How to use kbosompem/datalevin-nlq-mlx 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("kbosompem/datalevin-nlq-mlx") 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)
How to use kbosompem/datalevin-nlq-mlx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'kbosompem/datalevin-nlq-mlx');
How to use kbosompem/datalevin-nlq-mlx with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "kbosompem/datalevin-nlq-mlx"
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "kbosompem/datalevin-nlq-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kbosompem/datalevin-nlq-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
https://huggingface.co/kbosompem/datalevin-nlq-mlx/resolve/main/tokenizer.json
hf download hf://kbosompem/datalevin-nlq-mlx/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/kbosompem/datalevin-nlq-mlx/resolve/main/tokenizer.json
