Instructions to use mlx-community/Devstral-Small-2505-4bit-DWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Devstral-Small-2505-4bit-DWQ 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/Devstral-Small-2505-4bit-DWQ") 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/Devstral-Small-2505-4bit-DWQ 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/Devstral-Small-2505-4bit-DWQ"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Devstral-Small-2505-4bit-DWQ" # 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/Devstral-Small-2505-4bit-DWQ", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
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Download README.md from mlx-community/Devstral-Small-2505-4bit-DWQ: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/mlx-community/Devstral-Small-2505-4bit-DWQ/resolve/main/README.md
- Command line
-
hf download hf://mlx-community/Devstral-Small-2505-4bit-DWQ/README.md
-
curl -L -o README.md https://huggingface.co/mlx-community/Devstral-Small-2505-4bit-DWQ/resolve/main/README.md
1.24 kB
metadata
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: mlx
inference: false
base_model: mlx-community/Devstral-Small-2505-bf16
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: text-generation
tags:
- mlx
mlx-community/Devstral-Small-2505-4bit-DWQ
This model mlx-community/Devstral-Small-2505-4bit-DWQ was converted to MLX format from mlx-community/Devstral-Small-2505-bf16 using mlx-lm version 0.24.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Devstral-Small-2505-4bit-DWQ")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)