Instructions to use giangndm/qwen2.5-omni-3b-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use giangndm/qwen2.5-omni-3b-mlx-8bit 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("giangndm/qwen2.5-omni-3b-mlx-8bit") 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 giangndm/qwen2.5-omni-3b-mlx-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "giangndm/qwen2.5-omni-3b-mlx-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "giangndm/qwen2.5-omni-3b-mlx-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "giangndm/qwen2.5-omni-3b-mlx-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
giangndm/qwen2.5-omni-3b-mlx-8bit
This model giangndm/qwen2.5-omni-3b-mlx-8bit was converted to MLX format from Qwen/Qwen2.5-Omni-3B using mlx-lm version 0.24.0.
Use with mlx (https://github.com/giangndm/mlx-lm-omni)
uv add mlx-lm-omni
# or
uv add https://github.com/giangndm/mlx-lm-omni.git
from mlx_lm_omni import load, generate
import librosa
from io import BytesIO
from urllib.request import urlopen
model, tokenizer = load("giangndm/qwen2.5-omni-3b-mlx-8bit")
audio_path = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/1272-128104-0000.flac"
audio = librosa.load(BytesIO(urlopen(audio_path).read()), sr=16000)[0]
messages = [
{"role": "system", "content": "You are a speech recognition model."},
{"role": "user", "content": "Transcribe the English audio into text without any punctuation marks.", "audio": audio},
]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True)
- Downloads last month
- 62
Hardware compatibility
Log In to add your hardware
Quantized
Model tree for giangndm/qwen2.5-omni-3b-mlx-8bit
Base model
Qwen/Qwen2.5-Omni-3B