Instructions to use mlx-community/Qwen2-Math-1.5B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen2-Math-1.5B-Instruct-4bit 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("mlx-community/Qwen2-Math-1.5B-Instruct-4bit") 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 mlx-community/Qwen2-Math-1.5B-Instruct-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Qwen2-Math-1.5B-Instruct-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Qwen2-Math-1.5B-Instruct-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Qwen2-Math-1.5B-Instruct-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
File size: 654 Bytes
d2b2421 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ---
language:
- en
license: apache-2.0
tags:
- chat
- mlx
pipeline_tag: text-generation
---
# mlx-community/Qwen2-Math-1.5B-Instruct-4bit
The Model [mlx-community/Qwen2-Math-1.5B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2-Math-1.5B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-Math-1.5B-Instruct) using mlx-lm version **0.16.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2-Math-1.5B-Instruct-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|