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---
license: mit
library_name: transformers
datasets:
- AI-MO/NuminaMath-CoT
- KbsdJames/Omni-MATH
- RUC-AIBOX/STILL-3-Preview-RL-Data
- hendrycks/competition_math
language:
- en
base_model: agentica-org/DeepScaleR-1.5B-Preview
tags:
- mlx
---
# About:
**A fine-tuned version of Deepseek-R1-Distilled-Qwen-1.5B that surpasses the performance of OpenAI’s o1-preview with just 1.5B parameters on popular math evaluations.**
*Special thanks to Agentica for fine-tuning this version of Deepseek-R1-Distilled-Qwen-1.5B. More information about it can be found here: [https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview.](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview.)*
# Other Types/Sizes:
| Link | Type | Size| Notes |
|-------|-----------|-----------|-----------|
| [MLX] (https://huggingface.co/Alejandroolmedo/DeepScaleR-1.5B-Preview-8bit-mlx) | 8-bit | 1.90 GB | **Best Quality** |
| [MLX] (https://huggingface.co/Alejandroolmedo/DeepScaleR-1.5B-Preview-6bit-mlx) | 6-bit | 1.46 GB | Better Quality|
| [MLX] (https://huggingface.co/Alejandroolmedo/DeepScaleR-1.5B-Preview-4bit-mlx) | 4-bit | 1.01 GB | Good Quality|
I simply converted it to MLX format with a quantization of 4-bits for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips).
# Alejandroolmedo/DeepScaleR-1.5B-Preview-4bit-mlx
The Model [Alejandroolmedo/DeepScaleR-1.5B-Preview-4bit-mlx](https://huggingface.co/Alejandroolmedo/DeepScaleR-1.5B-Preview-4bit-mlx) was converted to MLX format from [agentica-org/DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview) 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/DeepScaleR-1.5B-Preview-4bit-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)
```
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