Duplicate from Jackrong/Qwopus3.5-27B-v3.5-GGUF

Co-authored-by: Jackrong <Jackrong@users.noreply.huggingface.co>

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README.md

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+---
+base_model: unsloth/Qwen3.5-27B
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen3_5
+- reasoning
+- chain-of-thought
+- agent
+- sft
+- code
+- biology
+- chemistry
+license: apache-2.0
+language:
+- en
+- zh
+- ko
+- ja
+- es
+pipeline_tag: image-text-to-text
+---
+
+# 🌟 Qwopus3.5-27B-v3.5
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/9EnS13MSxNU3snpAgEiLq.jpeg)
+
+
+
+## 💡 Model Overview & v3.5 Design
+
+Qwopus3.5-27B-v3.5 is a **data-scaled continuation** of the Qwopus3.5-27B-v3 model.
+
+The training data in v3.5 is expanded to cover a broader range of domains, including mathematics, programming,puzzle-solving,multilingual dialogue,instruction-following, muti-turn interactions,and STEM-related tasks.
+
+---
+
+Qwopus3.5-27B-v3.5 is a reasoning-enhanced model based on **Qwen3.5-27B**, designed for:
+
+- 🧩 Structured reasoning  
+- 🔧 Tool-augmented workflows  
+- 🔁 Multi-step agentic tasks  
+- ⚡ Token-efficient inference  
+
+Compared with Qwopus3.5-v3, **3.5 version does not introduce a new architecture, RL stage, or template redesign**.  
+
+This version is trained with approximately **2× more SFT data**.
+
+---
+
+## 🎯 Motivation & Generalization Insight
+
+The motivation behind v3.5 comes from a simple observation:
+
+> This work is motivated by the hypothesis that scaling high-quality SFT data may further enhance the generalization ability of large language models.
+
+In v3, Qwopus demonstrates that structured reasoning improves both **accuracy and efficiency**:
+
+- Structured reasoning is more effective than simply mimicking long CoT  
+- Act-then-refine is better suited for coding and multi-step tasks  
+- Improved reasoning structure enables more reliable use of existing knowledge 
+
+
+> [!IMPORTANT]  
+>This suggests that the improvement is not simply memorization or dataset overlap. Instead, reasoning SFT helps the model:
+> - 🧠 Better utilize existing knowledge  
+> - 🔍 Activate latent knowledge through structured reasoning  
+> - 🏗️ Learn reasoning procedures, not just output format  
+
+---
+
+## 🔬 Supporting Evidence
+
+Recent work:
+
+**Ren et al., 2026 — *Rethinking Generalization in Reasoning SFT*** ([arXiv:2604.06628](https://arxiv.org/abs/2604.06628))
+
+
+<div align="center">
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/5ZY5R4n81okA9glcV9EJV.png" width="85%"/>
+
+</div>
+
+<p align="center"><em>
+Short-epoch reasoning SFT can underestimate generalization — in-domain gains may appear early, while out-of-domain improvements often require sufficient optimization.
+</em></p>
+
+shows that generalization in reasoning SFT is **not fixed, but conditional** — depending on optimization, data quality, and model capability.
+
+Key takeaways:
+
+- Reasoning SFT can generalize when sufficiently trained (often showing a **dip → recovery** pattern)  
+- **High-quality long-CoT data** enables cross-domain transfer  
+- **Stronger models learn reasoning structure**, not just longer outputs (14B/27B/32B)  
+- Gains are **asymmetric** — reasoning improves, while safety may degrade  
+
+This suggests that reasoning SFT should be viewed as a **dynamic optimization process**, rather than a static training outcome.
+
+---
+
+
+### 📊 Evaluation results
+
+
+<div align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/DR9SRmTBDOl9c4S81jBdn.png" width="85%"/>
+</div>
+
+<p align="center"><em>
+Reasoning-focused SFT improves multi-step reasoning tasks, while introducing mild trade-offs on alignment-sensitive benchmarks.
+</em></p>
+
+
+A third-party benchmark report shows that Qwopus3.5-v3 achieves strong performance across reasoning-heavy tasks, especially on:
+
+- MATH500
+- MMLU-Pro
+- HumanEval
+- GSM8K
+- AIME-style reasoning tasks
+
+However, the same results also suggest a **capability trade-off**: reasoning-focused SFT can improve multi-step reasoning while causing mild regressions on some alignment-sensitive or tool-oriented benchmarks.
+
+This supports the view that Qwopus-v3 shifts the model toward **stronger reasoning efficiency and problem-solving ability**, rather than uniform gains across every benchmark.
+
+### 🌍 Preliminary v3.5 comparison on MMLU-Pro subsets
+
+Due to limited compute, v3.5 was evaluated on the **same 280 questions used for v3**, sampled from **7 selected MMLU-Pro categories**.
+
+On this subset:
+
+| Model  | Correct | Total | Accuracy |
+|--------|--------|-------|----------|
+| **v3**   | 250    | 280   | **89.29%** |
+| **v3.5** | 253    | 280   | **✅ 90.36%** |
+
+**✅ Gain:** **+1.07 percentage points**
+
+This suggests that scaling SFT data in v3.5 brings a **small but measurable improvement** on the controlled MMLU-Pro subset.
+
+Since this is not a full MMLU-Pro evaluation, the result should be viewed as a **preliminary reference**, not a definitive benchmark score.
+
+
+### 🪐 SWE / Agentic Coding Test Report 
+
+![Screenshot 2026-04-16 at 3.16.10 PM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/AsTcE5XOlZc7PqoMYWLyN.png)
+
+
+![Screenshot 2026-04-16 at 3.16.28 PM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/qcR-CnnE4z_5cBqK-i0Wx.png)
+
+
+
+Qwopus3.5-27B-v3.5 was tested on a 44-case SWE-style capability suite covering reasoning, tool calling, structured output, context handling, multilingual responses, programming, and multi-step agentic workflows.
+
+The Q5_K_M GGUF build achieved **43 / 44 passed tests (97.7%)**, including **14 / 15 programming tasks**. The only failure was a unit-test-writing case involving incorrect pytest assertions. Compared with Qwopus3.5-27B-v3, which scored **42 / 44 (95.5%)** on the same suite, v3.5 improved by **+2.2 points**.
+
+The most important gain is in multi-step agentic coding: v3.5 successfully read source code through a tool call, diagnosed a timezone parsing bug, and proposed a fix, while v3 failed to identify the root cause. This suggests that v3.5 is a small but meaningful upgrade over v3, especially for SWE-style workflows involving tool use, code inspection, bug diagnosis, and action planning.
+
+> [!NOTE]
+> Throughput differences are excluded from the model-level comparison because both runs use **Q5_K_M GGUF** builds, where quantization choices and runtime environments can affect speed.
+> 🏷️ **Acknowledgement:** Special thanks to **Kyle Hessling** for running and sharing the SWE-style capability tests for Qwopus3.5-27B-v3.5.  
+> X / Twitter: [@KyleHessling1](https://x.com/KyleHessling1)
+
+---
+## 📚 Resources & Guides
+
+👉 **[GitHub Repository: Jackrong-llm-finetuning-guide](https://github.com/R6410418/Jackrong-llm-finetuning-guide.git)**
+Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
+
+### 📥 Core Technical Document
+**🔗 [Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)](https://github.com/R6410418/Jackrong-llm-finetuning-guide/blob/main/guidePDF/Qwopus3-5-27b-Colab_complete_guide_to_llm_finetuning.pdf)**
+* **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face.
+* **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth.
+
+> **A Note:**
+> My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity. 
+> All training and testing for this project were self-funded. If you find this model or guide helpful, a **Star ⭐️ on GitHub** would be the greatest encouragement. Thank you! 🙏
+
+> [!IMPORTANT]
+> The Claude series model optimizations are named under the **Qwopus3.5 series**, with the latest version being **🌟Qwopus3.5-v3.5**.
+
+---
+
+## ⚠️ Limitations
+
+- Possible overfitting if scaling exceeds optimal regime  
+- Reasoning may still exhibit instability in edge cases  
+- Tool-calling performance depends on environment integration  
+- Not all capabilities are fully benchmarked yet  
+
+
+---
+
+## 🙏 Acknowledgements
+
+Special thanks to:
+
+- Unsloth for efficient fine-tuning  
+- Open-source datasets and community contributors  
+- Researchers exploring reasoning SFT and generalization  
+
+---
+
+## 📖 Citation
+
+```bibtex
+@misc{jackrong_qwopus35_v35,
+  title        = {Qwopus3.5-27B-v3.5},
+  author       = {Jackrong},
+  year         = {2026},
+  publisher    = {Hugging Face}
+}
+```

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