Instructions to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF",
	filename="Qwen3.6-35B-A3B-uncensored-heretic_IQ4_XS.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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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": "k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M

Ollama
How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with Ollama:
```
ollama run hf.co/k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF to start chatting

How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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": "k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-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 k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-35B-A3B-uncensored-heretic Japanese imatrix GGUF

日本語を主体としたImportance MatrixによるGGUF量子化です。

Japanese-focused imatrix GGUF quantizations of llmfan46/Qwen3.6-35B-A3B-uncensored-heretic.

量子化情報

元モデル: llmfan46/Qwen3.6-35B-A3B-uncensored-heretic
ベースモデル: Qwen/Qwen3.6-35B-A3B (Apache 2.0)
量子化ツール: llama.cpp b8892

量子化バリアントについて

ファイル名の末尾に_Lが付いたモデルはtoken embeddingとoutputの重みをQ8で量子化しています。入力トークンの解釈や出力トークンの選択に直接関わる部分の精度がより良く維持されることを期待しています。迷った場合はまず通常のIQ4_XSを選べば問題ないと思いますが、VRAMが16GBなどの場合でもGPUに載せられるレイヤー数が1層減るかどうか程度なのでIQ4_XS_Lを試してみるのもいいかもしれません。

imatrixについて

日本語テキストを主体としたキャリブレーションデータでImportance Matrixを生成しています。英語データをメインに生成されたimatrixと比較して、日本語の生成品質がより良く維持されることを期待しています。 imatrixデータファイル(Qwen3.6-35B-A3B-uncensored-heretic.imatrix)を同梱しているため、他の量子化タイプを生成したい場合にご利用いただけます。

⚠️ 注意 / Disclaimer

このモデルは検閲除去処理が施されたモデルの量子化です。安全フィルターが大幅に緩和されており、有害・不適切なコンテンツを生成する可能性があります。出力内容の利用については利用者自身の責任においてご判断ください。

This is a quantization of an abliterated model with significantly reduced safety filters. Use at your own risk and responsibility.

クレジット

元モデル: llmfan46
ベースモデル: Qwen Team

Downloads last month: 577

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

Model tree for k0ndra/Qwen3.6-35B-A3B-uncensored-heretic-ja-imatrix-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

llmfan46/Qwen3.6-35B-A3B-uncensored-heretic

Quantized

(18)

this model