Instructions to use glacio-dev/Qwen1.5-7B-Chat-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use glacio-dev/Qwen1.5-7B-Chat-Q4 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("glacio-dev/Qwen1.5-7B-Chat-Q4") 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 glacio-dev/Qwen1.5-7B-Chat-Q4 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "glacio-dev/Qwen1.5-7B-Chat-Q4"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "glacio-dev/Qwen1.5-7B-Chat-Q4" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "glacio-dev/Qwen1.5-7B-Chat-Q4", "messages": [ {"role": "user", "content": "Hello"} ] }'
glacio-dev/Qwen1.5-7B-Chat-Q4
This model was converted to MLX format from Qwen/Qwen1.5-7B-Chat.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("glacio-dev/Qwen1.5-7B-Chat-Q4")
response = generate(model, tokenizer, prompt="hello", verbose=True)
- Downloads last month
- 28
Model size
2B params
Tensor type
F16
·
U32 ·
Hardware compatibility
Log In to add your hardware
Quantized