Instructions to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

Use Docker

docker model run hf.co/end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-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": "end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

Ollama
How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS
```

Unsloth Studio

How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF to start chatting

How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

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": "end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

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 "end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS" \
  --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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF with Docker Model Runner:
```
docker model run hf.co/end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS
```

Lemonade

How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

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 end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled IQ4_XS GGUF

Unofficial community IQ4_XS GGUF quantization of hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled.

This repository contains a local-inference GGUF quantization for llama.cpp-compatible runtimes.

Source Models

Source fine-tune: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
Base model: Qwen/Qwen3.6-35B-A3B
License: Apache 2.0, following the source model

Quantization

Format: GGUF
Quantization: IQ4_XS
Quantized with: llama.cpp / llama-quantize
Source precision used locally: BF16 GGUF
Output size: about 18 GB
Reported quant size: 18051.46 MiB
Reported BPW: 4.37

Source Model Summary

The source model is a reasoning SFT fine-tune of Qwen/Qwen3.6-35B-A3B on reasoning-distillation data. It is intended for reasoning-heavy text workflows such as coding assistance, planning, math-style reasoning, and structured analytical responses.

The source fine-tune is text-only. The Qwen3.6 base architecture includes a vision encoder, but this quantized GGUF should be treated as a text-generation runtime artifact.

Local llama.cpp Example

llama-server \
  -m Qwen3.6-35B-A3B-Claude-4.6-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 \
  --temp 0.7 \
  --top-p 0.8 \
  --top-k 20 \
  --min-p 0.0

Attribution and Relationship to Source

This is an unofficial community GGUF quantization.

I did not train or fine-tune the source model. All training credit belongs to the original source model author and the base model authors.

If the original author prefers this quantization to be removed or transferred, please open a discussion on this repository.

Acknowledgements

Source fine-tune: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
Base model: Qwen/Qwen3.6-35B-A3B
GGUF tooling: llama.cpp

Downloads last month: 53

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for end000/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

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

Quantized

(20)

this model