forked from phoenix-oss/llama-stack-mirror
a7ecc92be1
# What does this PR do? the providers list is missing post_training. Add that column and `HuggingFace`, `TorchTune`, and `NVIDIA NEMO` as supported providers. also point to these providers in docs/source/providers/index.md, and describe basic functionality There are other missing provider types here as well, but starting with this Signed-off-by: Charlie Doern <cdoern@redhat.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
122 lines
3 KiB
Markdown
122 lines
3 KiB
Markdown
---
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orphan: true
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---
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# HuggingFace SFTTrainer
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[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets
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## Features
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- Simple access through the post_training API
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- Fully integrated with Llama Stack
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- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders)
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## Usage
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To use the HF SFTTrainer in your Llama Stack project, follow these steps:
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1. Configure your Llama Stack project to use this provider.
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2. Kick off a SFT job using the Llama Stack post_training API.
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## Setup
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You can access the HuggingFace trainer via the `ollama` distribution:
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```bash
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llama stack build --template ollama --image-type venv
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llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
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```
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## Run Training
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You can access the provider and the `supervised_fine_tune` method via the post_training API:
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```python
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import time
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import uuid
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from llama_stack_client.types import (
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post_training_supervised_fine_tune_params,
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algorithm_config_param,
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)
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def create_http_client():
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from llama_stack_client import LlamaStackClient
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return LlamaStackClient(base_url="http://localhost:8321")
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client = create_http_client()
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# Example Dataset
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client.datasets.register(
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purpose="post-training/messages",
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source={
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"type": "uri",
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"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
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},
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dataset_id="simpleqa",
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)
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training_config = post_training_supervised_fine_tune_params.TrainingConfig(
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data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
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batch_size=32,
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data_format="instruct",
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dataset_id="simpleqa",
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shuffle=True,
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),
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gradient_accumulation_steps=1,
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max_steps_per_epoch=0,
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max_validation_steps=1,
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n_epochs=4,
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)
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algorithm_config = algorithm_config_param.LoraFinetuningConfig( # this config is also currently mandatory but should not be
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alpha=1,
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apply_lora_to_mlp=True,
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apply_lora_to_output=False,
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lora_attn_modules=["q_proj"],
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rank=1,
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type="LoRA",
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)
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job_uuid = f"test-job{uuid.uuid4()}"
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# Example Model
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training_model = "ibm-granite/granite-3.3-8b-instruct"
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start_time = time.time()
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response = client.post_training.supervised_fine_tune(
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job_uuid=job_uuid,
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logger_config={},
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model=training_model,
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hyperparam_search_config={},
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training_config=training_config,
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algorithm_config=algorithm_config,
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checkpoint_dir="output",
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)
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print("Job: ", job_uuid)
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# Wait for the job to complete!
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while True:
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status = client.post_training.job.status(job_uuid=job_uuid)
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if not status:
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print("Job not found")
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break
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print(status)
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if status.status == "completed":
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break
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print("Waiting for job to complete...")
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time.sleep(5)
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end_time = time.time()
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print("Job completed in", end_time - start_time, "seconds!")
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print("Artifacts:")
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print(client.post_training.job.artifacts(job_uuid=job_uuid))
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```
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