOKENS}
-sample_packing_group_size: ${SAMPLE_PACKING_GROUP_SIZE}
-sample_packing_bin_size: ${SAMPLE_PACKING_BIN_SIZE}
-
-batch_flattening: ${BATCH_FLATTENING}
-device_map: ${DEVICE_MAP}
-max_memory: ${MAX_MEMORY}
-
-adapter: ${ADAPTER}
-lora_model_dir: ${LORA_MODEL_DIR}
-
-lora_r: ${LORA_R}
-lora_alpha: ${LORA_ALPHA}
-lora_dropout: ${LORA_DROPOUT}
-lora_target_modules:
-  - ${LORA_TARGET_MODULES}
-lora_target_linear: ${LORA_TARGET_LINEAR}
-peft_layers_to_transform: ${PEFT_LAYERS_TO_TRANSFORM}
-lora_modules_to_save: ${LORA_MODULES_TO_SAVE}
-lora_fan_in_fan_out: ${LORA_FAN_IN_FAN_OUT}
-
-loraplus_lr_ratio: ${LORAPLUS_LR_RATIO}
-loraplus_lr_embedding: ${LORAPLUS_LR_EMBEDDING}
-
-peft:
-  loftq_config:
-    loftq_bits: ${LOFTQ_BITS}
-
-relora_steps: ${RELORA_STEPS}
-relora_warmup_steps: ${RELORA_WARMUP_STEPS}
-relora_anneal_steps: ${RELORA_ANNEAL_STEPS}
-relora_prune_ratio: ${RELORA_PRUNE_RATIO}
-relora_cpu_offload: ${RELORA_CPU_OFFLOAD}
-
-wandb_mode: ${WANDB_MODE}
-wandb_project: ${WANDB_PROJECT}
-wandb_entity: ${WANDB_ENTITY}
-wandb_watch: ${WANDB_WATCH}
-wandb_name: ${WANDB_NAME}
-wandb_run_id: ${WANDB_RUN_ID}
-wandb_log_model: ${WANDB_LOG_MODEL}
-
-mlflow_tracking_uri: ${MLFLOW_TRACKING_URI}
-mlflow_experiment_name: ${MLFLOW_EXPERIMENT_NAME}
-mlflow_run_name: ${MLFLOW_RUN_NAME}
-hf_mlflow_log_artifacts: ${HF_MLFLOW_LOG_ARTIFACTS}
-
-use_comet: ${USE_COMET}
-comet_api_key: ${COMET_API_KEY}
-comet_workspace: ${COMET_WORKSPACE}
-comet_project_name: ${COMET_PROJECT_NAME}
-comet_experiment_key: ${COMET_EXPERIMENT_KEY}
-comet_mode: ${COMET_MODE}
-comet_online: ${COMET_ONLINE}
-comet_experiment_config: ${COMET_EXPERIMENT_CONFIG}
-
-output_dir: ${OUTPUT_DIR}
-
-torch_compile: ${TORCH_COMPILE}
-torch_compile_backend: ${TORCH_COMPILE_BACKEND}
-
-gradient_accumulation_steps: ${GRADIENT_ACCUMULATION_STEPS}
-micro_batch_size: ${MICRO_BATCH_SIZE}
-eval_batch_size: ${EVAL_BATCH_SIZE}
-num_epochs: ${NUM_EPOCHS}
-warmup_steps: ${WARMUP_STEPS}
-warmup_ratio: ${WARMUP_RATIO}
-learning_rate: ${LEARNING_RATE}
-lr_quadratic_warmup: ${LR_QUADRATIC_WARMUP}
-logging_steps: ${LOGGING_STEPS}
-eval_steps: ${EVAL_STEPS}
-evals_per_epoch: ${EVALS_PER_EPOCH}
-save_strategy: ${SAVE_STRATEGY}
-save_steps: ${SAVE_STEPS}
-saves_per_epoch: ${SAVES_PER_EPOCH}
-save_total_limit: ${SAVE_TOTAL_LIMIT}
-max_steps: ${MAX_STEPS}
-
-eval_table_size: ${EVAL_TABLE_SIZE}
-eval_max_new_tokens: ${EVAL_MAX_NEW_TOKENS}
-eval_causal_lm_metrics: ${EVAL_CAUSAL_LM_METRICS}
-
-profiler_steps: ${PROFILER_STEPS}
-loss_watchdog_threshold: ${LOSS_WATCHDOG_THRESHOLD}
-loss_watchdog_patience: ${LOSS_WATCHDOG_PATIENCE}
-
-save_safetensors: ${SAVE_SAFETENSORS}
-train_on_inputs: ${TRAIN_ON_INPUTS}
-group_by_length: ${GROUP_BY_LENGTH}
-gradient_checkpointing: ${GRADIENT_CHECKPOINTING}
-early_stopping_patience: ${EARLY_STOPPING_PATIENCE}
-
-lr_scheduler: ${LR_SCHEDULER}
-lr_scheduler_kwargs: ${LR_SCHEDULER_KWARGS}
-cosine_min_lr_ratio: ${COSINE_MIN_LR_RATIO}
-cosine_constant_lr_ratio: ${COSINE_CONSTANT_LR_RATIO}
-lr_div_factor: ${LR_DIV_FACTOR}
-
-optimizer: ${OPTIMIZER}
-optim_args: ${OPTIM_ARGS}
-optim_target_modules: ${OPTIM_TARGET_MODULES}
-weight_decay: ${WEIGHT_DECAY}
-adam_beta1: ${ADAM_BETA1}
-adam_beta2: ${ADAM_BETA2}
-adam_epsilon: ${ADAM_EPSILON}
-max_grad_norm: ${MAX_GRAD_NORM}
-
-neftune_noise_alpha: ${NEFTUNE_NOISE_ALPHA}
-
-flash_optimum: ${FLASH_OPTIMUM}
-xformers_attention: ${XFORMERS_ATTENTION}
-flash_attention: ${FLASH_ATTENTION}
-flash_attn_cross_entropy: ${FLASH_ATTN_CROSS_ENTROPY}
-flash_attn_rms_norm: ${FLASH_ATTN_RMS_NORM}
-flash_attn_fuse_qkv: ${FLASH_ATTN_FUSE_QKV}
-flash_attn_fuse_mlp: ${FLASH_ATTN_FUSE_MLP}
-sdp_attention: ${SDP_ATTENTION}
-s2_attention: ${S2_ATTENTION}
-resume_from_checkpoint: ${RESUME_FROM_CHECKPOINT}
-auto_resume_from_checkpoints: ${AUTO_RESUME_FROM_CHECKPOINTS}
-
-local_rank: ${LOCAL_RANK}
-
-special_tokens:
-  bos_token: ${SPECIAL_TOKEN_BOS}
-  eos_token: ${SPECIAL_TOKEN_EOS}
-  unk_token: ${SPECIAL_TOKEN_UNK}
-  pad_token: ${SPECIAL_TOKEN_PAD}
-
-tokens: ${TOKENS}
-
-fsdp: ${FSDP}
-fsdp_config: ${FSDP_CONFIG}
-deepspeed: ${DEEPSPEED}
-
-ddp_timeout: ${DDP_TIMEOUT}
-ddp_bucket_cap_mb: ${DDP_BUCKET_CAP_MB}
-ddp_broadcast_buffers: ${DDP_BROADCAST_BUFFERS}
-
-torchdistx_path: ${TORCHDISTX_PATH}
-pretraining_dataset: ${PRETRAINING_DATASET}
-debug: ${DEBUG}
-seed: ${SEED}
-strict: ${STRICT}
--- a/.runpod/src/handler.py
+++ b/.runpod/src/handler.py
@@ -1,64 +0,0 @@
-"""
-Runpod serverless entrypoint handler
-"""
-
-import os
-
-import runpod
-import yaml
-from huggingface_hub._login import login
-from train import train
-from utils import get_output_dir
-
-BASE_VOLUME = os.environ.get("BASE_VOLUME", "/runpod-volume")
-if not os.path.exists(BASE_VOLUME):
-    os.makedirs(BASE_VOLUME)
-
-logger = runpod.RunPodLogger()
-
-
-async def handler(job):
-    runpod_job_id = job["id"]
-    inputs = job["input"]
-    run_id = inputs.get("run_id", "default_run_id")
-    args = inputs.get("args", {})
-
-    # Set output directory
-    output_dir = os.path.join(BASE_VOLUME, get_output_dir(run_id))
-    args["output_dir"] = output_dir
-
-    # First save args to a temporary config file
-    config_path = "/workspace/test_config.yaml"
-
-    # Add run_name and job_id to args before saving
-    args["run_name"] = run_id
-    args["runpod_job_id"] = runpod_job_id
-
-    yaml_data = yaml.dump(args, default_flow_style=False)
-    with open(config_path, "w", encoding="utf-8") as file:
-        file.write(yaml_data)
-
-    # Handle credentials
-    credentials = inputs.get("credentials", {})
-
-    if "wandb_api_key" in credentials:
-        os.environ["WANDB_API_KEY"] = credentials["wandb_api_key"]
-    if "hf_token" in credentials:
-        os.environ["HF_TOKEN"] = credentials["hf_token"]
-
-    if os.environ.get("HF_TOKEN"):
-        login(token=os.environ["HF_TOKEN"])
-    else:
-        logger.info("No HF_TOKEN provided. Skipping login.")
-
-    logger.info("Starting Training.")
-    async for result in train(config_path):  # Pass the config path instead of args
-        logger.info(result)
-    logger.info("Training Complete.")
-
-    # Cleanup
-    del os.environ["WANDB_API_KEY"]
-    del os.environ["HF_TOKEN"]
-
-
-runpod.serverless.start({"handler": handler, "return_aggregate_stream": True})
--- a/.runpod/src/test_input.json
+++ b/.runpod/src/test_input.json
@@ -1,61 +0,0 @@
-{
-  "input": {
-    "user_id": "user",
-    "model_id": "llama-test",
-    "run_id": "llama-test",
-    "credentials": {
-      "wandb_api_key": "",
-      "hf_token": ""
-    },
-    "args": {
-      "base_model": "NousResearch/Meta-Llama-3-8B",
-      "model_type": "LlamaForCausalLM",
-      "tokenizer_type": "AutoTokenizer",
-      "load_in_8bit": true,
-      "load_in_4bit": false,
-      "strict": false,
-      "datasets": [
-        {
-          "path": "mhenrichsen/alpaca_2k_test",
-          "type": "alpaca"
-        }
-      ],
-      "val_set_size": 0.05,
-      "output_dir": "./outputs/lora-out",
-      "sequence_len": 4096,
-      "sample_packing": true,
-      "eval_sample_packing": false,
-      "pad_to_sequence_len": true,
-      "adapter": "lora",
-      "lora_r": 32,
-      "lora_alpha": 16,
-      "lora_dropout": 0.05,
-      "lora_target_linear": true,
-      "lora_modules_to_save": [
-        "embed_tokens",
-        "lm_head"
-      ],
-      "gradient_accumulation_steps": 4,
-      "micro_batch_size": 2,
-      "num_epochs": 1,
-      "optimizer": "adamw_bnb_8bit",
-      "lr_scheduler": "cosine",
-      "learning_rate": 0.0002,
-      "train_on_inputs": false,
-      "group_by_length": false,
-      "bf16": "auto",
-      "tf32": false,
-      "gradient_checkpointing": true,
-      "logging_steps": 1,
-      "flash_attention": true,
-      "warmup_steps": 1,
-      "evals_per_epoch": 1,
-      "eval_max_new_tokens": 128,
-      "saves_per_epoch": 1,
-      "weight_decay": 0.0,
-      "special_tokens": {
-        "pad_token": "<|end_of_text|>"
-      }
-    }
-  }
-}
--- a/.runpod/src/train.py
+++ b/.runpod/src/train.py
@@ -1,45 +0,0 @@
-"""
-Runpod train entrypoint
-"""
-
-import asyncio
-
-
-async def train(config_path: str, gpu_id: str = "0", preprocess: bool = True):
-    """
-    Run preprocessing (if enabled) and training with the given config file
-    :param config_path: Path to the YAML config file
-    :param gpu_id: GPU ID to use (default: "0")
-    :param preprocess: Whether to run preprocessing (default: True)
-
-    """
-    # First check if preprocessing is needed
-    if preprocess:
-        # Preprocess command
-        preprocess_cmd = (
-            f"CUDA_VISIBLE_DEVICES={gpu_id} axolotl preprocess {config_path}"
-        )
-        process = await asyncio.create_subprocess_shell(
-            preprocess_cmd,
-            stdout=asyncio.subprocess.PIPE,
-            stderr=asyncio.subprocess.STDOUT,
-        )
-
-        if process.stdout is not None:
-            async for line in process.stdout:
-                yield f"Preprocessing: {line.decode().strip()}"
-        await process.wait()
-        yield "Preprocessing completed."
-    else:
-        yield "Skipping preprocessing step."
-
-    # Training command
-    train_cmd = f"axolotl train {config_path}"
-    process = await asyncio.create_subprocess_shell(
-        train_cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.STDOUT
-    )
-
-    if process.stdout is not None:
-        async for line in process.stdout:
-            yield f"Training: {line.decode().strip()}"
-    await process.wait()
--- a/.runpod/src/utils.py
+++ b/.runpod/src/utils.py
@@ -1,89 +0,0 @@
-"""
-Runpod launcher utils
-"""
-
-import os
-
-import yaml
-
-
-def get_output_dir(run_id):
-    path = f"fine-tuning/{run_id}"
-    return path
-
-
-def make_valid_config(input_args):
-    """
-    Creates and saves updated config file, returns the path to the new config
-    :param input_args: dict of input args
-    :return: str, path to the updated config file
-    """
-    # Load default config
-    with open("config/config.yaml", "r", encoding="utf-8") as fin:
-        all_args = yaml.safe_load(fin)
-
-    if not input_args:
-        print("No args provided, using defaults")
-    else:
-        all_args.update(input_args)
-
-    # Create updated config path
-    updated_config_path = "config/updated_config.yaml"
-
-    # Save updated config to new file
-    with open(updated_config_path, "w", encoding="utf-8") as f:
-        yaml.dump(all_args, f)
-
-    return updated_config_path
-
-
-def set_config_env_vars(args: dict):
-    """
-    Convert API arguments into environment variables.
-    Handles nested dictionaries, lists, and special values.
-
-    Args:
-        args (dict): The arguments dictionary from the API request
-    """
-
-    def process_value(value):
-        """Convert Python values to string format for environment variables"""
-        if value is None:
-            return ""
-        if isinstance(value, bool):
-            return str(value).lower()
-        if isinstance(value, (list, dict)):
-            return str(value)
-        return str(value)
-
-    def set_env_vars(data, prefix=""):
-        """Recursively set environment variables from nested dictionary"""
-        for key, value in data.items():
-            env_key = prefix + key.upper()
-
-            # Handle special cases
-            if isinstance(value, dict):
-                # For nested dictionaries (like special_tokens)
-                set_env_vars(value, f"{env_key}_")
-            elif isinstance(value, list):
-                # Handle list of dictionaries (like datasets)
-                if value and isinstance(value[0], dict):
-                    for i, item in enumerate(value):
-                        set_env_vars(item, f"{env_key}_{i}_")
-                else:
-                    # For simple lists (like lora_target_modules)
-                    os.environ[env_key] = process_value(value)
-            else:
-                # Handle all other cases
-                os.environ[env_key] = process_value(value)
-
-    # Clear any existing related environment variables
-    # This prevents old values from persisting
-    for key in list(os.environ.keys()):
-        if key.startswith(
-            ("BASE_MODEL", "MODEL_TYPE", "TOKENIZER_TYPE", "DATASET", "LORA_", "WANDB_")
-        ):
-            del os.environ[key]
-
-    # Set new environment variables
-    set_env_vars(args)
--- a/.runpod/tests.json
+++ b/.runpod/tests.json
@@ -1,89 +0,0 @@
-{
-  "tests": [
-    {
-      "name": "quick_smoke_test_sft",
-      "input": {
-        "user_id": "user",
-        "model_id": "llama-test",
-        "run_id": "llama-test",
-        "credentials": {
-          "wandb_api_key": "",
-          "hf_token": ""
-        },
-        "args": {
-          "base_model": "NousResearch/Meta-Llama-3-8B",
-          "model_type": "LlamaForCausalLM",
-          "tokenizer_type": "AutoTokenizer",
-          "load_in_8bit": true,
-          "load_in_4bit": false,
-          "strict": false,
-          "datasets": [
-            {
-              "path": "mhenrichsen/alpaca_2k_test",
-              "type": "alpaca"
-            }
-          ],
-          "val_set_size": 0.05,
-          "output_dir": "./outputs/lora-out",
-          "sequence_len": 4096,
-          "sample_packing": true,
-          "eval_sample_packing": false,
-          "pad_to_sequence_len": true,
-          "adapter": "lora",
-          "lora_r": 32,
-          "lora_alpha": 16,
-          "lora_dropout": 0.05,
-          "lora_target_linear": true,
-          "lora_modules_to_save": [
-            "embed_tokens",
-            "lm_head"
-          ],
-          "gradient_accumulation_steps": 4,
-          "micro_batch_size": 2,
-          "num_epochs": 1,
-          "optimizer": "adamw_bnb_8bit",
-          "lr_scheduler": "cosine",
-          "learning_rate": 0.0002,
-          "train_on_inputs": false,
-          "group_by_length": false,
-          "bf16": "auto",
-          "tf32": false,
-          "gradient_checkpointing": true,
-          "logging_steps": 1,
-          "flash_attention": true,
-          "warmup_steps": 1,
-          "evals_per_epoch": 1,
-          "eval_max_new_tokens": 128,
-          "saves_per_epoch": 1,
-          "weight_decay": 0.0,
-          "special_tokens": {
-            "pad_token": "<|end_of_text|>"
-          }
-        }
-      },
-      "timeout": 100000
-    }
-  ],
-  "config": {
-    "gpuTypeId": "NVIDIA GeForce RTX 4090",
-    "gpuCount": 1,
-    "containerDiskInGb": 200,
-    "env": [
-      {
-        "key": "TOKENIZER",
-        "value": ""
-      },
-      {
-        "key": "DISABLE_LOG_STATS",
-        "value": "true"
-      }
-    ],
-    "allowedCudaVersions": [
-      "12.8",
-      "12.7",
-      "12.6",
-      "12.5",
-      "12.4"
-    ]
-  }
-}
--- a/cicd/cicd.sh
+++ b/cicd/cicd.sh
@@ -52,4 +52,4 @@ pytest -v --durations=10 \
  --cov-append \
  --cov-report=xml:e2e-coverage.xml

-codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION} || true
+codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION}
--- a/codecov.yml
+++ b/codecov.yml
@@ -1,7 +1,5 @@
 codecov:
  require_ci_to_pass: yes
-  notify:
-    wait_for_ci: true

 coverage:
  precision: 2
--- a/docs/custom_integrations.qmd
+++ b/docs/custom_integrations.qmd
@@ -49,7 +49,8 @@ sections = [
    ("Knowledge Distillation (KD)", "kd"),
    ("Liger Kernels", "liger"),
    ("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
-    ("Spectrum", "spectrum")
+    ("Spectrum", "spectrum"),
+    ("LLMCompressor", "llm_compressor")
 ]

 for section_name, folder_name in sections:
--- a/docs/docker.qmd
+++ b/docs/docker.qmd
@@ -28,8 +28,6 @@ main-base-py{python_version}-cu{cuda_version}-{pytorch_version}

 Tags examples:

- `main-base-py3.11-cu128-2.7.0`
- `main-base-py3.11-cu126-2.7.0`
 - `main-base-py3.11-cu124-2.6.0`
 - `main-base-py3.11-cu124-2.5.1`
 - `main-base-py3.11-cu124-2.4.1`
@@ -52,7 +50,7 @@ Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
 # on push to main
 main-py{python_version}-cu{cuda_version}-{pytorch_version}

-# latest main (currently torch 2.6.0, python 3.11, cuda 12.4)
+# latest main (currently torch 2.5.1, python 3.11, cuda 12.4)
 main-latest

 # nightly build
@@ -70,7 +68,6 @@ There may be some extra tags appended to the image, like `-vllm` which installs

 Tags examples:

- `main-py3.11-cu126-2.7.0`
 - `main-py3.11-cu124-2.6.0`
 - `main-py3.11-cu124-2.5.1`
 - `main-py3.11-cu124-2.4.1`
--- a/examples/llama-3/sparse-finetuning.yaml
+++ b/examples/llama-3/sparse-finetuning.yaml
@@ -0,0 +1,77 @@
+base_model: neuralmagic/Sparse-Llama-3.1-8B-2of4
+
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+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|>
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true
--- a/examples/llama-4/scout-qlora-single-h100-flex.yaml
+++ b/examples/llama-4/scout-qlora-single-h100-flex.yaml
@@ -10,6 +10,7 @@ plugins:
 liger_glu_activation: true
 liger_rms_norm: true
 liger_layer_norm: true
+cut_cross_entropy: true

 llama4_linearized_experts: true  # needed with custom linearized experts model
 load_in_4bit: true
--- a/setup.py
+++ b/setup.py
@@ -149,6 +149,9 @@ extras_require = {
    "vllm": [
        "vllm==0.7.2",
    ],
+    "llmcompressor": [
+        "llmcompressor==0.5.1",
+    ],
 }

 install_requires, dependency_links, extras_require_build = parse_requirements(
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -932,6 +932,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                collator = DataCollatorForSeq2Seq

        kwargs["return_tensors"] = "pt"
+        if issubclass(collator, DataCollatorForSeq2Seq):
+            kwargs["sequence_parallel_degree"] = training_args.sequence_parallel_degree
+            kwargs["ring_attn_func"] = training_args.ring_attn_func

        return collator(
            *collator_args,
@@ -1048,9 +1051,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
        if self.cfg.rpo_alpha is not None:
            training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha

-        if self.cfg.use_wandb:
-            training_args_kwargs["run_name"] = self.cfg.wandb_name
-
        training_args_cls = None
        blocklist_args_kwargs = []
        if self.cfg.rl == "simpo":
@@ -1121,12 +1121,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
            **training_args_kwargs,
        )

-        # unset run_name so wandb sets up experiment names
-        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
-            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
-                None
-            )
-
        return training_args

    def build(self, total_num_steps):
--- a/src/axolotl/core/trainers/base.py
+++ b/src/axolotl/core/trainers/base.py
@@ -371,15 +371,13 @@ class AxolotlTrainer(
                num_items_in_batch=num_items_in_batch,
            )

-        loss = super().compute_loss(
+        return super().compute_loss(
            model,
            inputs,
            return_outputs=return_outputs,
            num_items_in_batch=num_items_in_batch,
        )

-        return loss
-
    @staticmethod
    def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
        concatenated_batch = {}
--- a/src/axolotl/core/trainers/mixins/init.py
+++ b/src/axolotl/core/trainers/mixins/init.py
@@ -6,4 +6,4 @@
 from .optimizer import OptimizerMixin
 from .rng_state_loader import RngLoaderMixin
 from .scheduler import SchedulerMixin
-from .sequence_parallel import SequenceParallelContextManager, SequenceParallelMixin
+from .sequence_parallel import SequenceParallelMixin
--- a/src/axolotl/core/trainers/mixins/sequence_parallel.py
+++ b/src/axolotl/core/trainers/mixins/sequence_parallel.py
@@ -1,86 +1,16 @@
-"""
-Module for Axolotl trainer sequence parallelism mixin and training context manager
-"""
+"""Module for Axolotl trainer sequence parallelism mixin"""

-import functools
 import logging

-import torch
 import torch.distributed as dist
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import DistributedSampler, Sampler
-from torch.utils.hooks import RemovableHandle

-from axolotl.monkeypatch.attention.ring_attn import (
-    RingAttnFunc,
-    get_ring_attn_group,
-    update_ring_attn_params,
-)
+from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group

 LOG = logging.getLogger(__name__)


-def apply_sequence_parallelism(
-    batch: dict[str, torch.Tensor],
-    local_rank: int,
-    local_world_size: int,
-    ring_attn_func: RingAttnFunc,
-) -> dict[str, torch.Tensor]:
-    """
-    Apply sequence parallelism slicing to a batch.
-
-    Args:
-        batch: Batch dictionary (e.g., input_ids, attention_mask, etc.)
-        local_rank: Local rank in the sequence parallel group
-        local_world_size: World size of the sequence parallel group
-        ring_attn_func: The ring attention function to use
-
-    Returns:
-        Sliced batch dictionary.
-    """
-    # Update ring attention params if needed
-    if batch.get("position_ids") is not None:
-        update_ring_attn_params(position_ids=batch["position_ids"])
-
-    # Slice batch for sequence parallel processing
-    total_seq_len = batch["input_ids"].size(1)
-    for key in batch:
-        if (
-            key in batch
-            and isinstance(batch[key], torch.Tensor)
-            and batch[key].dim() > 1
-            and batch[key].size(1) == total_seq_len
-        ):
-
-            if ring_attn_func in [
-                RingAttnFunc.VARLEN_LLAMA3,
-                RingAttnFunc.BATCH_RING,
-            ]:
-                # Split in sequential fashion and grab this rank's chunk
-                batch[key] = (
-                    batch[key].chunk(local_world_size, dim=1)[local_rank].contiguous()
-                )
-            elif ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-                chunks = batch[key].chunk(2 * local_world_size, dim=1)
-
-                # Take rank's chunk and opposing chunk for zigzag pattern
-                selected_chunks = [
-                    chunks[local_rank],
-                    chunks[2 * local_world_size - local_rank - 1],
-                ]
-                batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
-            elif ring_attn_func is RingAttnFunc.BATCH_STRIPE:
-                # Split into striped data and stack
-                tensor = torch.stack(
-                    batch[key].split(local_world_size, dim=1),
-                    dim=1,
-                ).transpose(1, 2)
-                batch[key] = tensor[:, local_rank].contiguous()
-
-    return batch
-
-
 class SequenceParallelMixin:
    """
    Mixin class for sequence parallelism support in trainers.
@@ -157,157 +87,3 @@ class SequenceParallelMixin:
        return self._create_sequence_parallel_sampler(
            eval_dataset, shuffle=False, is_eval=True
        )
-
-
-class SequenceParallelContextManager:
-    """
-    Context manager for sequence parallelism operations.
-
-    This class provides a context that will automatically apply sequence parallelism
-    during model forward passes using a pre-forward hook, and gather outputs from
-    across the sequence parallelism group using a post-forward hook.
-    """
-
-    def __init__(
-        self,
-        model: nn.Module,
-        sequence_parallel_degree: int,
-        ring_attn_func: RingAttnFunc,
-    ):
-        self.model = model
-        self.sequence_parallel_degree = sequence_parallel_degree
-        self.ring_attn_func = ring_attn_func
-        self.process_group = get_ring_attn_group()
-
-        # Initialize sequence parallel group details
-        self.local_rank = dist.get_rank(self.process_group)
-        self.local_world_size = dist.get_world_size(self.process_group)
-
-        # Will store hook handles for removal
-        self.hook_handles: list[RemovableHandle] = []
-
-        # Create a partially applied version of the apply_sequence_parallelism function
-        # with pre-configured params
-        self.apply_sequence_parallelism = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=self.local_rank,
-            local_world_size=self.local_world_size,
-            ring_attn_func=self.ring_attn_func,
-        )
-
-    def __enter__(self):
-        # Forward pre-hook to apply sequence parallelism
-        def sequence_parallel_pre_hook(_, args, kwargs):
-            # Apply sequence parallelism to kwargs
-            kwargs = self.apply_sequence_parallelism(batch=kwargs)
-            return args, kwargs
-
-        # Forward post-hook to gather outputs
-        def sequence_parallel_post_hook(_, __, output):
-            # Gather the sharded outputs
-            return self.gather_outputs(output)
-
-        # Register both hooks
-        self.hook_handles.append(
-            self.model.register_forward_pre_hook(
-                sequence_parallel_pre_hook, with_kwargs=True
-            )
-        )
-        self.hook_handles.append(
-            self.model.register_forward_hook(sequence_parallel_post_hook)
-        )
-
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        # Remove all hooks
-        for handle in self.hook_handles:
-            handle.remove()
-        self.hook_handles = []
-
-    def gather_outputs(self, output):
-        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
-        # Handle different output formats (dict, tensor, etc.)
-        if isinstance(output, dict):
-            gathered_output = {}
-            for key, value in output.items():
-                if isinstance(value, torch.Tensor) and value.dim() > 1:
-                    # Gather logits or other sequence-sharded tensors
-                    gathered_value = self.gather_tensor(value)
-                    gathered_output[key] = gathered_value
-                else:
-                    gathered_value = value.clone()
-                    dist.all_reduce(
-                        gathered_value, op=dist.ReduceOp.SUM, group=self.process_group
-                    )
-                    gathered_output[key] = gathered_value
-            return gathered_output
-        if isinstance(output, torch.Tensor):
-            return self.gather_tensor(output)
-
-        return output
-
-    def gather_tensor(self, tensor):
-        """Gather a sharded tensor from all ranks."""
-        # Prepare tensors for all_gather
-        world_size = self.local_world_size
-
-        # Create list to store tensors from all ranks
-        gathered_tensors = [torch.zeros_like(tensor) for _ in range(world_size)]
-
-        # All-gather operation
-        dist.all_gather(gathered_tensors, tensor, group=self.process_group)
-
-        # Concatenate along sequence dimension (typically dim=1)
-        if self.ring_attn_func in [RingAttnFunc.VARLEN_LLAMA3, RingAttnFunc.BATCH_RING]:
-            # Simple concatenation for standard sharding
-            return torch.cat(gathered_tensors, dim=1)
-
-        if self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-            # Each rank has a pattern of (rank, world_size*2-rank-1)
-            reconstituted_tensors = [None] * (world_size * 2)
-
-            # First, split each gathered tensor into its two chunks
-            for rank, gathered_tensor in enumerate(gathered_tensors):
-                # Each tensor contains two chunks in the sequence dimension
-                chunk_size = gathered_tensor.size(1) // 2
-                chunk1, chunk2 = gathered_tensor.split(chunk_size, dim=1)
-
-                # Place chunks in their original positions
-                reconstituted_tensors[rank] = chunk1
-                reconstituted_tensors[world_size * 2 - rank - 1] = chunk2
-
-            # Concatenate the reconstituted tensors in the correct order
-            return torch.cat(reconstituted_tensors, dim=1)
-
-        # Otherwise, RingAttnFunc.BATCH_STRIPE
-        # In striping, each rank has every world_size-th slice
-        batch_size = tensor.size(0)
-        hidden_dim = tensor.size(-1)
-
-        # First, determine the full sequence length
-        total_seq_len = 0
-        for t in gathered_tensors:
-            total_seq_len += t.size(1)
-
-        # Create a tensor to hold the unstriped result
-        result = torch.zeros(
-            batch_size,
-            total_seq_len,
-            hidden_dim,
-            dtype=tensor.dtype,
-            device=tensor.device,
-        )
-
-        # For each rank's tensor, distribute its slices to the correct positions
-        for rank, gathered_tensor in enumerate(gathered_tensors):
-            # The rank's tensor contains every world_size-th slice
-            # starting from its rank position
-            seq_len = gathered_tensor.size(1)
-            for i in range(seq_len):
-                # Calculate the position in the full tensor
-                pos = i * world_size + rank
-                if pos < total_seq_len:
-                    result[:, pos] = gathered_tensor[:, i]
-
-        return result
--- a/src/axolotl/integrations/cut_cross_entropy/README.md
+++ b/src/axolotl/integrations/cut_cross_entropy/README.md
@@ -27,6 +27,8 @@ pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transform
 ```yaml
 plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+cut_cross_entropy: true
 ```

 ## Supported Models
--- a/src/axolotl/integrations/cut_cross_entropy/args.py
+++ b/src/axolotl/integrations/cut_cross_entropy/args.py
@@ -28,7 +28,7 @@ class CutCrossEntropyArgs(BaseModel):
    Input args for Cut Cross Entropy.
    """

-    cut_cross_entropy: Optional[bool] = True
+    cut_cross_entropy: Optional[bool] = None

    @model_validator(mode="before")
    @classmethod
--- a/src/axolotl/integrations/llm_compressor/README.md
+++ b/src/axolotl/integrations/llm_compressor/README.md
@@ -0,0 +1,108 @@
+# LLMCompressor Integration
+
+Fine-tune sparsified models in Axolotl using Neural Magic's [LLMCompressor](https://github.com/vllm-project/llm-compressor).
+
+This integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor's model compression capabilities with Axolotl's distributed training pipelines, users can efficiently fine-tune sparse models at scale.
+
+It uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.
+
+---
+
+## Requirements
+
+- Axolotl with `llmcompressor` extras:
+
+  ```bash
+  pip install "axolotl[llmcompressor]"
+  ```
+
+- Requires `llmcompressor >= 0.5.1`
+
+This will install all necessary dependencies to fine-tune sparsified models using the integration.
+
+---
+
+## Usage
+
+To enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:
+
+```yaml
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true
+# ... (other training arguments)
+```
+
+This plugin **does not apply pruning or sparsification itself** — it is intended for **fine-tuning models that have already been sparsified**.
+
+Pre-sparsified checkpoints can be:
+- Generated using [LLMCompressor](https://github.com/vllm-project/llm-compressor)
+- Downloaded from [Neural Magic's Hugging Face page](https://huggingface.co/neuralmagic)
+- Any custom LLM with compatible sparsity patterns that you've created yourself
+
+To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:
+[https://github.com/vllm-project/llm-compressor/blob/main/README.md](https://github.com/vllm-project/llm-compressor/blob/main/README.md)
+
+### Storage Optimization with save_compressed
+
+Setting `save_compressed: true` in your configuration enables saving models in a compressed format, which:
+- Reduces disk space usage by approximately 40%
+- Maintains compatibility with vLLM for accelerated inference
+- Maintains compatibility with llmcompressor for further optimization (example: quantization)
+
+This option is highly recommended when working with sparse models to maximize the benefits of model compression.
+
+### Example Config
+
+See [`examples/llama-3/sparse-finetuning.yaml`](examples/llama-3/sparse-finetuning.yaml) for a complete example.
+
+---
+
+## Inference with vLLM
+
+After fine-tuning your sparse model, you can leverage vLLM for efficient inference.
+You can also use LLMCompressor to apply additional quantization to your fine-tuned
+sparse model before inference for even greater performance benefits.:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+llm = LLM("path/to/your/sparse/model")
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+For more details on vLLM's capabilities and advanced configuration options, see the [official vLLM documentation](https://docs.vllm.ai/).
+
+## Learn More
+
+For details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:
+
+[https://github.com/vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor)
--- a/src/axolotl/integrations/llm_compressor/init.py
+++ b/src/axolotl/integrations/llm_compressor/init.py
@@ -0,0 +1,5 @@
+"""Integration entry point for the LLMCompressor plugin."""
+
+from .plugin import LLMCompressorPlugin
+
+__all__ = ["LLMCompressorPlugin"]
--- a/src/axolotl/integrations/llm_compressor/args.py
+++ b/src/axolotl/integrations/llm_compressor/args.py
@@ -0,0 +1,40 @@
+"""
+LLMCompressor and Sparse Finetuning config models.
+"""
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+from typing_extensions import Annotated
+
+
+class CompressionArgs(BaseModel):
+    """Sparse Finetuning config for LLMCompressor."""
+
+    # Typing for recipe is set to Any due to:
+    # https://github.com/vllm-project/llm-compressor/issues/1319
+    recipe: Annotated[
+        Any,
+        Field(
+            description="The recipe containing the compression algorithms and hyperparameters to apply."
+        ),
+    ]
+
+    save_compressed: Annotated[
+        bool,
+        Field(
+            default=False,
+            description="Whether to save the compressed model after training.",
+        ),
+    ]
+
+
+class LLMCompressorArgs(BaseModel):
+    """LLMCompressor configuration BaseModel."""
+
+    llmcompressor: Annotated[
+        CompressionArgs,
+        Field(
+            description="Arguments enabling compression pathways through the LLM Compressor plugins"
+        ),
+    ]
--- a/src/axolotl/integrations/llm_compressor/plugin.py
+++ b/src/axolotl/integrations/llm_compressor/plugin.py
@@ -0,0 +1,171 @@
+"""
+Sparse Finetuning plugin for Axolotl — enables handling of sparse neural networks
+by maintaining masks for zero weights during training.
+"""
+
+import logging
+from functools import wraps
+from typing import Any, Callable, Concatenate, ParamSpec, TypeVar
+
+from llmcompressor import active_session, create_session
+from llmcompressor.core import callbacks as session_callbacks
+from llmcompressor.recipe import Recipe
+from torch.nn import Module
+from transformers.trainer import Trainer
+from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
+from transformers.training_args import TrainingArguments
+
+from axolotl.integrations.base import BasePlugin
+
+P = ParamSpec("P")  # Params for generic function signatures
+R = TypeVar("R")  # Return type for generic function signatures
+
+LOG = logging.getLogger("axolotl.integrations.llm_compressor")
+
+
+class LLMCompressorCallbackHandler(TrainerCallback):
+    """
+    Trainer callback for Sparse Finetuning.
+    Maintains sparsity patterns during training by applying masks after optimization steps,
+    ensuring zero-weight updates are canceled out.
+    """
+
+    def __init__(self, trainer: Trainer, recipe: Any):
+        """
+        Initialize the Sparse Finetuning callback handler.
+
+        Args:
+            trainer (Trainer): Huggingface Trainer instance.
+            recipe (Recipe | dict): Sparse finetuning recipe to apply.
+        """
+        super().__init__()
+        self.trainer = trainer
+        self.recipe = (
+            Recipe.model_validate(recipe) if not isinstance(recipe, Recipe) else recipe
+        )
+        self.original_compute_loss = trainer.compute_loss
+        self.trainer.compute_loss = compute_loss_wrapper(self.trainer.compute_loss)
+        create_session()
+
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the beginning of training. Initializes the compression session.
+
+        Args:
+            args (TrainingArguments): Training arguments.
+            state (TrainerState): Trainer state.
+            control (TrainerControl): Trainer control.
+        """
+        super().on_train_begin(args, state, control, **kwargs)
+        self.trainer.accelerator.wait_for_everyone()
+        active_session().initialize(
+            model=self.trainer.model,
+            optimizer=self.trainer.optimizer,
+            start=state.epoch,
+            recipe=self.recipe,
+        )
+        self.trainer.accelerator.wait_for_everyone()
+
+    def on_step_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the beginning of a training step. Triggers batch_start callback.
+        """
+        super().on_step_begin(args, state, control, **kwargs)
+        session_callbacks.batch_start()
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the end of a training step. Triggers optimizer and batch_end callbacks.
+        """
+        super().on_step_end(args, state, control, **kwargs)
+        session_callbacks.optim_pre_step()
+        session_callbacks.optim_post_step()
+        session_callbacks.batch_end()
+
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the end of training. Finalizes the compression session.
+        """
+        super().on_train_end(args, state, control, **kwargs)
+        active_session().finalize()
+        self.trainer.compute_loss_func = self.original_compute_loss
+
+
+class LLMCompressorPlugin(BasePlugin):
+    """
+    Sparse Finetuning plugin for Axolotl integration.
+    """
+
+    def get_input_args(self) -> str:
+        """
+        Returns the path to the plugin's argument definition.
+
+        Returns:
+            str: Dotted path to the LLMCompressorArgs class.
+        """
+        return "axolotl.integrations.llm_compressor.args.LLMCompressorArgs"
+
+    def add_callbacks_post_trainer(self, cfg: Any, trainer: Trainer) -> list:
+        """
+        Adds Sparse Finetuning callback to the Trainer instance.
+
+        Args:
+            cfg (Any): Configuration object containing the sparse recipe.
+            trainer (Trainer): Huggingface Trainer instance.
+
+        Returns:
+            list: List containing the configured callback instances.
+        """
+        LOG.info("Adding Sparse Finetuning callback to the trainer")
+        callback = LLMCompressorCallbackHandler(
+            trainer=trainer,
+            recipe=cfg.llmcompressor.recipe,
+        )
+        return [callback]
+
+
+def compute_loss_wrapper(
+    compute_loss_func: Callable[Concatenate[Module, P], R],
+) -> Callable[Concatenate[Module, P], R]:
+    """
+    Wraps the loss computation function to trigger the loss_calculated callback.
+
+    Args:
+        compute_loss_func (Callable): Original loss computation function.
+
+    Returns:
+        Callable: Wrapped function that also invokes the loss_calculated callback.
+    """
+
+    @wraps(compute_loss_func)
+    def compute_and_notify(model: Module, *args: P.args, **kwargs: P.kwargs) -> R:
+        loss = compute_loss_func(model, *args, **kwargs)
+        if active_session().lifecycle.initialized_ and model.training:
+            session_callbacks.loss_calculated(loss=loss)
+        return loss
+
+    return compute_and_notify
--- a/src/axolotl/integrations/llm_compressor/utils.py
+++ b/src/axolotl/integrations/llm_compressor/utils.py
@@ -0,0 +1,40 @@
+"""Utilities for llmcompressor integration with axolotl."""
+
+from typing import Union
+
+from llmcompressor.transformers.sparsification.compressed_tensors_utils import (
+    modify_save_pretrained,
+)
+from transformers import PreTrainedModel, Trainer
+
+
+def save_compressed_model(
+    model: PreTrainedModel,
+    output_dir: Union[str, bytes],
+    trainer: Trainer,
+    safe_serialization: bool = False,
+    save_compressed: bool = False,
+) -> None:
+    """
+    Synchronize processes, apply compression hooks, and save the model.
+
+    Args:
+        model (PreTrainedModel): The model to be saved.
+        output_dir (str or bytes): Path where the model files will be written.
+        trainer (Trainer): Hugging Face Trainer for process synchronization.
+        safe_serialization (bool): Use safe serialization if True.
+        save_compressed (bool): Write compressed tensors if True.
+    """
+    trainer.accelerator.wait_for_everyone()
+
+    # Only the main process writes the files
+    if not trainer.accelerator.is_main_process:
+        return
+
+    modify_save_pretrained(model)
+    model.save_pretrained(
+        output_dir,
+        safe_serialization=safe_serialization,
+        save_compressed=save_compressed,
+        skip_sparsity_compression_stats=not save_compressed,
+    )
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -6,7 +6,6 @@ import os
 import signal
 import sys
 import weakref
-from contextlib import nullcontext
 from pathlib import Path
 from typing import Any, Dict

@@ -26,9 +25,6 @@ from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
    fix_untrained_tokens,
 )
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
-from axolotl.core.trainers.mixins.sequence_parallel import (
-    SequenceParallelContextManager,
-)
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
@@ -189,28 +185,16 @@ def execute_training(
        trainer: The configured trainer object.
        resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
    """
-    # Define the context managers to use
-    flash_context = (
-        torch.backends.cuda.sdp_kernel(
+    LOG.info("Starting trainer...")
+    if cfg.flash_optimum:
+        with torch.backends.cuda.sdp_kernel(
+            # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
            enable_flash=True,
            enable_math=True,
            enable_mem_efficient=True,
-        )
-        if cfg.flash_optimum
-        else nullcontext()
-    )
-    sequence_parallel_context = (
-        SequenceParallelContextManager(
-            model=trainer.model,
-            sequence_parallel_degree=cfg.sequence_parallel_degree,
-            ring_attn_func=cfg.ring_attn_func,
-        )
-        if cfg.sequence_parallel_degree > 1
-        else nullcontext()
-    )
-
-    LOG.info("Starting trainer...")
-    with flash_context, sequence_parallel_context:
+        ):
+            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    else:
        trainer.train(resume_from_checkpoint=resume_from_checkpoint)


@@ -287,6 +271,19 @@ def save_trained_model(
                os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
            except FileNotFoundError:
                pass
+    elif hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
+        from axolotl.integrations.llm_compressor.utils import (
+            save_compressed_model,
+        )
+
+        save_compressed_model(
+            model=model,
+            output_dir=cfg.output_dir,
+            trainer=trainer,
+            safe_serialization=safe_serialization,
+            save_compressed=cfg.llmcompressor.save_compressed,
+        )
+
    elif cfg.local_rank == 0:
        if cfg.flash_optimum and BetterTransformer:
            model = BetterTransformer.reverse(model)
@@ -295,6 +292,7 @@ def save_trained_model(
            trainer.model.save_pretrained(
                cfg.output_dir, safe_serialization=safe_serialization
            )
+
        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)


--- a/src/axolotl/utils/collators/batching.py
+++ b/src/axolotl/utils/collators/batching.py
@@ -1,12 +1,20 @@
-"""Data collators for axolotl to pad labels and position_ids for packed sequences"""
+"""
+Data collators for axolotl to pad labels and position_ids for packed sequences. Also
+includes logic for handling sequence parallelism collation.
+"""

 from dataclasses import dataclass
 from typing import Any

 import numpy as np
+import torch
+import torch.distributed as dist
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy

+from axolotl.monkeypatch.attention.ring_attn import update_ring_attn_params
+from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
+

@dataclass
 class DataCollatorForSeq2Seq:
@@ -41,6 +49,8 @@ class DataCollatorForSeq2Seq:
            The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
        return_tensors (`str`):
            The type of Tensor to return. Allowable values are "np", "pt" and "tf".
+        sequence_parallel_degree (`int`):
+            The degree of sequence parallelism. Default to 1 for no sequence parallelism.
    """

    tokenizer: PreTrainedTokenizerBase
@@ -51,6 +61,17 @@ class DataCollatorForSeq2Seq:
    label_pad_token_id: int = -100
    position_pad_token_id: int = 0
    return_tensors: str = "pt"
+    sequence_parallel_degree: int = 1
+    ring_attn_func: RingAttnFunc | None = None
+
+    def __post_init__(self):
+        if self.sequence_parallel_degree > 1:
+            from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+
+            # Get information about our position in the SP group
+            sp_group = get_ring_attn_group()
+            self.local_rank = dist.get_rank(group=sp_group)
+            self.local_world_size = dist.get_world_size(group=sp_group)

    def __call__(self, features, return_tensors=None):
        has_attn_mask = "attention_mask" in features[0].keys()
@@ -120,8 +141,62 @@ class DataCollatorForSeq2Seq:
            )
            features["decoder_input_ids"] = decoder_input_ids

+        if self.sequence_parallel_degree > 1:
+            features = self.apply_sequence_parallelism(features)
+
        return features

+    def apply_sequence_parallelism(
+        self, batch: dict[str, torch.Tensor]
+    ) -> torch.Tensor:
+        """
+        Apply sequence parallelism slicing to a batch.
+
+        Args:
+            batch: Batch dictionary from parent collator.
+
+        Returns:
+            Sliced batch dictionary.
+        """
+        # Get local (start, end) for sequence parallelism slicing
+        total_seq_len = batch["input_ids"].size(1)
+
+        # Update params for varlen ring attention calculation
+        if batch.get("position_ids") is not None:
+            update_ring_attn_params(position_ids=batch["position_ids"])
+
+        # Slice batch for sequence parallel processing
+        for key in batch:
+            if batch[key].size(1) == total_seq_len:
+                if self.ring_attn_func in [
+                    RingAttnFunc.VARLEN_LLAMA3,
+                    RingAttnFunc.BATCH_RING,
+                ]:
+                    batch[key] = (
+                        batch[key]
+                        .chunk(self.local_world_size, dim=1)[self.local_rank]
+                        .contiguous()
+                    )
+                elif self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
+                    chunks = batch[key].chunk(2 * self.local_world_size, dim=1)
+
+                    # Take rank's chunk and opposing chunk for zigzag pattern
+                    selected_chunks = [
+                        chunks[self.local_rank],
+                        chunks[2 * self.local_world_size - self.local_rank - 1],
+                    ]
+                    batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
+                elif self.ring_attn_func is RingAttnFunc.BATCH_STRIPE:
+                    # TODO(djsaunde): This doesn't seem to work as expected
+                    # Split into striped data and stack
+                    tensor = torch.stack(
+                        batch[key].split(self.local_world_size, dim=1),
+                        dim=1,
+                    ).transpose(1, 2)
+                    batch[key] = tensor[:, self.local_rank].contiguous()
+
+        return batch
+

@dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
--- a/src/axolotl/utils/config/init.py
+++ b/src/axolotl/utils/config/init.py
@@ -126,6 +126,9 @@ def normalize_config(cfg):
            with open(ds_config_path, encoding="utf-8") as f:
                cfg.deepspeed = json.load(f)

+    if cfg.sequence_parallel_degree is None:
+        cfg.sequence_parallel_degree = 1
+
    if cfg.saves_per_epoch:
        save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
        if save_steps < 1.0:  # prevent saves on every step
--- a/src/axolotl/utils/data/sft.py
+++ b/src/axolotl/utils/data/sft.py
@@ -134,9 +134,10 @@ def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
                    "csv", data_files=f.name, split="train", streaming=True
                )
        else:
-            iter_ds = load_dataset(
-                path, streaming=True, split=split, name=name, data_files=data_files
-            )
+            if is_local_main_process():
+                iter_ds = load_dataset(
+                    path, streaming=True, split=split, name=name, data_files=data_files
+                )

        if skip:
            LOG.info(f"Skipping {skip} samples from the dataset")
--- a/src/axolotl/utils/gradient_checkpointing/init.py
+++ b/src/axolotl/utils/gradient_checkpointing/init.py
@@ -1,7 +1,5 @@
 """custom checkpointing utils"""

-from functools import partial
-
 from axolotl.utils.gradient_checkpointing.unsloth import (
    Unsloth_Offloaded_Gradient_Checkpointer,
 )
@@ -11,10 +9,6 @@ def hf_grad_checkpoint_offload_wrapper(
    decoder_layer, *args, use_reentrant=None
 ):  # pylint: disable=unused-argument
    return Unsloth_Offloaded_Gradient_Checkpointer.apply(
-        (
-            decoder_layer.func.__self__
-            if isinstance(decoder_layer, partial)
-            else decoder_layer.__self__
-        ),
+        decoder_layer.__self__,
        *args,
    )
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -139,6 +139,22 @@ def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
        hasattr(model_config, "quantization_config")
        and model_config.quantization_config
    )
+
+    # Detect compressed-tensors config
+    is_compressed_tensors_config = (
+        quant_config_exists
+        and model_config.quantization_config.get("quant_method") == "compressed-tensors"
+    )
+
+    if is_compressed_tensors_config:
+        if model_config.quantization_config.get("config_groups"):
+            LOG.warning(
+                "Found `config_groups` in a compressed-tensors config. "
+                "QAT integration with llmcompressor is not tested."
+            )
+        # Skip further quant checks for compressed-tensors
+        return
+
    quant_config_method_is_gptq = (
        quant_config_exists
        and "quant_method" in model_config.quantization_config
--- a/src/axolotl/utils/schemas/config.py
+++ b/src/axolotl/utils/schemas/config.py
@@ -1149,17 +1149,22 @@ class AxolotlInputConfig(

        return data

-    @model_validator(mode="after")
-    def check_sequence_parallel_degree(self):
-        if not self.sequence_parallel_degree:
-            self.sequence_parallel_degree = 1
-        elif self.sequence_parallel_degree > 1:
-            if not self.flash_attention:
+    @field_validator("sequence_parallel_degree", mode="after")
+    @classmethod
+    def check_sequence_parallel_degree(cls, value, info):
+        if not value:
+            value = 1
+
+        if value > 1:
+            if not info.data.get("flash_attention"):
                raise ValueError(
                    "flash_attention: true must be set with sequence_parallel_degree > 1"
                )

-            if self.sample_packing and self.micro_batch_size > 1:
+            if (
+                info.data.get("sample_packing")
+                and not info.data["micro_batch_size"] == 1
+            ):
                raise ValueError(
                    "micro_batch_size must be set to 1 when sample_packing is enabled"
                    "due to a `ring-flash-attn` requirement"
@@ -1179,40 +1184,42 @@ class AxolotlInputConfig(
            # according to the proportion of non-padding tokens per rank.
            LOG.warning(
                "Sequence parallelism (SP) is enabled with "
-                f"sequence_parallel_degree={self.sequence_parallel_degree}. "
-                "Please note that logged losses may differ slightly to the non-SP "
-                "losses due to transformers Trainer implementation details. "
-                "Please see https://github.com/axolotl-ai-cloud/axolotl/pull/2495#issuecomment-2784022042 "
+                f"sequence_parallel_degree={value}. Please note that logged losses may "
+                "differ slightly to the non-SP losses due to transformers Trainer "
+                "implementation details. Please see "
+                "https://github.com/axolotl-ai-cloud/axolotl/pull/2495#issuecomment-2784022042 "
                "for more details."
            )

-        return self
+        return value

-    @model_validator(mode="after")
-    def validate_ring_attn_func(self):
-        if getattr(self, "sequence_parallel_degree", 1) == 1:
-            return self
+    @field_validator("ring_attn_func", mode="after")
+    @classmethod
+    def check_ring_attn_func(cls, value, info):
+        if not info.data.get("sequence_parallel_degree", 1) > 1:
+            return value

        from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc

-        if self.ring_attn_func is not None:
+        if value is not None:
+            # Set the ring attention function if passed in config
            valid_funcs = list(RingAttnFunc)
-            if self.ring_attn_func in valid_funcs:
-                self.ring_attn_func = RingAttnFunc(self.ring_attn_func)
+            if value in valid_funcs:
+                value = RingAttnFunc(value)
            else:
                raise ValueError(
-                    f"ring_attn_func: {self.ring_attn_func} must be in {valid_funcs}"
+                    f"ring_attn_func: {value} must be one of {valid_funcs}"
                )
        else:
            # Default ring attention function selection
-            sample_packing = getattr(self, "sample_packing", False)
-            self.ring_attn_func = (
+            sample_packing = info.data.get("sample_packing")
+            value = (
                RingAttnFunc.VARLEN_LLAMA3
                if sample_packing
                else RingAttnFunc.BATCH_RING
            )

-        return self
+        return value

    @model_validator(mode="before")
    @classmethod
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -348,7 +348,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                    load_from_cache_file=not cfg.is_preprocess,
                    desc="Add position_id column (PoSE)",
                )
-    elif cfg.sample_packing:
+    elif cfg.sample_packing or cfg.sequence_parallel_degree > 1:
        drop_long_kwargs = {}
        if filter_map_kwargs:
            drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"
@@ -358,7 +358,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
            **filter_map_kwargs,
            **drop_long_kwargs,
        )
-        if cfg.eval_sample_packing:
+        if cfg.eval_sample_packing or cfg.sequence_parallel_degree > 1:
            if eval_dataset:
                eval_dataset = eval_dataset.map(
                    add_position_ids,
@@ -528,13 +528,6 @@ def setup_torch_compile_env(cfg):
 def setup_deepspeed_env(cfg, stage=None):
    from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig

-    from axolotl.utils.distributed import distributed_state
-
-    if distributed_state and distributed_state.initialized:
-        raise RuntimeError(
-            "Distributed State already initialized before Deepspeed setup"
-        )
-
    os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
    os.environ["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = cfg.deepspeed
    if stage:
--- a/tests/e2e/integrations/test_llm_compressor.py
+++ b/tests/e2e/integrations/test_llm_compressor.py
@@ -0,0 +1,104 @@
+"""
+E2E smoke tests for LLMCompressorPlugin integration
+"""
+
+from pathlib import Path
+
+import pytest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import check_model_output_exists, require_torch_2_4_1
+
+MODELS = [
+    "nm-testing/llama2.c-stories42M-pruned2.4-compressed",
+    "nm-testing/llama2.c-stories42M-gsm8k-sparse-only-compressed",
+]
+
+
+@pytest.mark.parametrize(
+    "base_model", MODELS, ids=["no-checkpoint-recipe", "with-checkpoint-recipe"]
+)
+@pytest.mark.parametrize(
+    "save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
+)
+class TestLLMCompressorIntegration:
+    """
+    e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
+    """
+
+    @require_torch_2_4_1
+    def test_llmcompressor_plugin(
+        self, temp_dir, base_model: str, save_compressed: bool
+    ):
+        # core cfg
+        cfg = DictDefault(
+            {
+                "base_model": base_model,
+                "plugins": ["axolotl.integrations.llm_compressor.LLMCompressorPlugin"],
+                "sequence_len": 1024,
+                "val_set_size": 0.05,
+                "special_tokens": {"pad_token": "<|endoftext|>"},
+                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 1e-5,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "max_steps": 5,
+                "llmcompressor": {
+                    "recipe": {
+                        "finetuning_stage": {
+                            "finetuning_modifiers": {
+                                "ConstantPruningModifier": {
+                                    "targets": [
+                                        "re:.*q_proj.weight",
+                                        "re:.*k_proj.weight",
+                                        "re:.*v_proj.weight",
+                                        "re:.*o_proj.weight",
+                                        "re:.*gate_proj.weight",
+                                        "re:.*up_proj.weight",
+                                        "re:.*down_proj.weight",
+                                    ],
+                                    "start": 0,
+                                },
+                            },
+                        },
+                    },
+                    "save_compressed": save_compressed,
+                },
+            }
+        )
+
+        prepare_plugins(cfg)
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+        _check_llmcompressor_model_outputs(temp_dir, save_compressed)
+
+
+def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
+
+    # recipe.yaml should exist
+    assert (Path(temp_dir) / "recipe.yaml").exists()
+
+    # sparsity config exists if save_compressed
+    if save_compressed:
+        from compressed_tensors import ModelCompressor
+        from compressed_tensors.config import Sparse24BitMaskConfig
+
+        compressor = ModelCompressor.from_pretrained(temp_dir)
+        assert compressor is not None
+        assert isinstance(compressor.sparsity_config, Sparse24BitMaskConfig)
--- a/tests/e2e/patched/test_activation_checkpointing.py
+++ b/tests/e2e/patched/test_activation_checkpointing.py
@@ -1,77 +0,0 @@
-"""
-E2E tests for activation checkpointing
-"""
-
-import pytest
-import transformers
-from torch.utils.checkpoint import checkpoint
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-from ..utils import check_model_output_exists
-
-
-@pytest.fixture()
-def fix_checkpoint_after_test():
-    yield
-    transformers.modeling_utils.checkpoint = checkpoint
-
-
-class TestActivationCheckpointing:
-    """
-    E2E tests for activation checkpointing
-    """
-
-    def test_activation_checkpointing_offload(
-        self,
-        temp_dir,
-        fix_checkpoint_after_test,  # pylint: disable=unused-argument,redefined-outer-name
-    ):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "sequence_len": 1024,
-                "val_set_size": 0.0,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                    "eos_token": "<|im_end|>",
-                },
-                "datasets": [
-                    {
-                        "chat_template": "chatml",
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "split": "train[:10%]",
-                        "field_messages": "conversations",
-                        "message_field_role": "from",
-                        "message_field_content": "value",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 5,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "sample_packing": True,
-                "bf16": True,
-                "save_safetensors": True,
-                "gradient_checkpointing": "offload",
-            }
-        )
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        check_model_output_exists(temp_dir, cfg)
--- a/tests/e2e/patched/test_mixtral_samplepack.py
+++ b/tests/e2e/patched/test_mixtral_samplepack.py
@@ -99,7 +99,6 @@ class TestMixtral(unittest.TestCase):
                "bf16": "auto",
            }
        )
-        cfg = validate_config(cfg)
        normalize_config(cfg)
        cli_args = TrainerCliArgs()
        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
--- a/tests/e2e/patched/test_sp.py
+++ b/tests/e2e/patched/test_sp.py
@@ -2,19 +2,14 @@

 # pylint: disable=redefined-outer-name,unused-argument

-import functools
-import sys
 from unittest.mock import MagicMock, patch

 import pytest
 import torch
 from accelerate.state import PartialState

-from axolotl.core.trainers.mixins.sequence_parallel import apply_sequence_parallelism
 from axolotl.monkeypatch.attention.ring_attn import (
-    RingAttnFunc,
    get_ring_attn_group,
-    register_ring_attn,
    set_ring_attn_group,
 )
 from axolotl.utils.dict import DictDefault
@@ -52,27 +47,6 @@ def fixture_cfg():
    return cfg


-@pytest.fixture
-def sequence_parallel_batch():
-    """Create a test batch for sequence parallelism tests."""
-    batch_size = 1
-    seq_len = 8
-
-    # Create test tensors
-    input_ids = torch.arange(batch_size * seq_len).reshape(batch_size, seq_len)
-    attention_mask = torch.ones(batch_size, seq_len)
-    position_ids = torch.arange(seq_len).expand(batch_size, seq_len)
-
-    # Create test batch
-    batch = {
-        "input_ids": input_ids,
-        "attention_mask": attention_mask,
-        "position_ids": position_ids,
-    }
-
-    return batch
-
-
 class TestRingAttention:
    """Tests for the ring attention functionality."""

@@ -99,6 +73,11 @@ class TestRingAttention:
        self, mock_world_size, mock_rank, mock_new_group, partial_state
    ):
        """Test that ring attention groups are created correctly."""
+        from axolotl.monkeypatch.attention.ring_attn import (
+            RingAttnFunc,
+            register_ring_attn,
+        )
+
        # Setup mocks
        mock_world_size.return_value = 8  # 8 GPUs total
        mock_rank.return_value = 3  # GPU #3
@@ -122,303 +101,88 @@ class TestRingAttention:
        set_ring_attn_group(None)


-class TestConfigValidation:
-    """Tests for validating sequence parallelism configurations."""
+# Mock a simplified DataCollator test
+@patch("axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group")
+@patch("torch.distributed.get_rank")
+@patch("torch.distributed.get_world_size")
+def test_sequence_parallel_slicing(
+    mock_world_size, mock_rank, mock_get_group, partial_state
+):
+    """Test the basic sequence slicing logic without full collator instantiation."""
+    # Setup mocks
+    mock_get_group.return_value = MagicMock()
+    mock_rank.return_value = 1  # Second GPU
+    mock_world_size.return_value = 4  # 4 GPUs total

-    @pytest.fixture(autouse=True)
-    def setup_mocks(self, monkeypatch):
-        """Set up mocks for all tests in this class."""
-        # Mock the ring_flash_attn module
-        monkeypatch.setitem(sys.modules, "ring_flash_attn", MagicMock())
+    # Create a sample batch
+    batch = {
+        "input_ids": torch.tensor(
+            [
+                [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112],
+                [201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212],
+            ]
+        ),
+        "attention_mask": torch.ones(2, 12),
+    }

-    @pytest.fixture
-    def base_cfg(self):
-        """Create a base configuration for testing."""
-        return DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "learning_rate": 1e-3,
-                "output_dir": "./model-out",
-                "sequence_len": 512,
-                "special_tokens": {"pad_token": "<|endoftext|>"},
-            }
-        )
+    # Simplified slicing logic from SequenceParallelDataCollator
+    def slice_batch(batch, rank, world_size):
+        result = {}
+        for key in batch:
+            seq_len = batch[key].shape[1]
+            slice_size = seq_len // world_size
+            start_idx = rank * slice_size
+            end_idx = start_idx + slice_size if rank < world_size - 1 else seq_len
+            result[key] = batch[key][:, start_idx:end_idx]
+        return result

-    @pytest.mark.parametrize(
-        "config_updates, expected_values, should_pass, error_msg",
-        [
-            # Valid configuration
-            (
-                {"sequence_parallel_degree": 2, "flash_attention": True},
-                {"sequence_parallel_degree": 2, "flash_attention": True},
-                True,
-                None,
-            ),
-            # Default sequence_parallel_degree
-            ({}, {"sequence_parallel_degree": 1}, True, None),
-            # Invalid: sequence_parallel_degree > 1 without flash_attention
-            (
-                {"sequence_parallel_degree": 2, "flash_attention": False},
-                None,
-                False,
-                "flash_attention: true must be set",
-            ),
-            # Invalid: sequence_parallel_degree > 1 with sample_packing and micro_batch_size > 1
-            (
-                {
-                    "sequence_parallel_degree": 2,
-                    "flash_attention": True,
-                    "sample_packing": True,
-                    "micro_batch_size": 2,
-                    "pad_to_sequence_len": True,
-                },
-                None,
-                False,
-                "micro_batch_size must be set to 1",
-            ),
-        ],
-        ids=[
-            "valid_config",
-            "default_sp_degree",
-            "without_flash_attention",
-            "sample_packing_with_large_batch",
-        ],
+    # Slice the batch
+    result = slice_batch(
+        batch, rank=mock_rank.return_value, world_size=mock_world_size.return_value
    )
-    def test_sequence_parallel_config_validation(
-        self, base_cfg, config_updates, expected_values, should_pass, error_msg
-    ):
-        """Test various sequence parallelism configuration scenarios."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig

-        # Apply updates to base config
-        cfg = base_cfg
-        cfg.update(config_updates)
-
-        if should_pass:
-            # Should validate without errors
-            config = AxolotlInputConfig(**cfg)
-
-            # Check expected values
-            for key, value in expected_values.items():
-                assert getattr(config, key) == value
-        else:
-            # Should raise exception
-            with pytest.raises(ValueError) as excinfo:
-                AxolotlInputConfig(**cfg)
-            assert error_msg in str(excinfo.value)
-
-    @pytest.mark.parametrize(
-        "ring_attn_func, sample_packing, expected_func",
+    # Check slicing
+    assert result["input_ids"].shape == (2, 3)  # 12 tokens / 4 GPUs = 3 tokens per GPU
+    expected_input_ids = torch.tensor(
        [
-            (None, True, RingAttnFunc.VARLEN_LLAMA3),
-            (None, False, RingAttnFunc.BATCH_RING),
-        ],
-        ids=["default_with_sample_packing", "default_without_sample_packing"],
+            [104, 105, 106],  # Second slice of first sequence
+            [204, 205, 206],  # Second slice of second sequence
+        ]
    )
-    def test_ring_attn_func_validation(
-        self, base_cfg, ring_attn_func, sample_packing, expected_func
-    ):
-        """Test ring_attn_func validation and defaults."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
-
-        # Apply updates to base config
-        cfg = base_cfg | {
-            "sequence_parallel_degree": 2,
-            "flash_attention": True,
-            "sample_packing": sample_packing,
-        }
-
-        if ring_attn_func is not None:
-            cfg["ring_attn_func"] = ring_attn_func
-
-        # Should validate without errors
-        config = AxolotlInputConfig(**cfg)
-
-        # Check ring_attn_func value
-        assert config.ring_attn_func.value == expected_func
-
-    def test_invalid_ring_attn_func(self, base_cfg):
-        """Test that an invalid ring_attn_func is rejected."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
-
-        # Invalid configuration with invalid ring_attn_func
-        cfg = base_cfg | {
-            "sequence_parallel_degree": 2,
-            "flash_attention": True,
-            "ring_attn_func": "INVALID_FUNC",
-        }
-
-        # Should raise ValidationError
-        with pytest.raises(ValueError) as excinfo:
-            AxolotlInputConfig(**cfg)
-
-        # Verify error message
-        assert "ring_attn_func: INVALID_FUNC must be in" in str(excinfo.value)
+    assert torch.all(result["input_ids"] == expected_input_ids)


-class TestApplySequenceParallelism:
-    """Tests for the apply_sequence_parallelism function."""
+@patch.dict("sys.modules", {"ring_flash_attn": MagicMock()})
+def test_config_validation_with_valid_inputs(cfg):
+    """Test that valid sequence parallelism configurations pass validation."""
+    # Import the actual model class with appropriate mocks
+    from axolotl.utils.schemas.config import AxolotlInputConfig

-    @pytest.fixture(autouse=True)
-    def mock_distributed(self, monkeypatch):
-        """Mock torch.distributed functions for testing."""
-        # Mock is_initialized to return True
-        monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
+    # Valid configuration: sequence_parallel_degree > 1 and flash_attention is True
+    cfg = cfg | {
+        "sequence_parallel_degree": 2,
+        "flash_attention": True,
+    }

-        # Mock get_rank to return 0 by default
-        monkeypatch.setattr(torch.distributed, "get_rank", lambda *args, **kwargs: 0)
+    # Should validate without errors
+    config = AxolotlInputConfig(**cfg)
+    assert config.sequence_parallel_degree == 2
+    assert config.flash_attention is True

-        # Mock get_world_size to return 2 by default
-        monkeypatch.setattr(
-            torch.distributed, "get_world_size", lambda *args, **kwargs: 2
-        )

-        # Mock the process group
-        monkeypatch.setattr(
-            "axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group",
-            MagicMock,
-        )
+def test_config_validation_with_invalid_inputs(cfg):
+    """Test that invalid sequence parallelism configurations fail validation."""
+    from axolotl.utils.schemas.config import AxolotlInputConfig

-        # Mock update_ring_attn_params
-        monkeypatch.setattr(
-            "axolotl.monkeypatch.attention.ring_attn.update_ring_attn_params",
-            lambda **kwargs: None,
-        )
+    # Invalid configuration: sequence_parallel_degree > 1 but flash_attention is False
+    cfg = cfg | {
+        "sequence_parallel_degree": 2,
+        "flash_attention": False,
+    }

-    def test_world_size_one(self, sequence_parallel_batch):
-        """Test that function returns original batch when world size is 1."""
-        result = apply_sequence_parallelism(
-            batch=sequence_parallel_batch,
-            local_rank=0,
-            local_world_size=1,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
+    # Should raise ValidationError
+    with pytest.raises(ValueError) as excinfo:
+        AxolotlInputConfig(**cfg)

-        # Should return the original batch unchanged
-        assert result == sequence_parallel_batch
-
-    def test_batch_ring_rank0(self, sequence_parallel_batch):
-        """Test BATCH_RING sharding for rank 0 in a 2-process group."""
-        batch = sequence_parallel_batch
-        seq_len = batch["input_ids"].size(1)
-
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Check that sequence dimension was sharded correctly
-        assert result["input_ids"].shape[1] == seq_len // 2
-        assert result["attention_mask"].shape[1] == seq_len // 2
-
-        # Verify content: rank 0 should get the first half of the sequence
-        assert torch.equal(result["input_ids"], batch["input_ids"][:, : seq_len // 2])
-        assert torch.equal(
-            result["position_ids"], batch["position_ids"][:, : seq_len // 2]
-        )
-
-    def test_batch_ring_rank1(self, sequence_parallel_batch):
-        """Test BATCH_RING sharding for rank 1 in a 2-process group."""
-        batch = sequence_parallel_batch
-        seq_len = batch["input_ids"].size(1)
-        original_input_ids = batch["input_ids"].clone()
-
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Verify content: rank 1 should get the second half of the sequence
-        assert torch.equal(result["input_ids"], original_input_ids[:, seq_len // 2 :])
-
-    def test_batch_zigzag(self, sequence_parallel_batch):
-        """Test BATCH_ZIGZAG sharding pattern."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-        seq_len = batch["input_ids"].size(1)
-
-        # Test rank 0
-        result_rank0 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
-
-        # Test rank 1
-        result_rank1 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
-
-        # Checks for both ranks
-        assert result_rank0["input_ids"].shape[1] == seq_len // 2
-        assert result_rank1["input_ids"].shape[1] == seq_len // 2
-
-        # For a 2-rank system with 8 tokens, check specific zigzag pattern
-        # Rank 0 should get chunks [0, 1] and [6, 7]
-        # Rank 1 should get chunks [2, 3] and [4, 5]
-        if seq_len == 8:
-            # Create expected tensors for comparison
-            rank0_expected = torch.cat(
-                [original_input_ids[:, :2], original_input_ids[:, 6:8]], dim=1
-            )
-
-            rank1_expected = torch.cat(
-                [original_input_ids[:, 2:4], original_input_ids[:, 4:6]], dim=1
-            )
-
-            assert torch.equal(result_rank0["input_ids"], rank0_expected)
-            assert torch.equal(result_rank1["input_ids"], rank1_expected)
-
-    def test_partial_application(self, sequence_parallel_batch):
-        """Test that we can create a partially applied version of the function."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-
-        # Create a partially applied function
-        rank0_ring_parallel = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Use the partially applied function
-        result = rank0_ring_parallel(batch=batch)
-
-        # Verify it works as expected
-        assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2
-        assert torch.equal(
-            result["input_ids"],
-            original_input_ids[:, : original_input_ids.shape[1] // 2],
-        )
-
-    def test_missing_position_ids(self, sequence_parallel_batch):
-        """Test handling of batch without position_ids."""
-        # Create a batch without position_ids
-        batch = {
-            k: v for k, v in sequence_parallel_batch.items() if k != "position_ids"
-        }
-        original_input_ids = batch["input_ids"].clone()
-
-        # This should run without error even though position_ids is missing
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Verification should pass
-        assert "position_ids" not in result
-        assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2
+    # Verify error message
+    assert "flash_attention: true must be set" in str(excinfo.value)
Author	SHA1	Message	Date
Wing Lian	b708a1cc45	validate config to set defaults	2025-04-26 13:11:25 -04:00
Rahul Tuli	daa9a58f83	Add: line about further optimizations using llmcompressor Signed-off-by: Rahul Tuli <rtuli@redhat.com>	2025-04-24 14:06:25 -04:00
Rahul Tuli	ae7069e15b	Merge branch 'main' into llmcompressor-sft	2025-04-24 12:37:14 -05:00
Rahul Tuli	20d48cd617	Address Review Comments: * deleted redundant docs/llm_compressor.qmd * incorporated feedback in integration README.md * added llmcompressor integration to docs/custom_integrations.qmd Signed-off-by: Rahul Tuli <rtuli@redhat.com>	2025-04-24 13:36:09 -04:00
Rahul Tuli	e766a730ba	Add: .qmd file	2025-04-24 12:45:57 -04:00
Rahul Tuli	7dc797860e	Tests, Style, Updates	2025-04-24 12:45:57 -04:00
Rahul Tuli	ff4904c8c4	Rebase and updates!	2025-04-24 12:45:57 -04:00
Rahul Tuli	45b7293793	Add: `llm_compressor` integration documentation	2025-04-24 12:45:57 -04:00
Rahul Tuli	279c7178bc	Move: LLMCompressorPlugin into it's own submodule	2025-04-24 12:45:57 -04:00
Rahul Tuli	e73c3709f9	Update model config	2025-04-24 12:45:57 -04:00
Rahul Tuli	33562189f8	Use: absolute import	2025-04-24 12:45:57 -04:00
Rahul Tuli	c057a2268f	Rename: sft.yaml to sparse-finetuning.yaml	2025-04-24 12:45:57 -04:00
Rahul Tuli	9d7a3809b5	Add: llcompressor installable	2025-04-24 12:45:57 -04:00
Rahul Tuli	b7b24d6a64	Address review comments from @markurtz	2025-04-24 12:45:57 -04:00
Rahul Tuli	8b82b8f7a1	Apply suggestions from @markurtz Co-authored-by: Mark Kurtz <mark.j.kurtz@gmail.com>	2025-04-24 12:45:57 -04:00
Rahul Tuli	81da58c0a1	Update llmcompressor version to latest	2025-04-24 12:45:57 -04:00
Rahul Tuli	2cd5a234a7	Revert: TODO's	2025-04-24 12:45:57 -04:00
Rahul Tuli	8c1af0747d	Use: warning over warn	2025-04-24 12:45:57 -04:00
Rahul Tuli	a06b360d99	pre commit hooks	2025-04-24 12:45:57 -04:00
Rahul Tuli	0f6456a14f	Add:llmcompressor instalable	2025-04-24 12:45:57 -04:00
Rahul Tuli	47a333ce49	Update: review comments!	2025-04-24 12:45:57 -04:00
Rahul Tuli	f9d6776c28	Add: SFTPlugin with llmcompressor	2025-04-24 12:45:57 -04:00