Text Generation
Transformers
Safetensors
English
Chinese
deep-research
qwen3
sft
rl
agent
web-search
Instructions to use qzdc/QREF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qzdc/QREF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="qzdc/QREF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("qzdc/QREF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use qzdc/QREF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "qzdc/QREF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qzdc/QREF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/qzdc/QREF
- SGLang
How to use qzdc/QREF 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 "qzdc/QREF" \ --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": "qzdc/QREF", "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 "qzdc/QREF" \ --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": "qzdc/QREF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use qzdc/QREF with Docker Model Runner:
docker model run hf.co/qzdc/QREF
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Download README.md from qzdc/QREF: direct link, hf CLI and curl.
- Browser
- Download file 719 Bytes
-
https://huggingface.co/qzdc/QREF/resolve/main/README.md
- Command line
-
hf download hf://qzdc/QREF/README.md
-
curl -L -o README.md https://huggingface.co/qzdc/QREF/resolve/main/README.md
719 Bytes
| license: apache-2.0 | |
| language: | |
| - en | |
| - zh | |
| base_model: | |
| - Qwen/Qwen3-8B | |
| tags: | |
| - deep-research | |
| - qwen3 | |
| - sft | |
| - rl | |
| - agent | |
| - web-search | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| # QREF — Deep-Research Agent (Qwen3-8B SFT + RL) | |
| Final checkpoints of our deep-research / web-agent pipeline, built on top of **Qwen3-8B**. | |
| | Subfolder | Stage | Training step | | |
| |---|---|---| | |
| | [`SFT/`](./tree/main/SFT) | Supervised Fine-Tuning | `global_step = 1099` | | |
| | [`RL/`](./tree/main/RL) | Reinforcement Learning | `global_step = 166` | | |
| Both checkpoints share the same architecture (`Qwen3ForCausalLM`, bfloat16) and tokenizer as the base model. | |
| ## License | |
| Apache-2.0, following the base model's license. | |