Instructions to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF",
	filename="Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.i1-IQ1_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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 mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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 mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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": "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M

SGLang

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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 mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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 mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF to start chatting

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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": "mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-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 mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

About

weighted/imatrix quants of https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	imatrix	0.3	imatrix file (for creating your own quants)
GGUF	i1-IQ1_S	7.6	for the desperate
GGUF	i1-IQ1_M	8.3	mostly desperate
GGUF	i1-IQ2_XXS	9.6
GGUF	i1-IQ2_XS	10.6
GGUF	i1-IQ2_S	10.8
GGUF	i1-IQ2_M	11.8
GGUF	i1-Q2_K_S	12.3	very low quality
GGUF	i1-Q2_K	13.0	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	13.7	lower quality
GGUF	i1-IQ3_XS	14.6
GGUF	i1-Q3_K_S	15.3	IQ3_XS probably better
GGUF	i1-IQ3_S	15.4	beats Q3_K*
GGUF	i1-IQ3_M	15.5
GGUF	i1-Q3_K_M	16.9	IQ3_S probably better
GGUF	i1-Q3_K_L	18.2	IQ3_M probably better
GGUF	i1-IQ4_XS	18.8
GGUF	i1-Q4_0	19.9	fast, low quality
GGUF	i1-Q4_K_S	20.0	optimal size/speed/quality
GGUF	i1-Q4_K_M	21.3	fast, recommended
GGUF	i1-Q4_1	21.9
GGUF	i1-Q5_K_S	24.1
GGUF	i1-Q5_K_M	24.8
GGUF	i1-Q6_K	28.6	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Downloads last month: 9,351

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

View +1 variant

Model tree for mradermacher/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Adapter

lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

Adapter

(13)

this model

Quantizations

1 model

mradermacher
/

Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-i1-GGUF