README.md

---
library_name: transformers
license: apache-2.0
base_model: open-thoughts/OpenThinker-32B
tags:
- llama-factory
- full
- generated_from_trainer
- mlx
- mlx-my-repo
datasets:
- open-thoughts/open-thoughts-114k
model-index:
- name: OpenThinker-32B
  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/Qwen2.5-32B-Instruct__**](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)** on the **[**__OpenThoughts-114k__**](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)** dataset.**

*Special thanks to the folks at Open Thoughts for fine-tuning this version of Qwen/Qwen2.5-32B-Instruct. More information about it can be found here:* 

[https://huggingface.co/open-thoughts/OpenThinker-32B](https://huggingface.co/open-thoughts/OpenThinker-32B) (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 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-32B-8bit-mlx) | 8-bit | 34.80 GB | **Best Quality** |
| [MLX] (https://huggingface.co/AlejandroOlmedo/OpenThinker-32B-4bit-mlx) | 4-bit | 18.40 GB | Good Quality|


# AlejandroOlmedo/OpenThinker-32B-8bit-mlx

The Model [AlejandroOlmedo/OpenThinker-32B-8bit-mlx](https://huggingface.co/AlejandroOlmedo/OpenThinker-32B-8bit-mlx) was converted to MLX format from [open-thoughts/OpenThinker-32B](https://huggingface.co/open-thoughts/OpenThinker-32B) 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-32B-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)
```