---
library_name: transformers
license: apache-2.0
base_model: open-thoughts/OpenThinker-7B
tags:
- llama-factory
- full
- generated_from_trainer
- mlx
- mlx-my-repo
datasets:
- open-thoughts/open-thoughts-114k
model-index:
- name: OpenThinker-7B
  results: []
---

# **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__**](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)** on the **[**__OpenThoughts-114k dataset__**](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)** dataset. This model improves upon the **[**__Bespoke-Stratos-7B model__**](https://huggingface.co/bespokelabs/Bespoke-Stratos-7B)**, which used 17k examples (**[**__Bespoke-Stratos-17k dataset__**](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k)**).**

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

[**__https://github.com/open-thoughts/open-thoughts__**](https://github.com/open-thoughts/open-thoughts) (Open Thoughts Git Repo)

I simply converted it to MLX format (using mlx-lm version **0.20.5**.) with a quantization of 8-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-8bit-mlx

The Model [AlejandroOlmedo/OpenThinker-7B-8bit-mlx](https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-8bit-mlx) was converted to MLX format from [open-thoughts/OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B) using mlx-lm version **0.20.5**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("AlejandroOlmedo/OpenThinker-7B-8bit-mlx")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```