khtsly/qwen3.5-27b-claude-4.6-opus-distilled-32k-gguf overview

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    "base_model": "Qwen/Qwen3.5-27B",
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      "reasoning",
      "chain-of-thought",
      "lora",
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    "datasets": [
      "nohurry/Opus-4.6-Reasoning-3000x-filtered"
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      "language": [
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      "license": "apache-2.0",
      "base_model": "Qwen/Qwen3.5-27B",
      "tags": [
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        "reasoning",
        "chain-of-thought",
        "lora",
        "luau",
        "gguf",
        "llama.cpp",
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      "datasets": [
        "nohurry/Opus-4.6-Reasoning-3000x-filtered"
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    "readme_markdown": "---\nlanguage:\n- en\n- zh\nlicense: apache-2.0\nbase_model: Qwen/Qwen3.5-27B\ntags:\n- unsloth\n- qwen\n- qwen3.5\n- reasoning\n- chain-of-thought\n- lora\n- luau\n- gguf\n- llama.cpp\n- vision-language-model\ndatasets:\n- nohurry/Opus-4.6-Reasoning-3000x-filtered\npipeline_tag: image-text-to-text\n---\n\n# Qwen3.5-27B-Claude-4.6-Opus-Distilled-32k\n\n## # Model Introduction\n**Qwen3.5-27B-Claude-4.6-Opus-Distilled-32k** is a highly capable reasoning and coding model fine-tuned on top of the `Qwen3.5-27B` hybrid dense architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions, with a specialized focus on extended output generation and improved Luau programming capability.\n\nThrough Supervised Fine-Tuning (SFT) focusing on structured reasoning logic and a massive 32k output length max, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted `<think>` tags, and delivering comprehensive, nuanced solutions—even for highly extensive generation tasks.\n\n### # Benchmark\n| Benchmark | Baseline (27B) | Distilled (27B) | [Jackrong (27B)](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled) |\n| :--- | :---: | :---: | :---: |\n| GPQA Diamond CoT (0-shot) | 55.05 | **69.69** | 67.67 |\n| ARC-Challenge (25-shot) | 73.80 | **74.65** | 74.39 |\n| AIME 2026 (0-shot) | 0.0 | 16.66 | **33.33** |\n| AIME 2025 (0-shot) | 0.0 | 23.33 | **26.66** |\n| MMLU-CF (0-shot) | 72.30 | **73.75** | - |\n| *Humanities* | 78.28 | **79.80** | - |\n| *Social Sciences* | 70.83 | **71.91** | - |\n| *STEM* | 65.74 | **67.25** | - |\n| *Other* | 74.35 | **76.02** | - |\n| IFEval (0-shot) | 38.13 | 38.13 | **38.81** |\n| *Prompt-Level* | 31.05 | 31.05 | **31.7** |\n| *Instruction-Level* | 45.20 | 45.20 | **45.92** |\n\n*The benchmark is taken in 4-bit using `lm eval`. No chat-template enabled in this run. Higher the score is better.*\n\n## # Training Pipeline Overview\n\n```text\nBase Model (Qwen3.5-27B-FP8)\n │\n ▼\nSupervised Fine-Tuning (SFT) + LoRA (r=64, α=128)\n(Response-Only Training masked on \"<|im_start|>assistant\\n\")\n(Max 32k Output Length)\n+\nnohurry/Opus-4.6-Reasoning-3000x-filtered + luau coding samples\n(shuffled)\n │\n ▼\nFinal Model (Qwen3.5-27B-Claude-4.6-Opus-Distilled-32k)\n```\n\n### # Supervised Fine-Tuning (SFT) Details\n- **Objective:** To inject high-density reasoning logic, establish a strict internal thinking format prior to output, and train the model to sustain coherent generation over exceptionally long contexts.\n- **Extended Output Capacity:** Trained specifically to handle up to **32,768 (32k) tokens of maximum output** (recommended), allowing for massive codebases, comprehensive essays, and deeply detailed reasoning traces.\n- **LoRA Configuration:** Fine-tuned efficiently using LoRA (16-bit) with **Rank (r) set to 64** and **Alpha (α) set to 128**, ensuring strong adaptation and retention of complex Opus-level logic.\n- **Method:** Utilized **Unsloth** for highly efficient memory and compute optimization. A critical component was the `train_on_responses_only` strategy, masking instructions so the loss is purely calculated over the generation of the `<think>` sequences and the subsequent solutions.\n- **Format Enforcement:** All training samples were systematically normalized so the model strictly abides by the structure `<think> {internal reasoning} </think>\\n {final answer}`.\n\n### # Datasets Used\nThe dataset consists of highly curated, filtered reasoning distillation data, supplemented by specialized coding sets:\n\n| Dataset Name | Description / Purpose |\n|--------------|-----------------------|\n| [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | Provides comprehensive, high-quality Claude 4.6 Opus reasoning trajectories. |\n| **Custom Luau Coding Set** | 75 meticulously crafted various Luau coding samples generated natively by Opus 4.6, injecting specialized high-quality domain knowledge for Roblox/Luau scripting capability. |\n\n### # Training Compute & Loss Curve\n* **Hardware:** 1x NVIDIA H100 (80GB)\n* **Training Duration:** ~50 Minutes\n* **Estimated Total Cost:** $12.00\n* **Distillation Efficacy:** The loss curve demonstrated a strong, healthy downward trajectory throughout the run, confirming successful knowledge transfer from the Opus teacher model. The model converged steadily from an initial loss of **0.588880** down to a final loss of **0.176861**.\n\n## # Core Skills & Capabilities\n1. **Massive Output Generation:** Capable of sustaining coherent, high-quality output for up to 32k tokens, making it ideal for writing extensive code, documentation, or deep analytical reports in a single shot.\n2. **Modular & Structured Thinking:** Inheriting traits from Opus-level reasoning, the model confidently parses prompts and outlines plans sequentially in its `<think>` block, avoiding exploratory \"trial-and-error\" self-doubt.\n3. **Luau Proficiency:** Thanks to the targeted 75-sample dataset, the model exhibits improved syntax adherence and logic formulation for the Luau programming language.\n\n## # Limitations & Intended Use\n- **Hallucination Risk:** While reasoning is strong, the model remains an autoregressive LLM. Extended 32k outputs may experience minor drift or hallucinate external facts if relying on real-world verification without grounding.\n- **Intended Scenario:** Best suited for offline analytical tasks, heavy coding (especially Luau), math, and logic-dependent prompting where the user needs transparent internal logic and extremely long, continuous outputs.\n\n## # Acknowledgements\n\nThis model's development was made possible by the foundational tools and contributions from the broader AI ecosystem:\n\n* **[Unsloth AI](https://unsloth.ai/):** For their state-of-the-art framework, enabling highly efficient, memory-optimized LoRA tuning and seamless 32k context scaling.\n* **Qwen Team:** For engineering the robust and highly capable `Qwen3.5-27B` dense base architecture.\n* **Dataset Contributors:** Special recognition to `nohurry` for the rigorous curation of the Claude 4.6 Opus reasoning trajectories, which serves as the core cognitive engine for this project's SFT phase.\n\n-https://ko-fi.com/khtsly",
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    "gguf",
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    "region:us",
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  "last_modified": "2026-03-22T20:30:37.000Z",
  "created_at": "2026-03-06T13:29:11.000Z",
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