Instructions to use mobbitxt/Cydonia-24B-v4.3-JANG_2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mobbitxt/Cydonia-24B-v4.3-JANG_2L 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("mobbitxt/Cydonia-24B-v4.3-JANG_2L") 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
- LM Studio
- MLX LM
How to use mobbitxt/Cydonia-24B-v4.3-JANG_2L with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mobbitxt/Cydonia-24B-v4.3-JANG_2L"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mobbitxt/Cydonia-24B-v4.3-JANG_2L" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mobbitxt/Cydonia-24B-v4.3-JANG_2L", "messages": [ {"role": "user", "content": "Hello"} ] }'
This is a JANG quantization of TheDrummer/Cydonia-24B-v4.3 using the JANG_2L preset. You need a JANG runtime, this does not work with pure MLX.
Links
- GitHub | HuggingFace | MLX Studio | PyPI | Format Spec
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Model size
24B params
Tensor type
U32
·
F16 ·
Hardware compatibility
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Quantized
Model tree for mobbitxt/Cydonia-24B-v4.3-JANG_2L
Base model
mistralai/Mistral-Small-3.1-24B-Base-2503 Finetuned
TheDrummer/Cydonia-24B-v4.3