Instructions to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Llama-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-8B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-8B")
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
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B

SGLang

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B 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 "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" \
    --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": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
		"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 "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" \
        --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": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B
```

duplicated bos_token when using apply_chat_template with Tokenizer

#20

by irvingjr - opened Feb 18, 2025

Discussion

irvingjr

Feb 18, 2025

    tokenizer: PreTrainedTokenizer = self.tokenizer_model
    msg = tokenizer.apply_chat_template(list_of_msg, tokenize=False, tools=None)
    outputs = tokenizer(
        msg, max_length=max_seq_len, truncation=True, add_special_tokens=True,
    )// add_special_tokens is the key. and sftTrainer from trl also set add_special_tokens to be true.

using the code above, for following text.

message = [
{"role": "system", "content": "You are an AI assistant."},
{"role": "user", "content": "What is the meaning of life?."},
{"role": "assistant", "content": "The meaning of life is 42."},
{"role": "user", "content": "That's ridiculous."},
{"role": "assistant", "content": "I agree."},
]

apply_chat_template will output following text:

<｜begin▁of▁sentence｜>You are an AI assistant.<｜User｜>What is the meaning of life?.<｜Assistant｜>The meaning of life is 42.<｜end▁of▁sentence｜><｜User｜>That's ridiculous.<｜Assistant｜>I agree.<｜end▁of▁sentence｜>

note that the <｜begin▁of▁sentence｜> already render to the message since the template add it before system context

and since the add_bos_token in tokenizer_config.json is true, the tokenizer(add_special_tokens=True) will output another bos as followed:

irvingjr

Feb 18, 2025

maybe we can modify the tokenizer_config.config and set add_bos_token to false by default?

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