Instructions to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32 with Transformers:

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

pipe = pipeline("text-generation", model="jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32", trust_remote_code=True, 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32

SGLang

How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32 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 "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32" \
    --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": "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32",
		"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 "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32" \
        --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": "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32 with Docker Model Runner:
```
docker model run hf.co/jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32
```

DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g32 / model-00003-of-00004.safetensors

jkim96

Upload DASH-Q quantized checkpoint (INTNone, gNone)

de8f023 verified about 1 month ago

Download

History Blame Contribute Delete

4.99 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 049033a35c53caa06eb663b5be9b6aa4c78ee67cad882b995f07d7f847edd329
Size of remote file:: 4.99 GB
SHA256:: ad970b7e84f28d4441995039bd915540f8e973bdfd0fc696b9704870052c9dbf

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.