🔥 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, 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.
This fine-tuning run is inspired by
Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction.
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 on X.
Training Summary
1Qwen/Qwen3.6-35B-A3B
2 -> supervised fine-tuning with LoRA
3 -> merged full model
4 -> 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.
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 for the training stack,
llama.cpp for GGUF tooling, and
Jackrong for the public reasoning-distillation workflow that inspired this fine-tune.
