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

🌟 Qwopus3.5-27B-v3.5

💡 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)

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<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

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<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:

✅ 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

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 Q5KM 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

📚 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

@misc{jackrong_qwopus35_v35,
  title        = {Qwopus3.5-27B-v3.5},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face}
}