Text Generation
MLX
Safetensors
Polish
llama
veterinary
medical
polish
reasoning
clinical-ai
bielik
fine-tuned
sophisticated-merge
model-soup
evolutionary
v3
apple-silicon
clinical
quantized
q8
conversational
8-bit precision
Instructions to use LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8 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("LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8") 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
- MLX LM
How to use LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LibraxisAI/svetliq-11b-v3-evolutionary-preview-mlx-q8", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 209 Bytes
0316baa | 1 2 3 4 | {{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %} |