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

Instructions to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Ollama
How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Ollama:
```
ollama run hf.co/Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
```

Unsloth Studio

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF to start chatting

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
```

Lemonade

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Grach0/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Grach0

hesamation commited on Apr 21

Commit

578dc3d

0 Parent(s):

Duplicate from hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF

Browse files

Files changed (6) hide show

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+---
+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.