Instructions to use amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

TensorRT

How to use amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 with TensorRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4

SGLang

How to use amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 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 "amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4" \
    --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": "amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4",
		"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 "amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4" \
        --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": "amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4
```

amer8 commited on Apr 11

Commit

3a9a6a4

verified ·

1 Parent(s): 6681c20

Update README.md

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README.md CHANGED Viewed

@@ -42,14 +42,14 @@ This is an **NVFP4 quantized** version of [deepseek-ai/DeepSeek-R1-Distill-Llama
 ```python
 from tensorrt_llm import LLM
-llm = LLM(model="YOUR_USERNAME/DeepSeek-R1-Distill-Llama-8B-NVFP4")
 output = llm.generate("Paris is great because")
 print(output)
 ```
 ### With TensorRT-LLM Server
 ```bash
-trtllm-serve YOUR_USERNAME/DeepSeek-R1-Distill-Llama-8B-NVFP4 \
   --backend pytorch \
   --port 8000
 ```

 ```python
 from tensorrt_llm import LLM
+llm = LLM(model="amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4")
 output = llm.generate("Paris is great because")
 print(output)
 ```
 ### With TensorRT-LLM Server
 ```bash
+trtllm-serve amer8/DeepSeek-R1-Distill-Llama-8B-NVFP4 \
   --backend pytorch \
   --port 8000
 ```