Instructions to use ASiatras/Chios_Chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ASiatras/Chios_Chatbot 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("ASiatras/Chios_Chatbot") 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 ASiatras/Chios_Chatbot with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ASiatras/Chios_Chatbot"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ASiatras/Chios_Chatbot" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ASiatras/Chios_Chatbot", "messages": [ {"role": "user", "content": "Hello"} ] }'
Meltemi 7B Instruct (Chios dialect) — MLX 4-bit (fused)
This is an MLX-converted 4-bit Meltemi model with a Chios-dialect LoRA adapter fused in, so you can run it without specifying --adapter-path.
The training corpus was built from the pdf24_ocrPdf/ OCR PDFs and cleaned to contain only Greek / polytonic Greek letters (no Latin characters).
Usage
python -m mlx_lm chat --model YOUR_LOCAL_FOLDER_OR_HF_REPO --system-prompt "Μίλα πάντα στη χιώτικη διάλεκτο."
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Model size
1B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for ASiatras/Chios_Chatbot
Base model
ilsp/Meltemi-7B-Instruct-v1
# 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("ASiatras/Chios_Chatbot") 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)