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>

README.md

@@ -107,26 +107,29 @@ By reducing friction and complexity, Llama Stack empowers developers to focus on
 ### API Providers
 Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.
 
-| **API Provider Builder** |    **Environments**    | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** |
+| **API Provider Builder** |    **Environments**    | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | **Post Training** |
-|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|
+|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|:-----------------:|
-|      Meta Reference      |      Single Node       |     ✅      |       ✅       |     ✅      |     ✅      |       ✅       |
+|      Meta Reference      |      Single Node       |     ✅      |       ✅       |     ✅      |     ✅      |       ✅       |               |
-|        SambaNova         |         Hosted         |            |       ✅       |            |     ✅      |               |
+|        SambaNova         |         Hosted         |            |       ✅       |            |     ✅      |               |                  |
-|         Cerebras         |         Hosted         |            |       ✅       |            |            |               |
+|         Cerebras         |         Hosted         |            |       ✅       |            |            |               |                  |
-|        Fireworks         |         Hosted         |     ✅      |       ✅       |     ✅      |            |               |
+|        Fireworks         |         Hosted         |     ✅      |       ✅       |     ✅      |            |               |                |
-|       AWS Bedrock        |         Hosted         |            |       ✅       |            |     ✅      |               |
+|       AWS Bedrock        |         Hosted         |            |       ✅       |            |     ✅      |               |                |
-|         Together         |         Hosted         |     ✅      |       ✅       |            |     ✅      |               |
+|         Together         |         Hosted         |     ✅      |       ✅       |            |     ✅      |               |                |
-|           Groq           |         Hosted         |            |       ✅       |            |            |               |
+|           Groq           |         Hosted         |            |       ✅       |            |            |               |                 |
-|          Ollama          |      Single Node       |            |       ✅       |            |            |               |
+|          Ollama          |      Single Node       |            |       ✅       |            |            |               |                 |
-|           TGI            | Hosted and Single Node |            |       ✅       |            |            |               |
+|           TGI            | Hosted and Single Node |            |       ✅       |            |            |               |                 |
-|        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |
+|        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |                 |
-|          Chroma          |      Single Node       |            |               |     ✅      |            |               |
+|          Chroma          |      Single Node       |            |               |     ✅      |            |               |                 |
-|        PG Vector         |      Single Node       |            |               |     ✅      |            |               |
+|        PG Vector         |      Single Node       |            |               |     ✅      |            |               |                 |
-|    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |
+|    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |                |
-|           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |
+|           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |                 |
-|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |
+|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |                 |
-|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |
+|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |                 |
-|          Gemini          |         Hosted         |            |       ✅       |            |            |               |
+|          Gemini          |         Hosted         |            |       ✅       |            |            |               |                 |
-|          watsonx         |         Hosted         |            |       ✅       |            |            |               |
+|          watsonx         |         Hosted         |            |       ✅       |            |            |               |                 |
+|        HuggingFace       |       Single Node      |            |                |            |            |               |       ✅        |
+|         TorchTune        |       Single Node      |            |                |            |            |               |       ✅        |
+|       NVIDIA NEMO        |         Hosted         |            |                |            |            |               |       ✅        |
 
 
 ### Distributions

docs/source/providers/index.md

@@ -30,6 +30,18 @@ Runs inference with an LLM.
 ## Post Training
 Fine-tunes a model.
 
+#### Post Training Providers
+The following providers are available for Post Training:
+
+```{toctree}
+:maxdepth: 1
+
+external
+post_training/huggingface
+post_training/torchtune
+post_training/nvidia_nemo
+```
+
 ## Safety
 Applies safety policies to the output at a Systems (not only model) level.
 

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))
+```

docs/source/providers/post_training/nvidia_nemo.md

@@ -0,0 +1,163 @@
+---
+orphan: true
+---
+# NVIDIA NEMO
+
+[NVIDIA NEMO](https://developer.nvidia.com/nemo-framework) is a remote post training provider for Llama Stack. It provides enterprise-grade fine-tuning capabilities through NVIDIA's NeMo Customizer service.
+
+## Features
+
+- Enterprise-grade fine-tuning capabilities
+- Support for LoRA and SFT fine-tuning
+- Integration with NVIDIA's NeMo Customizer service
+- Support for various NVIDIA-optimized models
+- Efficient training with NVIDIA hardware acceleration
+
+## Usage
+
+To use NVIDIA NEMO in your Llama Stack project, follow these steps:
+
+1. Configure your Llama Stack project to use this provider.
+2. Set up your NVIDIA API credentials.
+3. Kick off a fine-tuning job using the Llama Stack post_training API.
+
+## Setup
+
+You'll need to set the following environment variables:
+
+```bash
+export NVIDIA_API_KEY="your-api-key"
+export NVIDIA_DATASET_NAMESPACE="default"
+export NVIDIA_CUSTOMIZER_URL="your-customizer-url"
+export NVIDIA_PROJECT_ID="your-project-id"
+export NVIDIA_OUTPUT_MODEL_DIR="your-output-model-dir"
+```
+
+## 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=8,  # Default batch size for NEMO
+        data_format="instruct",
+        dataset_id="simpleqa",
+        shuffle=True,
+    ),
+    n_epochs=50,  # Default epochs for NEMO
+    optimizer_config=post_training_supervised_fine_tune_params.TrainingConfigOptimizerConfig(
+        lr=0.0001,  # Default learning rate
+        weight_decay=0.01,  # NEMO-specific parameter
+    ),
+    # NEMO-specific parameters
+    log_every_n_steps=None,
+    val_check_interval=0.25,
+    sequence_packing_enabled=False,
+    hidden_dropout=None,
+    attention_dropout=None,
+    ffn_dropout=None,
+)
+
+algorithm_config = algorithm_config_param.LoraFinetuningConfig(
+    alpha=16,  # Default alpha for NEMO
+    type="LoRA",
+)
+
+job_uuid = f"test-job{uuid.uuid4()}"
+
+# Example Model - must be a supported NEMO model
+training_model = "meta/llama-3.1-8b-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))
+```
+
+## Supported Models
+
+Currently supports the following models:
+- meta/llama-3.1-8b-instruct
+- meta/llama-3.2-1b-instruct
+
+## Supported Parameters
+
+### TrainingConfig
+- n_epochs (default: 50)
+- data_config
+- optimizer_config
+- log_every_n_steps
+- val_check_interval (default: 0.25)
+- sequence_packing_enabled (default: False)
+- hidden_dropout (0.0-1.0)
+- attention_dropout (0.0-1.0)
+- ffn_dropout (0.0-1.0)
+
+### DataConfig
+- dataset_id
+- batch_size (default: 8)
+
+### OptimizerConfig
+- lr (default: 0.0001)
+- weight_decay (default: 0.01)
+
+### LoRA Config
+- alpha (default: 16)
+- type (must be "LoRA")
+
+Note: Some parameters from the standard Llama Stack API are not supported and will be ignored with a warning.

docs/source/providers/post_training/torchtune.md

@@ -0,0 +1,125 @@
+---
+orphan: true
+---
+# TorchTune
+
+[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch.
+
+## Features
+
+- Simple access through the post_training API
+- Fully integrated with Llama Stack
+- GPU support and single device capabilities.
+- Support for LoRA
+
+## Usage
+
+To use TorchTune in your Llama Stack project, follow these steps:
+
+1. Configure your Llama Stack project to use this provider.
+2. Kick off a fine-tuning job using the Llama Stack post_training API.
+
+## Setup
+
+You can access the TorchTune trainer by writing your own yaml pointing to the provider:
+
+```yaml
+post_training:
+  - provider_id: torchtune
+    provider_type: inline::torchtune
+    config: {}
+```
+
+you can then build and run your own stack with this provider.
+
+## 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(
+    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 = "meta-llama/Llama-2-7b-hf"
+
+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))
+```