Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF

Instructions to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-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": "KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

Ollama
How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF with Ollama:
```
ollama run hf.co/KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF to start chatting

How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

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": "KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

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 "KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0" \
  --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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0
```

Lemonade

How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

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 KartoffelToby/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

KartoffelToby

lordx64 commited on May 4

Commit

977ab20

0 Parent(s):

Duplicate from lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF

Browse files

Files changed (3) hide show

.gitattributes +36 -0
Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.Q8_0.gguf +3 -0
README.md +60 -0

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+oid sha256:5186ec1d401a26ebc93e9e24e2c67554c26a572fe28e0ae1447e426acc32e8dd
+size 36903140096

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+---
+base_model: lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+- gguf
+- llama.cpp
+- lmstudio
+- reasoning
+- chain-of-thought
+- qwen
+- qwen3.6
+- moe
+- distillation
+quantized_by: lordx64
+license: apache-2.0
+---
+# Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF
+GGUF quantizations of [`lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled`](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled) for
+use with [llama.cpp](https://github.com/ggerganov/llama.cpp) and
+[LM Studio](https://lmstudio.ai/).
+The base model is a reasoning-distilled variant of Qwen3.6-35B-A3B fine-tuned
+to imitate the chain-of-thought style of Claude Opus 4.7. It thinks in explicit
+`<think>...</think>` blocks before producing the final answer.
+## Quant files
+See the file list for all available quant levels. Common choices:
+| File | Quant | Approx size | Use case |
+|---|---|---|---|
+| `*.IQ4_XS.gguf` | IQ4_XS | ~18 GB | Smallest quant with good quality — default pick for LM Studio |
+| `*.Q4_K_M.gguf` | Q4_K_M | ~21 GB | Balanced quality / size |
+| `*.Q5_K_M.gguf` | Q5_K_M | ~25 GB | Higher quality |
+| `*.Q8_0.gguf`   | Q8_0   | ~35 GB | Near-lossless |
+## Running in llama.cpp
+```bash
+llama-server \
+  -m Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.IQ4_XS.gguf \
+  --host 127.0.0.1 --port 18081 \
+  -c 32768 -fa on \
+  --cache-type-k q8_0 --cache-type-v turbo4
+```
+## Running in LM Studio
+Search for `lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-Q8_0-GGUF` inside LM Studio's model browser and pick the quant
+that fits your RAM/VRAM. The model should appear automatically once HF indexes
+this repo.
+## License
+Apache 2.0, inherited from the base model. See
+[`lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled`](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled) for training details,
+evaluations, and intended use.