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--- |
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license: apache-2.0 |
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datasets: |
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- Gen-Verse/ReasonFlux-V2-Reasoner-DPO |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen3-1.7B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- trl |
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- DPO |
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--- |
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# **ReasonFlux-Qwen3-dpo** |
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> **ReasonFlux-Qwen3-dpo** is a fine-tuned version of **Qwen3-1.7B**, trained on the [**Gen-Verse/ReasonFlux-V2-Reasoner-DPO**](https://huggingface.co/datasets/Gen-Verse/ReasonFlux-V2-Reasoner-DPO) dataset. |
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> It adopts a **template-augmented reasoning paradigm**, internalizing structured **thought templates** through **iterative hierarchical reinforcement learning** and **direct preference optimization (DPO)**. |
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> This design enables the model to reason more transparently, consistently, and adaptively across multi-domain scientific and mathematical tasks. |
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> \[!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/ReasonFlux-Qwen3-dpo-GGUF](https://huggingface.co/prithivMLmods/ReasonFlux-Qwen3-dpo-GGUF) |
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--- |
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## **Key Features** |
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1. **Template-Augmented Reasoning** |
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Incorporates structured **reasoning templates** that guide step-by-step thinking, improving coherence and reducing hallucinations. |
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2. **DPO Fine-Tuning with Hierarchical Reinforcement** |
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Leverages **direct preference optimization** along with **iterative reinforcement learning**, internalizing high-quality reasoning behaviors. |
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3. **Scientific & Mathematical Expertise** |
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Excels at symbolic derivations, step-by-step proofs, and multi-domain STEM reasoning (physics, chemistry, biology, mathematics). |
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4. **Code Understanding & Generation** |
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Provides detailed coding explanations, debugging support, and optimization hints across multiple programming languages. |
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5. **Structured Output Mastery** |
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Fluent in producing outputs across **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML** for seamless integration in research and technical workflows. |
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6. **Efficient Deployment** |
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Lightweight yet powerful, designed for **mid-range GPUs**, **research clusters**, and **edge AI environments**. |
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--- |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/ReasonFlux-Qwen3-dpo" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain how reinforcement learning differs from supervised learning with real-world examples." |
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messages = [ |
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{"role": "system", "content": "You are a reasoning tutor skilled in science, math, and coding."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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--- |
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## **Intended Use** |
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* Advanced reasoning tutor for mathematics, coding, and scientific research |
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* Research assistant capable of structured problem-solving with template-guided reasoning |
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* Technical documentation and structured data generation |
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* STEM-focused chatbot or API for research and education workflows |
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* Deployment in environments requiring transparent reasoning with efficient compute use |
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## **Limitations** |
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* Not optimized for casual or creative writing |
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* Context limitations may restrict multi-document or full codebase comprehension |
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* Specializes in structured reasoning—general chit-chat may underperform |
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* Optimized for **clarity of reasoning** rather than **natural conversational tone** |
