--- library_name: transformers base_model: - Jackrong/Qwopus3.6-27B-v2 tags: - gguf - llama.cpp - image-text-to-text - vision - multimodal - text-generation-inference - transformers - unsloth - conversational - qwen3_6 - reasoning - chain-of-thought - lora - sft - agent - tool-use - function-calling - coder license: apache-2.0 language: - en - zh - es - ru - ja pipeline_tag: image-text-to-text datasets: - Jackrong/Claude-opus-4.6-TraceInversion-9000x - Jackrong/Claude-opus-4.7-TraceInversion-5000x - lambda/hermes-agent-reasoning-traces --- 🪐 Qwopus-3.6-27B-Coder Coder SFT Release Agentic Coding & Tool-Use Reasoning Model Fine-Tuned on Qwopus3.6-27B-v2 🧬 Trace Inversion & Negentropy 🧠 27B Dense Model ⚡ Agentic Coding 🛠️ Tool Calling & Agent 🏆 SWE-bench Verified: 67.0% (off-thinking) 💡 What is Qwopus-3.6-27B-Coder? 🪐 Qwopus-3.6-27B-Coder is a reasoning-enhanced agentic coding model built on top of Qwopus3.6-27B-v2 . It inherits the powerful reasoning foundation of the v2 base — which achieved 87.43% MMLU-Pro (300ex) and 75.25% SWE-bench Verified — and further specializes it for agentic code generation, structured tool calling, debugging, and instruction-following in developer workflows. The model is designed to excel at repository-level coding tasks, multi-turn tool orchestration, and complex logical reasoning under realistic agent environments. 🧩 Agentic Coding Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows. 🛠️ Tool Calling Learns from real agent trajectories with tool definitions, tool calls, and environment feedback for robust multi-turn execution. 🧬 Trace Inversion Inherits the full Qwopus training recipe with reconstructed step-by-step reasoning trajectories from Claude Opus. 🚀 27B Scale Dense 27B parameters with native long-context support, delivering deep reasoning with practical single-GPU deployability. > [!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 🧠 1.1 Base Model: Qwopus3.6-27B-v2 Qwopus3.6-27B-v2 is a reasoning-enhanced dense language model built on Qwen3.6-27B . Through a multi-stage curriculum learning pipeline and Trace Inversion augmentation, it achieves strong performance across knowledge, coding, and reasoning benchmarks. This coder variant inherits that foundation and extends it with specialized coding and tool-use data. Attribute Specifications & Details 🧠 Architecture Dense Transformer / 27 Billion Parameters 🏢 Base Developer Alibaba Cloud (DAMO Academy) — Qwen3.6-27B 🎯 Primary Focus Agentic coding, tool-use stability, code debugging, structured instruction following, repository-level tasks 🧬 Distillation Strategy Trace Inversion + high-quality agent trajectories + curriculum SFT 📄 Context Window Native support up to 32K tokens (fine-tuning target); compatible with longer contexts via RoPE/YaRN scaling 🧪 1.2 Hardware Cooperation & Joint Collaboration This project is built in close collaboration and joint effort with engineer Kyle Hessling , whose hardware infrastructure and training support made stable 27B-scale fine-tuning and evaluation possible. 👉 You can follow him for hardware and model training updates on X / Twitter: @KyleHessling1 🦥 1.3 Fine-Tuning Framework (Unsloth) The model training workflow is accelerated and memory-optimized with Unsloth . Special thanks to the Unsloth team for making efficient large-model fine-tuning accessible. 👉 Documentation and fine-tuning guidance: unsloth.ai/docs ⚡ 1.4 MTP Variant: Faster Speculative Decoding A Multi-Token Prediction (MTP) variant of this model is also available, featuring auxiliary prediction heads ( draft=2 ) for speculative decoding. Based on the Qwopus3.6-27B-v2-MTP benchmark, the MTP variant achieved ~1.66x speedup over standard decoding with preserved accuracy. See the Qwopus3.6-27B-v2-MTP model card for detailed MTP performance analysis. 🌟 The custom MTP heads processing pipeline is open-sourced in qwen-mtp-gguf . If you find this toolkit helpful, please consider leaving a star on GitHub! --- ## 📖 2. Background & Motivation 🎯 2.1 Why a 27B Coder Model? The Qwopus coder line has demonstrated strong results at the 4B and 9B scales. The 27B coder variant represents a significant leap in reasoning depth, code generation quality, and tool-use robustness. At 27B parameters, the model has sufficient capacity to internalize complex repository structures, multi-file dependencies, and nuanced tool-calling patterns — while remaining deployable on a single GPU (e.g., RTX 5090). This scale bridges the gap between compact local models and expensive API-based solutions, making it suitable for production agentic coding workflows. 🧬 2.2 Trace Inversion & Agent Behavior Commercial and frontier models often expose only compressed reasoning summaries. Qwopus-style training uses Trace Inversion to reconstruct these compressed "Reasoning Bubbles" into fuller learnable reasoning traces. For coding, this is paired with agent trajectories that include tool definitions, tool calls, and real feedback, teaching the model to reason through interactive work rather than only produce static answers. This model integrates: claude-opus-4.6-traceInversion-9000x : 9,000 high-value, fully reconstructed step-by-step reasoning trajectories. claude-opus-4.7-traceInversion-5000x : 5,000 complex multi-turn logic and mathematics samples optimized for negative entropy reconstruction. lambda/hermes-agent-reasoning-traces : ~10,000 high-quality multi-turn tool-calling trajectories from GLM-5.1 and kimi-4.6 models. 📦 2.3 Special Dataset: Trace Inversion & Agent Traces Trace Inversion: Uses a specialized logical reconstructor, Trace-Inverter-4B , to reverse-engineer compressed reasoning bubbles into complete, step-by-step learnable CoT chains. This approach addresses the "Information Entropy Trap" — where direct imitation of compressed summaries leads to reasoning fractures — by ensuring the model learns continuous, rigorous logical derivations. Agent Traces (lambda/hermes-agent-reasoning-traces): Each sample contains real multi-turn tool execution results (not fabricated outputs), with step-by-step reasoning inside tags. Coverage includes: Terminal & Coding: Script writing, debugging, environment configuration Repository Tasks: Bug fixing, refactoring, code review Browser Automation: Web navigation, scraping, form filling Agent Tools: Memory persistence, task delegation, skill management --- ## 📊 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. ```text [ 🗺️ 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...
