Instructions to use RLHFlow/LLaMA3-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RLHFlow/LLaMA3-SFT with Transformers:

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

pipe = pipeline("text-generation", model="RLHFlow/LLaMA3-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-SFT")
model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-SFT")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Local Apps Settings

vLLM

How to use RLHFlow/LLaMA3-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RLHFlow/LLaMA3-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLHFlow/LLaMA3-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RLHFlow/LLaMA3-SFT

SGLang

How to use RLHFlow/LLaMA3-SFT 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 "RLHFlow/LLaMA3-SFT" \
    --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": "RLHFlow/LLaMA3-SFT",
		"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 "RLHFlow/LLaMA3-SFT" \
        --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": "RLHFlow/LLaMA3-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RLHFlow/LLaMA3-SFT with Docker Model Runner:
```
docker model run hf.co/RLHFlow/LLaMA3-SFT
```

Missing BOS token in tokenized text

by ZhaofengWu - opened Aug 8, 2024

Discussion

ZhaofengWu

Aug 8, 2024

>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-SFT")
>>> tokenizer.apply_chat_template([{"role": "user", "content": "test"}])
[128006, 882, 128007, 198, 1985, 128009, 198]
>>> tokenizer.convert_ids_to_tokens(tokenizer.apply_chat_template([{"role": "user", "content": "test"}]))
['<|start_header_id|>', 'user', '<|end_header_id|>', 'Ċ', 'test', '<|eot_id|>', 'Ċ']

The BOS token is not added to the tokenized text. This is in contrast to, for example, llama-3-instruct's tokenizer which does add this token (see the chat template in https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json). Is this intentional?

weqweasdas

RLHFlow org Aug 8, 2024

The llama3-instruct model updates the tokenizer twice due to bugs after our project.

We fix the first bug but do not fix the bos issue so the model is trained without the bos. That is why we also delete the bos token when we service our reward model. But we found that it did not influence that much. For instance, it leads to less then 1% accuracy in reward model accuracy.

ZhaofengWu

Aug 8, 2024

Got it, thank you!

ZhaofengWu changed discussion status to closed Aug 8, 2024

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