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Please refer our [document](./docs/training.md) to see how to start [Single GPU](./docs/training.md#single-gpu) or [Multi-GPU](./docs/training.md#multiple-gpus-with-fsdp) runs with fms-hf-tuning.
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You can also refer the same [document](./docs/training.md#tips-on-parameters-to-set) on how to use various training arguments.
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* Please refer our document on [training](./docs/training.md) to see how to start [Single GPU](./docs/training.md#single-gpu) or [Multi-GPU](./docs/training.md#multiple-gpus-with-fsdp) runs with fms-hf-tuning.
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* You can also refer the same a different [section](./docs/training.md#tips-on-parameters-to-set) of the same document on tips to set various training arguments.
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### *Debug recommendation:*
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While training, if you encounter flash-attn errors such as `undefined symbol`, you can follow the below steps for clean installation of flash binaries. This may occur when having multiple environments sharing the pip cache directory or torch version is updated.
- For each tuning technique, we run testing on a single large model of each architecture type and claim support for the smaller models. For example, with QLoRA technique, we tested on granite-34b GPTBigCode and claim support for granite-20b-multilingual.
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- While we expect most Hugging Face decoder models to work, we have primarily tested fine-tuning for below family of models.
- LoRA Layers supported : All the linear layers of a model + output `lm_head` layer. Users can specify layers as a list or use `all-linear` as a shortcut. Layers are specific to a model architecture and can be specified as noted [here](https://github.com/foundation-model-stack/fms-hf-tuning?tab=readme-ov-file#lora-tuning-example)
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- Legend:
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✅ Ready and available
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✔️ Ready and available - compatible architecture (*see first bullet point above)
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🚫 Not supported
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? May be supported, but not tested
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Model Name & Size | Model Architecture | Full Finetuning | Low Rank Adaptation (i.e. LoRA) | qLoRA(quantized LoRA) |
(*) - Supported with `fms-hf-tuning` v2.4.0 or later.
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(**) - Supported for q,k,v,o layers . `all-linear` target modules does not infer on vLLM yet.
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(***) - Supported from platform up to 8k context length - same architecture as llama3-8b.
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### Supported vision model
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We also support full fine-tuning and LoRA tuning for vision language models - `Granite 3.2 Vision`, `Llama 3.2 Vision`, and `LLaVa-Next` from `v2.8.1` onwards.
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For information on supported dataset formats and how to tune a vision-language model, please see [this document](./vision-language-model-tuning.md).
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Model Name & Size | Model Architecture | LoRA Tuning | Full Finetuning |
* vLLM currently does not support inference with LoRA-tuned vision models. To use a tuned LoRA adapter of vision model, please merge it with the base model before running vLLM inference.
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An extended list for tested models is maintaned in the [supported models](./docs/supported-models.md) document but might have outdated information.
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## Data Support
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Users can pass training data as either a single file or a Hugging Face dataset ID using the `--training_data_path` argument along with other arguments required for various [use cases](./docs/advanced-data-preprocessing.md#use-cases-supported-via-command-line-argument-training_data_path). If user choose to pass a file, it can be in any of the [supported formats](#supported-data-formats). Alternatively, you can use our powerful [data preprocessing backend](./docs/advanced-data-preprocessing.md) to preprocess datasets on the fly.
For each of the requested trackers the code expects you to pass a config to the `sft_trainer.train` function which can be specified through `tracker_conifgs` argument [here](https://github.com/foundation-model-stack/fms-hf-tuning/blob/a9b8ec8d1d50211873e63fa4641054f704be8712/tuning/sft_trainer.py#L78) details of which are present below.
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@@ -45,11 +45,18 @@ Experiment tracking in fms-hf-tuning allows users to track their experiments wit
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The code supports currently these trackers out of the box,
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*`FileLoggingTracker` : A built in tracker which supports logging training loss to a file.
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- Since this is builin no need to install anything.
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*`Aimstack` : A popular opensource tracker which can be used to track any metrics or metadata from the experiments.
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- Install by running
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`pip install fms-hf-tuning[aim]`
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*`MLflow Tracking` : Another popular opensource tracker which stores metrics, metadata or even artifacts from experiments.
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- Install by running
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`pip install fms-hf-tuning[mlflow]`
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*`Clearml Tracking` : Another opensource tracker which stores metrics, metadata or even artifacts from experiments.
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- Install by running
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`pip install fms-hf-tuning[clearml]`
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Further details on enabling and using the trackers mentioned above can be found [here](./experiment-tracking.md).
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Note. All trackers expect some arguments or can be customized by passing command line arguments which are described in our document on [experiment tracking](./experiment-tracking.md). For further details on enabling and using the trackers use the experiment tracking document.
(*) - Supported for q,k,v,o layers . `all-linear` target modules does not infer on vLLM yet.
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(**) - Supported from platform up to 8k context length - same architecture as llama3-8b.
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### Supported vision model
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We also support full fine-tuning and LoRA tuning for vision language models - `Granite 3.2 Vision`, `Llama 3.2 Vision`, and `LLaVa-Next` from `v2.8.1` onwards.
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For information on supported dataset formats and how to tune a vision-language model, please see [this document](./vision-language-model-tuning.md).
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Model Name & Size | Model Architecture | LoRA Tuning | Full Finetuning |
* vLLM currently does not support inference with LoRA-tuned vision models. To use a tuned LoRA adapter of vision model, please merge it with the base model before running vLLM inference.
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