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 10, 2025

Commit

73bd426

verified ·

1 Parent(s): 577d996

Update README.md

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@@ -8,7 +8,7 @@ base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
 library_name: transformers
 ---
-# DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic
 ## Model Overview
 - **Model Architecture:** LlamaForCausalLM
@@ -42,7 +42,7 @@ from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
 number_gpus = 1
-model_name = "neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
@@ -112,7 +112,7 @@ OpenLLM Leaderboard V1:
 ```
 lm_eval \
   --model vllm \
-  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic",dtype=auto,max_model_len=4096,enable_chunked_prefill=True \
   --tasks openllm \
   --write_out \
   --batch_size auto \
@@ -124,7 +124,7 @@ OpenLLM Leaderboard V2:
 ```
 lm_eval \
   --model vllm \
-  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic",dtype=auto,max_model_len=4096,enable_chunked_prefill=True \
   --apply_chat_template \
   --fewshot_as_multiturn \
   --tasks leaderboard \
@@ -142,7 +142,7 @@ lm_eval \
       <th>Category</th>
       <th>Metric</th>
       <th>deepseek-ai/DeepSeek-R1-Distill-Llama-8B</th>
-      <th>neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-Dynamic</th>
       <th>Recovery</th>
     </tr>
   </thead>

 library_name: transformers
 ---
+# DeepSeek-R1-Distill-Llama-8B-FP8-dynamic
 ## Model Overview
 - **Model Architecture:** LlamaForCausalLM
 from vllm import LLM, SamplingParams
 number_gpus = 1
+model_name = "neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
 ```
 lm_eval \
   --model vllm \
+  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic",dtype=auto,max_model_len=4096,enable_chunked_prefill=True \
   --tasks openllm \
   --write_out \
   --batch_size auto \
 ```
 lm_eval \
   --model vllm \
+  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic",dtype=auto,max_model_len=4096,enable_chunked_prefill=True \
   --apply_chat_template \
   --fewshot_as_multiturn \
   --tasks leaderboard \
       <th>Category</th>
       <th>Metric</th>
       <th>deepseek-ai/DeepSeek-R1-Distill-Llama-8B</th>
+      <th>neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic</th>
       <th>Recovery</th>
     </tr>
   </thead>