Instructions to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF",
	filename="huihui-qwen35-4b-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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": "hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M

Ollama
How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with Ollama:
```
ollama run hf.co/hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF to start chatting

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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": "hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-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 hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotdogs/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

hotdogs commited on May 14

Commit

2541b90

verified ·

1 Parent(s): bf6852f

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+---
+license: apache-2.0
+language:
+  - en
+  - zh
+  - multilingual
+tags:
+  - qwen3.5
+  - qwen35
+  - gguf
+  - multimodal
+  - vision
+  - image-text-to-text
+  - abliterated
+  - uncensored
+  - llama.cpp
+  - 4b
+pipeline_tag: image-text-to-text
+base_model:
+  - huihui-ai/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated
+  - Qwen/Qwen3.5-4B
+library_name: llama.cpp
+---
+# Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated — GGUF
+GGUF conversion of [huihui-ai/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated) for use with [llama.cpp](https://github.com/ggerganov/llama.cpp).
+## Credits
+| Role | Model / Author |
+|---|---|
+| **Base LLM** | [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) — Alibaba Qwen Team |
+| **Abliterated (uncensored)** | [huihui-ai/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.5-4B-Claude-4.6-Opus-abliterated) — Huihui AI |
+| **GGUF Conversion** | [hotdogs](https://huggingface.co/hotdogs) — via [llama.cpp](https://github.com/ggerganov/llama.cpp) |
+🙏 Huge thanks to **Qwen Team** (Alibaba) for the base model, **Huihui AI** for the abliteration, and **ggerganov** for llama.cpp!
+## Model Details
+| Spec | Value |
+|---|---|
+| Parameters | ~4B |
+| Architecture | Qwen3.5 Multimodal (QWEN35) |
+| hiddensize | 2560 |
+| Layers | 32 |
+| Attention Heads | 16 (KV: 4) |
+| Context Length | **262,144** (256K tokens) |
+| FFN Intermediate | 9216 |
+| Vision Encoder | 24 layers, hiddensize=1024, patchsize=16 |
+| Modality | **image-text-to-text** 🖼️➡️📝 |
+| Censorship | **Abliterated** (refusal direction removed) |
+| License | Apache 2.0 |
+## Available Quantizations
+| File | Size | BPW | Quality | Recommended For |
+|---|---|---|---|---|
+| huihui-qwen35-4b-BF16.gguf | 7.9 GB | 16.00 | ⭐ Full | Best quality, 16GB+ VRAM |
+| huihui-qwen35-4b-Q8_0.gguf | 4.2 GB | ~8.00 | ⭐ Very High | Balanced, 8GB+ VRAM |
+| huihui-qwen35-4b-Q4_K_M.gguf | 2.6 GB | 5.13 | ⭐ Good | Low VRAM, 6GB+ VRAM |
+| mmproj-huihui-qwen35-4b-BF16.gguf | 645 MB | — | Vision | **Multimodal projector** (required for images) |
+## Usage
+### Text-only
+./llama-cli -m huihui-qwen35-4b-Q4_K_M.gguf -p "Hello!" -n 256
+### Multimodal (image + text)
+./llama-qwen2vl-cli -m huihui-qwen35-4b-Q4_K_M.gguf --mmproj mmproj-huihui-qwen35-4b-BF16.gguf --image photo.jpg -p "Describe this image"
+### Server (OpenAI-compatible API)
+./llama-server -m huihui-qwen35-4b-Q4_K_M.gguf --mmproj mmproj-huihui-qwen35-4b-BF16.gguf --host 0.0.0.0 --port 8080
+### Python (llama-cpp-python)
+llm = Llama(model_path="huihui-qwen35-4b-Q4_K_M.gguf", n_ctx=32768)
+output = llm("Hello!", max_tokens=128)
+## About Abliteration
+This model has undergone **directional ablation** — a technique that removes the "refusal direction" from the model's activation space (Arditi et al. 2024). The model will not refuse questions that base Qwen3.5 would normally decline.
+**Use responsibly.** Ensure your use case complies with applicable laws.
+## Conversion Notes
+- Converted with llama.cpp convert_hf_to_gguf.py
+- BF16 output type
+- QWEN35 architecture, Qwen3VLVisionModel for mmproj
+- Metadata preserved from source model