README.md

---
base_model: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
license: apache-2.0
language:
- en
datasets:
- nohurry/Opus-4.6-Reasoning-3000x-filtered
- Jackrong/Qwen3.5-reasoning-700x
- Roman1111111/claude-opus-4.6-10000x
tags:
- gguf
- llama.cpp
- qwen
- qwen3.6
- qwen3_5_moe
- moe
- reasoning
- chain-of-thought
- conversational
- quantized
- unsloth
- text-generation
quantized_by: hesamation
model-index:
- name: Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      type: TIGER-Lab/MMLU-Pro
      name: MMLU-Pro
      split: test
    metrics:
    - type: exact_match
      name: exact_match, custom-extract, limited sample on source merged model
      value: 75.71
---

# 🔥 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF

GGUF quantizations of [`hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled`](https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled), a reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on Claude Opus 4.6-style chain-of-thought distillation data.

The source fine-tune is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples. Treat these GGUF files as text-generation/runtime quantizations of the merged fine-tuned checkpoint.

- **Developed by:** [@hesamation](https://x.com/Hesamation)
- **Source model:** [`hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled`](https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled)
- **Base model:** [`Qwen/Qwen3.6-35B-A3B`](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)
- **License:** apache-2.0

This fine-tuning run is inspired by [Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled), including the notebook/training workflow style and Claude Opus reasoning-distillation direction.

[![Follow on X](https://img.shields.io/badge/Follow%20on-X-000000?style=flat-square&logo=x)](https://x.com/Hesamation) [![Discord](https://img.shields.io/badge/Discord-Open%20Source%20AI%20Builders-5865F2?style=flat-square&logo=discord&logoColor=white)](https://discord.gg/vtJykN3t)

## Available GGUF Quantizations

This repo is intended to host the following GGUF variants. Files are uploaded as each quantization finishes.

| Quant | Typical use |
| --- | --- |
| `Q4_K_M` | Smallest practical general-purpose quant for local inference |
| `Q5_K_M` | Better quality/size balance than Q4 |
| `Q6_K` | Higher-quality quant when VRAM/RAM budget allows |
| `Q8_0` | Largest quant here; closest to source quality among these options |

## Benchmark Results

The benchmark below was run on the merged source model, not separately on each GGUF quant. Quantization can change scores, especially at lower bitrates, so treat this as source-checkpoint context.

The MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark.

| Benchmark | Harness | Samples per model | Setting | Metric | Base model | Source merged model | Delta |
| --- | --- | ---: | --- | --- | ---: | ---: | ---: |
| MMLU-Pro overall | lm-evaluation-harness | 70 | `--limit 5` across 14 subjects | exact_match, custom-extract | 42.86% | 75.71% | +32.85 pp |

Base model: `Qwen/Qwen3.6-35B-A3B`. Source merged model: `hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled`.

> [!WARNING]
> **Community benchmarks welcome**
>
> To better understand this fine-tuned model and its GGUF quantizations, I welcome independent benchmark results. If you run evaluations, please include the benchmark name, harness/script, sample count, decoding settings, quant file, and raw logs or result files when possible.
>
> Share results by opening a PR/discussion or DMing [@hesamation](https://x.com/Hesamation) on X.

## Training Summary

```text
Qwen/Qwen3.6-35B-A3B
  -> supervised fine-tuning with LoRA
  -> merged full model
  -> GGUF quantization with llama.cpp
```

| Setting | Value |
| --- | --- |
| Fine-tuning method | Supervised fine-tuning with LoRA |
| LoRA target | Attention-only modules |
| LoRA rank / alpha | 32 / 32 |
| Micro-batch size | 1 |
| Gradient accumulation | 32 |
| Epochs | 2 |
| Completed steps | 762 / 762 |
| Final reported training loss | 0.3362497625740494 |
| Dataset max tokens | 8192 |
| Max sequence length | 32768 |

## Training Data

The source model samples and normalizes reasoning conversations from three datasets, then renders them with the `qwen3-thinking` chat template and response-only SFT masking.

| Dataset | Requested sample count | Role |
| --- | ---: | --- |
| [`nohurry/Opus-4.6-Reasoning-3000x-filtered`](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | 3,900 | Claude Opus reasoning trajectories |
| [`Jackrong/Qwen3.5-reasoning-700x`](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) | 700 | Curated Qwen reasoning samples |
| [`Roman1111111/claude-opus-4.6-10000x`](https://huggingface.co/datasets/Roman1111111/claude-opus-4.6-10000x) | 9,633 | Additional Claude Opus reasoning examples |

## Intended Use

These GGUF files are intended for local or server-side text inference through runtimes that support GGUF and the Qwen3.6 architecture, such as recent `llama.cpp` builds. Choose the quantization based on your memory budget and quality target.

Because the fine-tune is text-only, image/video behavior should be treated as inherited from the base model rather than improved by this training run.

## Acknowledgements

Thanks to the Qwen team for the base model, [Unsloth](https://github.com/unslothai/unsloth) for the training stack, [llama.cpp](https://github.com/ggerganov/llama.cpp) for GGUF tooling, and [Jackrong](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled) for the public reasoning-distillation workflow that inspired this fine-tune.