Instructions to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF",
	filename="Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-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 lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-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 lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS

Use Docker

docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-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": "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS

Ollama
How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with Ollama:
```
ollama run hf.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS
```

Unsloth Studio new

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-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 lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-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 lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-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 lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF to start chatting

Pi new

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-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": "Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS
```

Lemonade

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF-IQ4_XS

List all available models

lemonade list

Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF

GGUF quantizations of lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled for use with llama.cpp and LM Studio.

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

llama-server \
  -m Qwen3.6-35B-A3B-Kimi-K2.6-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-Kimi-K2.6-Reasoning-Distilled-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-Kimi-K2.6-Reasoning-Distilled for training details, evaluations, and intended use.

Downloads last month: 4,675

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Adapter

lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled

Quantized

(1)

this model