Instructions to use nerhneiro/llm-course-hw2-ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nerhneiro/llm-course-hw2-ppo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nerhneiro/llm-course-hw2-ppo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nerhneiro/llm-course-hw2-ppo")
model = AutoModelForCausalLM.from_pretrained("nerhneiro/llm-course-hw2-ppo", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nerhneiro/llm-course-hw2-ppo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nerhneiro/llm-course-hw2-ppo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nerhneiro/llm-course-hw2-ppo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nerhneiro/llm-course-hw2-ppo

SGLang

How to use nerhneiro/llm-course-hw2-ppo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nerhneiro/llm-course-hw2-ppo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nerhneiro/llm-course-hw2-ppo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nerhneiro/llm-course-hw2-ppo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nerhneiro/llm-course-hw2-ppo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nerhneiro/llm-course-hw2-ppo with Docker Model Runner:
```
docker model run hf.co/nerhneiro/llm-course-hw2-ppo
```

Model Card

In this task I fine-tuned SmolLM2-135M-Instruct using PPO method. We were obliged to use SmolLM2-135M-Instruct as ref_policy model and reward_model that we have alredy trained as reward and value models.

We had to reformat a dataset abit and transform it into chosen and rejected chat examples.

Training Details

I used PPOConfig with the arguments given below:

num_train_epochs=1,
per_device_train_batch_size=16,
learning_rate=1e-6,
seed=42,
logging_steps=7,

Generation example

user

I just read that scientists are working on developing a way to turn thoughts into speech - how close are we to making this a reality, and what are the implications for people with disabilities?

assistant

I think it's going to be a huge breakthrough. I mean, we're talking about a way to communicate with people who are paralyzed or have aphasia. It's going to be a game-changer.

After one epoch training loss reached 1.0223016738891602.

It was harder to teach this model. I assume PPO requires a more mindful hyperparameter selection. Sometimes it got tangled in repetitions or tried to continue the chat on its own.

Downloads last month: 6

Safetensors

Model size

0.1B params

Tensor type

F32