momix-44/qwen3.5-35b-a3b-claude-4.6-opus-reasoning-distilled-gguf overview

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    "base_model": "qwen/Qwen3.5-35B-A3B",
    "tags": [
      "text-generation-inference",
      "transformers",
      "unsloth",
      "qwen3_5_moe",
      "unsloth",
      "qwen",
      "qwen3.5",
      "reasoning",
      "chain-of-thought"
    ],
    "license": "apache-2.0",
    "language": [
      "zh",
      "en",
      "ko"
    ],
    "pipeline_tag": "text-generation",
    "datasets": [
      "nohurry/Opus-4.6-Reasoning-3000x-filtered",
      "Jackrong/Qwen3.5-reasoning-700x"
    ],
    "frontmatter": {
      "base_model": "qwen/Qwen3.5-35B-A3B",
      "tags": [
        "text-generation-inference",
        "transformers",
        "unsloth",
        "qwen3_5_moe",
        "unsloth",
        "qwen",
        "qwen3.5",
        "reasoning",
        "chain-of-thought"
      ],
      "license": "apache-2.0",
      "language": [
        "zh",
        "en",
        "ko"
      ],
      "pipeline_tag": "text-generation",
      "datasets": [
        "nohurry/Opus-4.6-Reasoning-3000x-filtered",
        "Jackrong/Qwen3.5-reasoning-700x"
      ]
    },
    "hero_image_url": "https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/GHkMJL6I383eIwK1qj80K.jpeg",
    "summary": "> 📢 **Release Note** > **Build Environment Upgrades:** > - **Fine-tuning Framework**: **Unsloth 2026.3.3** > - **Core Dependencies**: **Transformers 5.2.0** > - Compared to the original model, **autonomy and stability are significantly improved**. !HB8AleUaMAArNyM",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: qwen/Qwen3.5-35B-A3B\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- qwen3_5_moe\n- unsloth\n- qwen\n- qwen3.5\n- reasoning\n- chain-of-thought\nlicense: apache-2.0\nlanguage:\n- zh\n- en\n- ko\npipeline_tag: text-generation\ndatasets:\n- nohurry/Opus-4.6-Reasoning-3000x-filtered\n- Jackrong/Qwen3.5-reasoning-700x\n---\n\n# 🌟 Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled\n\n> 📢 **Release Note**\n> **Build Environment Upgrades:**\n> - **Fine-tuning Framework**: **Unsloth 2026.3.3** \n> - **Core Dependencies**: **Transformers 5.2.0**\n> - Compared to the original model, **autonomy and stability are significantly improved**.\n\n![HB8AleUaMAArNyM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/GHkMJL6I383eIwK1qj80K.jpeg)\n\n\n## 💡 Model Introduction\n**Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled** is a highly capable reasoning model fine-tuned on top of the powerful Qwen3.5 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. \n\nThrough Supervised Fine-Tuning (SFT) focusing specifically on structured reasoning logic, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted `<think>` tags, and ultimately delivering precise, nuanced solutions. \n\n### 🧠 Example of Learned Reasoning Scaffold（Example）\n\nThe model includes targeted optimizations addressing Qwen3.5’s tendency toward excessive transitional or repetitive reasoning on simple queries. Through deep distillation and structural imitation of Claude-4.6-Opus reasoning chains, the model adopts a more efficient structured thinking pattern:  \n**“Let me analyze this request carefully: 1..2..3...”.**  \nThis streamlined reasoning paradigm significantly reduces redundant cognitive loops while preserving deep analytical capacity, resulting in substantially improved inference efficiency.\n\n```text\nLet me analyze this request carefully:\n\n1. Identify the core objective of the problem.\n2. Break the task into clearly defined subcomponents.\n3. Evaluate constraints and edge cases.\n4. Formulate a step-by-step solution plan.\n5. Execute the reasoning sequentially and verify consistency.\n            .\n            .\n            .\n```\n\n## 🗺️ Training Pipeline Overview\n\n```text\nBase Model (Qwen3.5-35B-A3B)\n │\n ▼\nSupervised Fine-Tuning (SFT) + LoRA\n │\n ▼\nFinal Model (Claude-4.6-Opus-Reasoning-Distilled,text-only)\n```\n\n## 📋 Stage Details\n\n### 🔹 Supervised Fine-Tuning (SFT)\n- **Objective:** To inject high-density reasoning logic and establish a strict format for problem-solving involving an internal thinking state prior to outputting the final response.\n- **Methodology:** We utilized **Unsloth** for highly efficient memory and compute optimization. A critical component of this stage is 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### 📚 All Datasets Used\nThe dataset consists of high-quality, filtered reasoning distillation data:\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 Claude 4.6 Opus reasoning trajectories. |\n| [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) | Injecting high-intensity, structured reasoning instances. |\n| [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) | Additional curated reasoning samples designed to strengthen structured step-by-step problem solving and improve reasoning diversity. |\n\n## 🌟 Core Skills & Capabilities\n1. **Modular & Structured Thinking:** Inheriting traits from Opus-level reasoning, the model demonstrates confident parsing of the prompt, establishing an outlined plan in its `<think>` block sequentially rather than exploratory \"trial-and-error\" self-doubt.\n2. **Extended Context Support:** Fine-tuned smoothly with an 8192 context window allowing complex multi-step reasoning traces to exist gracefully within memory limits.\n\n## ⚠️ Limitations & Intended Use\n- **Hallucination Risk:** While reasoning is strong, the model remains an autoregressive LLM; external facts provided during the thinking sequence may occasionally contain hallucinations if verifying real-world events.\n- **Intended Scenario:** Best suited for offline analytical tasks, coding, math, and heavy logic-dependent prompting where the user needs to transparently follow the AI's internal logic.\n- **Preview Version Notice:** Because this model is relatively new and intentionally lightweight, the surrounding ecosystem — including inference templates, fine-tuning pipelines, routing configurations, and tooling integrations — may not yet be fully mature or standardized. As a result, users may encounter occasional bugs, compatibility inconsistencies, or integration edge cases. The current release should be considered a preview build while the broader architectural stack and supporting utilities continue to stabilize and improve.\n\n### ⚠️ Training Disclaimer\n\nDuring the fine-tuning process, the Triton kernel required approximately **131072 bytes of shared memory per CUDA block**. On some GPUs this exceeded the available shared memory limits, which caused kernel execution issues. To ensure training stability and proper kernel execution, the fine-tuning was therefore conducted on **80GB VRAM GPUs**.\n\nThis model was fine-tuned using a **LoRA-based parameter-efficient training strategy**, where only a small subset of parameters were updated. In total, **465,551,360 parameters were trainable out of 35,572,733,296 total parameters**, corresponding to **approximately 1.31% of the model being trained**.\n\nDuring training, the loss curve exhibited noticeable fluctuations, which is common in LoRA-based reasoning distillation tasks. However, the overall trend remained **consistently decreasing**, with the training loss eventually converging to approximately **0.384**.\n## 🙏 Acknowledgements\nSignificant thanks to the [Unsloth AI](https://unsloth.ai/) team for making rapid fine-tuning of MoE and large LLM models accessible. Additionally, we acknowledge Qwen internally, and the open-source community developers producing exceptional distilled datasets (`nohurry` and `TeichAI`).\n\n## 📖 Citation\n\nIf you use this model in your research or projects, please cite:\n\n```bibtex\n@misc{jackrong_qwen35_opus_distilled,\n  title        = {Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled},\n  author       = {Jackrong},\n  year         = {2026},\n  publisher    = {Hugging Face},\n  howpublished = {\\url{https://huggingface.co/Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled}}\n}\n```\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "safetensors",
    "gguf",
    "qwen3_5_moe",
    "image-text-to-text",
    "text-generation-inference",
    "unsloth",
    "qwen",
    "qwen3.5",
    "reasoning",
    "chain-of-thought",
    "text-generation",
    "conversational",
    "zh",
    "en",
    "ko",
    "dataset:nohurry/Opus-4.6-Reasoning-3000x-filtered",
    "dataset:Jackrong/Qwen3.5-reasoning-700x",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us"
  ],
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  "last_modified": "2026-03-10T16:41:14.000Z",
  "created_at": "2026-03-08T18:38:27.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
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