[!NOTE] Compatibility scope: The update targets chat-history serialization and template rendering across Hugging Face Jinja, llama.cpp/minja, and OpenAI-compatible tool-call representations. It does not change the trained model weights, coding specialization, vision capability, or MTP architecture.

Original Qwopus3.6-27B-Coder-MTP-GGUF Model Card

The complete original model card is retained below for model background, training details, benchmarks, usage guidance, resources, acknowledgements, and citation.

[!WARNING] Community Release Notice: Qwopus-3.6-27B-Coder is an experimental community release intended for research, evaluation, and agent workflow exploration. It has not undergone full safety evaluation or broad general-domain benchmarking.

[!IMPORTANT] Benchmark Status: The first completed benchmark is SWE-bench Verified full 500 in thinking-off / no-thinking mode, where the Q5_K_M 27B GGUF run resolved 335/500 = 67.0%. Other benchmark suites remain pending and will be updated as testing completes.

💡 1. Base Model, Training Stack & Collaboration

📖 2. Background & Motivation

📊 3. Performance Benchmarks

📊 Evaluation & Performance Metrics

First completed result: SWE-bench Verified full 500, evaluated in no-thinking mode for fast local agentic coding.

⚡

No-Thinking SWE-bench Result This benchmark was intentionally run with thinking disabled. The goal is to show the model's practical coding ability when used as a fast local agent, without relying on long visible reasoning traces. On an RTX 5090 with MTP enabled, the model runs at approximately 100 tokens/sec, making this result especially relevant for interactive development workflows.

SWE-bench Verified 67.0% 335 / 500 resolved

Inference Mode Thinking Off no visible CoT required

Local Throughput ~100 t/s RTX 5090 + MTP

Evaluation Build Q5_K_M 27B GGUF quant

Evaluation setup: SWE-bench Verified full 500, Qwopus-3.6-27B-Coder Q5_K_M GGUF, thinking-off / no-thinking mode. Final score: 335/500 = 67.0%.

💻 3.1 SWE-bench Verified: Full 500 No-Thinking Result

SWE-bench Verified measures whether a model can solve real GitHub issues by editing repository code and passing the hidden tests. In this run, Qwopus-3.6-27B-Coder solved 335 out of 500 verified tasks while running in no-thinking mode, prioritizing direct action quality and local speed over long explicit reasoning.

Metric	Result	Notes
Final score	335/500 = 67.0%	Full SWE-bench Verified 500-task split
Mode	Thinking off	No long visible chain-of-thought during evaluation
Quantization	Q5_K_M GGUF	Local 27B quantized deployment
Throughput	~100 tokens/sec	Observed on RTX 5090 with MTP enabled

🧩 3.2 Repository-Level Breakdown

The result is strongest on practical library-maintenance tasks such as scikit-learn, xarray, requests, and Django, while also showing solid coverage on symbolic mathematics, test infrastructure, documentation tooling, and plotting libraries.

Repository

Resolved

Rate

scikit-learn

27/32

84%

pydata/xarray

18/22

82%

psf/requests

6/8

75%

django

166/231

72%

sympy

48/75

64%

pytest

12/19

63%

sphinx-doc

26/44

59%

matplotlib

20/34

59%

astropy

9/22

41%

pylint

2/10

20%

⚖️ 3.3 SWE-bench Verified Reference Comparison

Important comparison note: the reference scores below are from external model reports and are generally thinking-enabled or harness-specific where noted. Qwopus-3.6-27B-Coder is shown here as a no-thinking, quantized local run, so this table should be read as positioning context rather than a strict same-mode leaderboard.

Model	Thinking Mode	SWE-bench Verified	Context
Qwopus-3.6-27B-Coder	Off / No-thinking	67.0	Q5_K_M, RTX 5090 + MTP, ~100 t/s
OpenAI GPT-5	On	70.1	Thinking-on reference
OpenAI GPT-5 mini	On	59.8	Thinking-on reference
OpenAI GPT-5 nano	On	34.8	Thinking-on reference
GLM-4.7	On	70.6	OpenHands reference
GLM-4.5-Air	On	57.6	OpenHands reference
Qwen3-Coder-30B-A3B-Instruct (2025-07)	Off / No-thinking	70.3	No-thinking reference
Claude 4.0 Opus	On	67.6	Thinking-on reference
Claude 4.5 Opus	On	80.9	Thinking-on reference
Qwen3.6-27B	On	77.2	Thinking-on reference
Qwen3.5-397B-A17B	On	76.2	Thinking-on reference
Qwen3.5-27B	On	75.0	Thinking-on reference
Qwen3.6-35B-A3B	On	73.4	Thinking-on reference
Gemma4-31B	On	52.0	Thinking-on reference
Gemma4-26B-A4B	On	17.4	Thinking-on reference

🎮 3.4 Live Thinking-Disabled Demo: Boat Survival

Kyle Hessling also tested Qwopus-3.6-27B-Coder in a small interactive game environment with thinking disabled. The demo is a practical smoke test for fast decision-making, instruction adherence, and local responsiveness beyond static benchmark tables.

Open the Hugging Face Space View Kyle's reference post

Takeaway: The headline is not that this no-thinking local run beats every thinking-enabled frontier reference. The important result is that a quantized 27B local coder can reach 67.0% on the full SWE-bench Verified split while staying fast enough for interactive agent loops. This makes Qwopus-3.6-27B-Coder a practical option for developers who want strong repository-level repair performance without paying the latency cost of long reasoning mode.

🗺️ 4. Training & Data Pipeline Overview

The training process fuses Trace Inversion data augmentation with a Three-Stage Curriculum Learning pipeline. The core engineering focuses on expanding context length gradually while training on reconstructed reasoning traces and real agent trajectories to keep the output format stable.

       [ 🗺️ Trace Inversion: Reconstructing Distillation Workflow ]

  A. Surrogate Model Training (Trace Inverter)
     Open-source Model (GLM-5.1 / DS-V4) ──► Complete Reasoning Chain ──► [ Qwen3-235B Compression ] ──► Reasoning Bubbles
                                              │                                   │
                                              └──────────► [ Training ] ◄─────────┘
                                                   (Base: Qwen3-4B-Instruct)
                                                   (Result: Trace-Inverter-4B)

  B. Inversion Phase: Reconstructing Claude-4.7-Max
     _______________________________________________________
    |                                                       |
    |  Claude-4.7-Max API ──► Compressed Bubbles + Answer   |
    |_______________________________________________________|
                      │
                      ▼
    [ 🧠 Trace-Inverter-4B (Logic Reconstructor) ] ──► Synthetic Deep Reasoning Trace (Learnable CoT)
                      │
                      ▼
    [ 🧩 Data Splicing ] ◄────────── (Original Prompt + Response)
    (Embed reconstructed CoT in <think> tags, splicing with original prompt/response)
                      │
                      ▼
             (Result: claude-opus-4.6/4.7 inverted sets)

  C. Final Coder SFT Curriculum Pipeline
     ___________________________________________
    |                                           |
    |       Base Model (Qwopus3.6-27B-v2)       |
    |___________________________________________|
                      │
                      ▼
    [ 📦 Phase 1: Format Inception ] ──► [ 🛠️ Phase 2: Agent/Coding Expansion ] ──► [ 🚀 Phase 3: Long-Context SFT ]
      ( < 4096 tokens )                     ( 4096 - 8192 tokens )                     ( 8192 - 32K tokens )
      (Stable <think> format)               (Tool traces + coding tasks)               (Long / multi-turn / replay)
                      │                                                                            │
                      └─────────────────────────────┬──────────────────────────────────────────────┘
                                                    ▼
                                   _______________________________________________
                                  |                                               |
                                  |   🌟 Final Model: Qwopus-3.6-27B-Coder        |
                                  |_______________________________________________|

[!NOTE] Due to the complex and diverse format of agent trajectory datasets, rigorous cleaning and format standardization were applied to ensure data quality.

📚 5. Three-Stage Curriculum Learning

To steadily scale reasoning quality under long-context inference, Qwopus-3.6-27B-Coder uses a curriculum-style data mixture building on the approach proven in the Qwopus coder line. The model is first stabilized on short, clean reasoning samples, then exposed to complex coding and agent traces, and finally reinforced with longer contexts plus replay data.

🎯 6. Recommended Use Cases & Known Limits

[!CAUTION] Deployment note: The model may emit reasoning inside <think> and </think> tags. Front-end applications and agent frameworks should parse or hide these sections where appropriate. For tool calling, ensure the prompt format and system prompt match the training data configuration to activate agent capabilities.

⚠️ 7. Training & Deployment Notes

[!CAUTION] Compatibility Notes

• Tool Calling Format: To activate the model's agent capabilities, ensure the prompt format and system prompt include appropriate tool definitions and match the training data format.
• Reasoning Output Extraction: The model's thinking process is wrapped in <think> and </think> tags. Front-end applications may need to parse and hide these tags.
• Long-Context Usage: For contexts beyond 32K, consider enabling RoPE/YaRN scaling (e.g., --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 in llama.cpp).

📋 8. Benchmark Progress

The first completed evaluation is the no-thinking SWE-bench Verified run reported above. Additional local agentic benchmarks remain pending and will be added after testing.

📚 9. Resources & Guides

👉 GitHub Repository: Jackrong-llm-finetuning-guide Access the repository to dive into the codebase and reproduce our results.

👉 Qwen MTP GGUF Processing Workflow A custom splitting and merging methodology designed specifically for Qwen series Multi-Token Prediction (MTP) heads.

👉 benchlocal Evaluation Framework The evaluation framework used to run the local agentic and coding benchmarks.

👉 Qwopus3.6-27B-v2 Model Card Base model card with full MMLU-Pro, SWE-bench, and throughput benchmarks.

🙏 10. Acknowledgements

Special thanks to:

• The Qwen team for providing the powerful Qwen3.6-27B base model.
• Unsloth for providing the highly efficient fine-tuning framework.
• Kyle Hessling for the close collaboration on hardware, training infrastructure, and evaluation support.
• Open-source datasets and community contributors, particularly lambda/hermes-agent-reasoning-traces for the high-quality agent trajectory data.

📖 11. Citation

@misc{jackrong_qwopus36_27b_coder,
  title        = {Qwopus-3.6-27B-Coder},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Jackrong/Qwopus-3.6-27B-Coder}}
}