Instructions to use nanzhang/QuantLRM-R1-Qwen3-8B-3-bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nanzhang/QuantLRM-R1-Qwen3-8B-3-bit with Transformers:

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

pipe = pipeline("text-generation", model="nanzhang/QuantLRM-R1-Qwen3-8B-3-bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nanzhang/QuantLRM-R1-Qwen3-8B-3-bit")
model = AutoModelForCausalLM.from_pretrained("nanzhang/QuantLRM-R1-Qwen3-8B-3-bit")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nanzhang/QuantLRM-R1-Qwen3-8B-3-bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nanzhang/QuantLRM-R1-Qwen3-8B-3-bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nanzhang/QuantLRM-R1-Qwen3-8B-3-bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nanzhang/QuantLRM-R1-Qwen3-8B-3-bit

SGLang

How to use nanzhang/QuantLRM-R1-Qwen3-8B-3-bit 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 "nanzhang/QuantLRM-R1-Qwen3-8B-3-bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nanzhang/QuantLRM-R1-Qwen3-8B-3-bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "nanzhang/QuantLRM-R1-Qwen3-8B-3-bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nanzhang/QuantLRM-R1-Qwen3-8B-3-bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nanzhang/QuantLRM-R1-Qwen3-8B-3-bit with Docker Model Runner:
```
docker model run hf.co/nanzhang/QuantLRM-R1-Qwen3-8B-3-bit
```

Add metadata, sample usage, and improve model details

by nielsr HF Staff - opened Feb 10

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+33

-1

nielsr

Feb 10

Hi, I'm Niels from the community science team at Hugging Face.

This PR improves the model card by adding key metadata to enhance discoverability and user experience on the Hub:

pipeline_tag: text-generation: Ensures the model appears in relevant searches.
library_name: transformers: Enables the automated "Use in Transformers" code snippet button.
base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B: Provides clarity on the original model this quantization is based on.

Additionally, I've added a "Sample Usage" section, directly pulling code snippets from the official GitHub repository to help users easily get started with inference. I've also clarified the paper link in the introduction with its full title.

Please review and merge if this looks good!

Add metadata, sample usage, and improve model details2a48d01e

nanzhang changed pull request status to merged Feb 11

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