Instructions to use RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic

SGLang

How to use RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic 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 "RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic" \
    --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": "RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic",
		"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 "RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic" \
        --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": "RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic with Docker Model Runner:
```
docker model run hf.co/RedHatAI/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic
```

nm-research commited on Feb 6, 2025

Commit

c6145eb

verified ·

1 Parent(s): 14375d3

Add Leaderboard-v2 evals

Browse files

Files changed (1) hide show

README.md +8 -7

README.md CHANGED Viewed

@@ -152,15 +152,16 @@ lm_eval \
 #### OpenLLM Leaderboard V2 evaluation scores
 | Metric                                                   | deepseek-ai/DeepSeek-R1-Distill-Llama-8B             | neuralmagic-ent/DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic |
 |---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:|
-| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot)       |                             |                                        |
-| BBH (Acc-Norm, 3-shot)                                  |                             |                                        |
-| GPQA (Acc-Norm, 0-shot)                                 |                              |                                         |
-| MUSR (Acc-Norm, 0-shot)                                 |                              |                                       |
-| MMLU-Pro (Acc, 5-shot)                                  |                             |                                       |
-| **Average Score**                                       | ****                        | ****                                   |
-| **Recovery (%)**                                            | **100.00**                       | ****                                   |
 #### Coding evaluation scores

 #### OpenLLM Leaderboard V2 evaluation scores
 | Metric                                                   | deepseek-ai/DeepSeek-R1-Distill-Llama-8B             | neuralmagic-ent/DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic |
 |---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:|
+| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot)       |         38.34                    |          38.22                              |
+| BBH (Acc-Norm, 3-shot)                                  |          38.19                   |               38.32                         |
+| GPQA (Acc-Norm, 0-shot)                                 |          28.87                    |                   27.56                      |
+| MUSR (Acc-Norm, 0-shot)                                 |          33.31                    |                33.71                       |
+| MMLU-Pro (Acc, 5-shot)                                  |          20.10                   |         21.39                              |
+| **Average Score**                                       | **26.47**                        | **26.53**                                   |
+| **Recovery (%)**                                            | **100.00**                       | **100.24**                                   |
 #### Coding evaluation scores