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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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.
|