初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/ReasonFlux-Qwen3-dpo
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- Gen-Verse/ReasonFlux-V2-Reasoner-DPO
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- text-generation-inference
+- code
+- DPO
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/JdFSTIRr6eR0sp9hJ7xAV.png)
+
+# **ReasonFlux-Qwen3-dpo**
+
+> **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.
+> It adopts a **template-augmented reasoning paradigm**, internalizing structured **thought templates** through **iterative hierarchical reinforcement learning** and **direct preference optimization (DPO)**.
+> This design enables the model to reason more transparently, consistently, and adaptively across multi-domain scientific and mathematical tasks.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/ReasonFlux-Qwen3-dpo-GGUF](https://huggingface.co/prithivMLmods/ReasonFlux-Qwen3-dpo-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Template-Augmented Reasoning**
+   Incorporates structured **reasoning templates** that guide step-by-step thinking, improving coherence and reducing hallucinations.
+
+2. **DPO Fine-Tuning with Hierarchical Reinforcement**
+   Leverages **direct preference optimization** along with **iterative reinforcement learning**, internalizing high-quality reasoning behaviors.
+
+3. **Scientific & Mathematical Expertise**
+   Excels at symbolic derivations, step-by-step proofs, and multi-domain STEM reasoning (physics, chemistry, biology, mathematics).
+
+4. **Code Understanding & Generation**
+   Provides detailed coding explanations, debugging support, and optimization hints across multiple programming languages.
+
+5. **Structured Output Mastery**
+   Fluent in producing outputs across **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML** for seamless integration in research and technical workflows.
+
+6. **Efficient Deployment**
+   Lightweight yet powerful, designed for **mid-range GPUs**, **research clusters**, and **edge AI environments**.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/ReasonFlux-Qwen3-dpo"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain how reinforcement learning differs from supervised learning with real-world examples."
+
+messages = [
+    {"role": "system", "content": "You are a reasoning tutor skilled in science, math, and coding."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* Advanced reasoning tutor for mathematics, coding, and scientific research
+* Research assistant capable of structured problem-solving with template-guided reasoning
+* Technical documentation and structured data generation
+* STEM-focused chatbot or API for research and education workflows
+* Deployment in environments requiring transparent reasoning with efficient compute use
+
+## **Limitations**
+
+* Not optimized for casual or creative writing
+* Context limitations may restrict multi-document or full codebase comprehension
+* Specializes in structured reasoning—general chit-chat may underperform
+* Optimized for **clarity of reasoning** rather than **natural conversational tone**