Instructions to use Gen-Verse/ReasonFlux-Coder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gen-Verse/ReasonFlux-Coder-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gen-Verse/ReasonFlux-Coder-4B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Gen-Verse/ReasonFlux-Coder-4B") model = AutoModelForCausalLM.from_pretrained("Gen-Verse/ReasonFlux-Coder-4B") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Gen-Verse/ReasonFlux-Coder-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gen-Verse/ReasonFlux-Coder-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gen-Verse/ReasonFlux-Coder-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Gen-Verse/ReasonFlux-Coder-4B
- SGLang
How to use Gen-Verse/ReasonFlux-Coder-4B 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 "Gen-Verse/ReasonFlux-Coder-4B" \ --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": "Gen-Verse/ReasonFlux-Coder-4B", "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 "Gen-Verse/ReasonFlux-Coder-4B" \ --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": "Gen-Verse/ReasonFlux-Coder-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Gen-Verse/ReasonFlux-Coder-4B with Docker Model Runner:
docker model run hf.co/Gen-Verse/ReasonFlux-Coder-4B
Improve model card: add text generation pipeline tag and Github README information
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# Introduction to our ReasonFlux-Coders
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We introduce **ReasonFlux-Coders**, trained with **CURE**, our algorithm for co-evolving an LLM's coding and unit test generation abilities.
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[Paper](https://arxiv.org/abs/2506.03136) | [Code](https://github.com/Gen-Verse/CURE)
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@article{wang2025cure,
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pipeline_tag: text-generation
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# Introduction to our ReasonFlux-Coders
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We introduce **ReasonFlux-Coders**, trained with **CURE**, our algorithm for co-evolving an LLM's coding and unit test generation abilities.
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* **ReasonFlux-Coder-7B** and **ReasonFlux-Coder-14B** outperform similarly sized Qwen Coders, DeepSeek Coders, and Seed-Coders, and naturally integrate into common test-time scaling and agentic coding pipelines.
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* **ReasonFlux-Coder-4B** is our Long-CoT model, outperforming Qwen3-4B while achieving 64.8% efficiency in unit test generation. We have demonstrated its ability to serve as a reward model for training base models via reinforcement learning (see our [paper](https://arxiv.org/abs/2506.03136)).
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[Paper](https://arxiv.org/abs/2506.03136) | [Code](https://github.com/Gen-Verse/CURE)
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## Introduction
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We propose **CURE**, a novel reinforcement learning framework that co-evolves LLM coder and unit tester to improve the overall coding ability of large language models. Trained on just 4.5 K samples, our [ReasonFlux-Coder models](https://huggingface.co/collections/Gen-Verse/reasonflux-coder-6833109ed9300c62deb32c6b) outperform similarly sized Qwen Coder, DeepSeek Coder and Seed Coder. **Also, this is the first open-source Coding-RL project to make everything publicly available—including models, evaluation benchmarks, training and testing datasets, and training codes!**
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## Citation
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```
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@article{wang2025cure,
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