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---
library_name: transformers
license: apache-2.0
base_model: open-thoughts/OpenThinker-7B
tags:
- llama-factory
- full
- generated_from_trainer
- mlx
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 **0.21.4**.) 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](https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-4bit-mlx) was
converted to MLX format from [open-thoughts/OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B)
using mlx-lm version **0.21.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
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)
```
|