Instructions to use Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1"
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 Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1
Run Hermes
hermes
- MLX LM
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-beta-v1", "messages": [ {"role": "user", "content": "Hello"} ] }'
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base_model: Qwen/Qwen3-VL-4B-Instruct
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pipeline_tag: text-generation
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Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
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These models are intended for advanced users who require unrestricted, high-performance language generation.
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# Model Card for Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-
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### Model Description
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Introducing *Josiefied-Qwen3-VL-4B-Instruct-
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**Recommended system prompt:**
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base_model: Qwen/Qwen3-VL-4B-Instruct
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library_name: mlx
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pipeline_tag: text-generation
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tags:
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- mlx
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Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
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These models are intended for advanced users who require unrestricted, high-performance language generation.
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# Model Card for Goekdeniz-Guelmez/Josiefied-Qwen3-VL-4B-Instruct-abliterated-v1
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### Model Description
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Introducing *Josiefied-Qwen3-VL-4B-Instruct-abliterated-v1*, a new addition to the JOSIEFIED family — fine-tuned with a focus on openness and instruction alignment. This is model has been abliterated, and finetuned completely end-to-end on Apple silicon, using MLX.
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**Recommended system prompt:**
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