Instructions to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-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 k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-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 k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf k0ndra/Qwen3.5-35B-A3B-heretic-v2-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 serve -hf k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf k0ndra/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf k0ndra/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M

Ollama
How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF with Ollama:
```
ollama run hf.co/k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF to start chatting

How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-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 k0ndra/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-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 serve -hf k0ndra/Qwen3.5-35B-A3B-heretic-v2-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/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-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 k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-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 "k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-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"

Qwen3.5-35B-A3B-heretic-v2 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/Qwen3.5-35B-A3B-heretic-v2.

量子化情報

元モデル: llmfan46/Qwen3.5-35B-A3B-heretic-v2
ベースモデル: Qwen/Qwen3.5-35B-A3B (Apache 2.0)
量子化ツール: llama.cpp b8559

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。

（おそらく）英語データをメインにで生成されたimatrixと比較して、低ビット量子化（IQ3/IQ4クラス）において日本語の生成品質をより良く維持することを期待していましたが、no thinkingでの選択式や抽出型などの日本語ベンチマークでは微妙な結果でした。 llmfan46/Qwen3.5-35B-A3B-heretic-v2-GGUFを使用することを推奨します。Q6_K以上ではimatrixによる差異は小さいと思うので、本リポジトリでは低ビット量子化に絞って公開しています。

imatrixデータファイル(Qwen3.5-35B-A3B-heretic-v2.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: 3,309

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

3-bit

4-bit

Model tree for k0ndra/Qwen3.5-35B-A3B-heretic-v2-ja-imatrix-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

llmfan46/Qwen3.5-35B-A3B-ultra-uncensored-heretic

Quantized

(9)

this model