Instructions to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated 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 TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Use Docker

docker model run hf.co/TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Llama new
LM Studio
Jan

vLLM

How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated",
		"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/TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Ollama
How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with Ollama:
```
ollama run hf.co/TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M
```
Unsloth Desktop

How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with Docker Model Runner:
```
docker model run hf.co/TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M
```

Lemonade

How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Run and chat with the model

lemonade run user.cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated-Q5_K_M

List all available models

lemonade list

Hermes Agent

How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_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 TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_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 "TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated:Q5_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"

cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated

Miroir public et non modifié du fichier Huihui-Qwen3-VL-4B-Instruct-abliterated.Q5_K_M.gguf (Q5_K_M, 2,89 Go) publié par mradermacher/Huihui-Qwen3-VL-4B-Instruct-abliterated-GGUF.

Aucune re-quantification, aucun fine-tuning, aucun changement de template : les poids sont bit pour bit identiques à la source.

sha256  403ffcfda140dedc4e3b116ac4a6833d5055e9aaa3a0e90e8eccb0f0a382b72e
taille  2889515456 octets

Ce que c'est


Type	vision
Paramètres	4,4 B
Architecture	`qwen3vl`
Contexte natif	262 144 tokens
Quantification	Q5_K_M (~5,4 bits/poids)
Poids sur disque	2,89 Go
Usage visé	vision légère — CPU possible
Licence	apache-2.0

Généalogie

Étape	Dépôt
Modèle racine	`Qwen/Qwen3-VL-4B-Instruct`
Abliteration (fp16)	`huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated`
Quantification GGUF	`mradermacher/Huihui-Qwen3-VL-4B-Instruct-abliterated-GGUF`
Miroir (ce dépôt)	`TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated`

Utilisation

# Téléchargement
hf download TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated Huihui-Qwen3-VL-4B-Instruct-abliterated.Q5_K_M.gguf --local-dir .

# llama.cpp
llama-cli -m Huihui-Qwen3-VL-4B-Instruct-abliterated.Q5_K_M.gguf -p "Bonjour" -c 8192 -ngl 99

Avec Ollama, créez un Modelfile :

FROM ./Huihui-Qwen3-VL-4B-Instruct-abliterated.Q5_K_M.gguf
PARAMETER num_ctx 8192
PARAMETER temperature 0.7

puis ollama create qwen3vl-4b -f Modelfile.

Quantification Q5_K_M

K-quant mixte par blocs de 256 poids : environ 5,3 à 5,7 bits par poids en moyenne (couches d'attention plus précises que les couches FFN, échelles incluses). Repères : Q4_K_M ≈ 4,8 · Q5_K_M ≈ 5,4 · Q6_K ≈ 6,6 · Q8_0 ≈ 8,5 bits/poids.

Attribution

Modèle original : Qwen/Qwen3-VL-4B-Instruct
Abliteration : huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated
Quantification GGUF : mradermacher/Huihui-Qwen3-VL-4B-Instruct-abliterated-GGUF
Licence apache-2.0, conservée telle quelle.

Ce dépôt n'est qu'un point de distribution redondant. Tout le crédit revient aux auteurs ci-dessus ; en cas de divergence, la source fait foi.

Downloads last month: 11

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

5-bit

Model tree for TheShellMaster/cypher-Huihui-Qwen3-VL-4B-Instruct-abliterated

Base model

Qwen/Qwen3-VL-4B-Instruct

Finetuned

huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated

Quantized

(14)

this model