Instructions to use RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16")
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-70B-quantized.w4a16")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16")
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

vLLM

How to use RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 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-70B-quantized.w4a16"
# 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-70B-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16

SGLang

How to use RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 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-70B-quantized.w4a16" \
    --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-70B-quantized.w4a16",
		"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-70B-quantized.w4a16" \
        --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-70B-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16
```

nm-research commited on Feb 27, 2025

Commit

671f6fb

verified ·

1 Parent(s): c61000a

Add reasoning evals

Browse files

Files changed (1) hide show

README.md +25 -0

README.md CHANGED Viewed

@@ -172,6 +172,31 @@ lm_eval \
   </thead>
   <tbody>
     <tr>
       <td rowspan="7"><b>OpenLLM V1</b></td>
       <td>ARC-Challenge (Acc-Norm, 25-shot)</td>
       <td>63.65</td>

   </thead>
   <tbody>
     <tr>
+<td rowspan="4"><b>Reasoning</b></td>
+<td>AIME 2024 (pass@1)</td>
+<td>67.83</td>
+<td>65.61</td>
+<td>96.73%</td>
+</tr>
+<tr>
+<td>MATH-500 (pass@1)</td>
+<td>95.29</td>
+<td>95.19</td>
+<td>99.9%</td>
+</tr>
+<tr>
+<td>GPQA Diamond (pass@1)</td>
+<td>65.57</td>
+<td>64.04</td>
+<td>97.67%</td>
+</tr>
+<tr>
+<td><b>Average Score</b></td>
+<td><b>76.23</b></td>
+<td><b>74.95</b></td>
+<td><b>98.23%</b></td>
+</tr>
+    <tr>
       <td rowspan="7"><b>OpenLLM V1</b></td>
       <td>ARC-Challenge (Acc-Norm, 25-shot)</td>
       <td>63.65</td>