docs: add post training to providers list (#2280)

# 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>

docs/source/providers/post_training/huggingface.md

@@ -0,0 +1,122 @@
+---
+orphan: true
+---
+# HuggingFace SFTTrainer
+
+[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
+
+## Features
+
+- Simple access through the post_training API
+- Fully integrated with Llama Stack
+- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders)
+
+## Usage
+
+To use the HF SFTTrainer in your Llama Stack project, follow these steps:
+
+1. Configure your Llama Stack project to use this provider.
+2. Kick off a SFT job using the Llama Stack post_training API.
+
+## Setup
+
+You can access the HuggingFace trainer via the `ollama` distribution:
+
+```bash
+llama stack build --template ollama --image-type venv
+llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
+```
+
+## Run Training
+
+You can access the provider and the `supervised_fine_tune` method via the post_training API:
+
+```python
+import time
+import uuid
+
+
+from llama_stack_client.types import (
+    post_training_supervised_fine_tune_params,
+    algorithm_config_param,
+)
+
+
+def create_http_client():
+    from llama_stack_client import LlamaStackClient
+
+    return LlamaStackClient(base_url="http://localhost:8321")
+
+
+client = create_http_client()
+
+# Example Dataset
+client.datasets.register(
+    purpose="post-training/messages",
+    source={
+        "type": "uri",
+        "uri": "huggingface://datasets/llamastack/simpleqa?split=train",
+    },
+    dataset_id="simpleqa",
+)
+
+training_config = post_training_supervised_fine_tune_params.TrainingConfig(
+    data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
+        batch_size=32,
+        data_format="instruct",
+        dataset_id="simpleqa",
+        shuffle=True,
+    ),
+    gradient_accumulation_steps=1,
+    max_steps_per_epoch=0,
+    max_validation_steps=1,
+    n_epochs=4,
+)
+
+algorithm_config = algorithm_config_param.LoraFinetuningConfig(  # this config is also currently mandatory but should not be
+    alpha=1,
+    apply_lora_to_mlp=True,
+    apply_lora_to_output=False,
+    lora_attn_modules=["q_proj"],
+    rank=1,
+    type="LoRA",
+)
+
+job_uuid = f"test-job{uuid.uuid4()}"
+
+# Example Model
+training_model = "ibm-granite/granite-3.3-8b-instruct"
+
+start_time = time.time()
+response = client.post_training.supervised_fine_tune(
+    job_uuid=job_uuid,
+    logger_config={},
+    model=training_model,
+    hyperparam_search_config={},
+    training_config=training_config,
+    algorithm_config=algorithm_config,
+    checkpoint_dir="output",
+)
+print("Job: ", job_uuid)
+
+
+# Wait for the job to complete!
+while True:
+    status = client.post_training.job.status(job_uuid=job_uuid)
+    if not status:
+        print("Job not found")
+        break
+
+    print(status)
+    if status.status == "completed":
+        break
+
+    print("Waiting for job to complete...")
+    time.sleep(5)
+
+end_time = time.time()
+print("Job completed in", end_time - start_time, "seconds!")
+
+print("Artifacts:")
+print(client.post_training.job.artifacts(job_uuid=job_uuid))
+```