AlejandroOlmedo/OpenThinker-7B-4bit-mlx

About:

A fully open-source family of reasoning models built using a dataset derived by distilling DeepSeek-R1.

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the OpenThoughts-114k dataset dataset. This model improves upon the Bespoke-Stratos-7B model, which used 17k examples (Bespoke-Stratos-17k dataset).

Special thanks to the folks at Open Thoughts for fine-tuning this version of Qwen/Qwen2.5-7B-Instruct. More information about it can be found here:

https://huggingface.co/open-thoughts/OpenThinker-7B (Base Model)

https://github.com/open-thoughts/open-thoughts (Open Thoughts Git Repo)

I simply converted it to MLX format (using mlx-lm version 0.21.4.) with a quantization of 4-bit for better performance on Apple Silicon Macs.

Other Types:

Link	Type	Size	Notes
[MLX] (https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-8bit-mlx)	8-bit	8.10 GB	Best Quality
[MLX] (https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-4bit-mlx)	4-bit	4.30 GB	Good Quality

AlejandroOlmedo/OpenThinker-7B-4bit-mlx

The Model AlejandroOlmedo/OpenThinker-7B-4bit-mlx was converted to MLX format from open-thoughts/OpenThinker-7B using mlx-lm version 0.21.4.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("AlejandroOlmedo/OpenThinker-7B-4bit-mlx")

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)