Instructions to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF",
	filename="Qwen3.6-35B-A3B-IQ4_NL.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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M

Ollama
How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with Ollama:
```
ollama run hf.co/Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF to start chatting

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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": "Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 "Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-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 Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M
```

Lemonade

How to use Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-35B-A3B — imatrix GGUF quantizations

GGUF imatrix builds of Qwen/Qwen3.6-35B-A3B.

Quantized with llama.cpp usage: /home/artjoms/llama.cpp/build/bin/llama-quantize [--help] [--allow-requan using importance-matrix calibration on a public multilingual + code + math corpus.

Prompt format

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Available quants

Filename	Quant	Size	Notes
Qwen3.6-35B-A3B-Q8_0.gguf	`Q8_0`	34.37 GB	Practically lossless. Closest to source with significant size cut.
Qwen3.6-35B-A3B-Q6_K_L.gguf	`Q6_K`	26.79 GB	Q6_K with Q8_0 embed/output tensors. Near-lossless top tier.
Qwen3.6-35B-A3B-Q6_K.gguf	`Q6_K`	26.56 GB	Near-lossless quality. Recommended for highest practical fidelity.
Qwen3.6-35B-A3B-Q5_K_L.gguf	`Q5_K_M`	23.32 GB	Q5_K_M with Q8_0 embed/output. High quality with small overhead.
Qwen3.6-35B-A3B-Q5_K_M.gguf	`Q5_K_M`	23.03 GB	High quality, balanced size. Recommended general-purpose.
Qwen3.6-35B-A3B-Q5_K_S.gguf	`Q5_K_S`	22.33 GB	Slightly smaller than Q5_K_M with similar quality.
Qwen3.6-35B-A3B-Q4_K_L.gguf	`Q4_K_M`	20.06 GB	Q4_K_M with Q8_0 embed/output. Sweet spot of quality and size.
Qwen3.6-35B-A3B-Q4_K_M.gguf	`Q4_K_M`	19.71 GB	Best size/quality tradeoff. Recommended default.
Qwen3.6-35B-A3B-Q4_K_S.gguf	`Q4_K_S`	18.52 GB	Compact with minor quality loss versus Q4_K_M.
Qwen3.6-35B-A3B-IQ4_NL.gguf	`IQ4_NL`	18.42 GB	Slightly larger than IQ4_XS. Online repacking for ARM CPU inference.
Qwen3.6-35B-A3B-IQ4_XS.gguf	`IQ4_XS`	17.44 GB	Most efficient sub-Q4. Smaller than Q4_K_S with comparable quality.

Calibration

Imatrix generated from reapmix (community calibration mix) — ~400K tokens — multilingual + code + math. This is the same class of public calibration data used by other community GGUF publishers; no claim of unique calibration is made for this release.

*_L and *_XL variants override the output tensor and/or token embedding to Q8_0 (versus the base type), at small extra disk for typically improved output stability at low bit-rates.

Download

Single file:

hf download Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF --include "Qwen3.6-35B-A3B-Q4_K_M.gguf" --local-dir .

Whole repo:

hf download Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF --local-dir ./Qwen3.6-35B-A3B-gguf

Run

./llama-server -m Qwen3.6-35B-A3B-Q4_K_M.gguf -c 32768 -ngl 99 --host 0.0.0.0 --port 8080

Picking a quant

Q8_0 / Q6_K_L — RAM headroom, want ceiling quality
Q5_K_M / Q4_K_L — workstation default, very small quality loss
Q4_K_M — best general size/quality tradeoff, the default choice
IQ4_XS — smallest with serious quality, for tight RAM
IQ3_M / Q3_K_M — when 8GB-class VRAM is the budget
IQ2_M and below — emergency only, quality degrades visibly

Build info

llama.cpp release: usage: /home/artjoms/llama.cpp/build/bin/llama-quantize [--help] [--allow-requan
Host: iron-z-01 (x86_64)
Generated: 2026-06-10T22:32:10

Credits

Original model by Qwen
Calibration: reapmix (community calibration mix)
llama.cpp by ggerganov and contributors

Downloads last month: 289

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for Krasnopjorovs/Qwen3.6-35B-A3B-Imatrix-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(626)

this model