README.md

metadata

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.

I simply converted it to MLX format for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips).

Alejandroolmedo/DeepScaleR-1.5B-Preview-Q8-mlx

The Model Alejandroolmedo/DeepScaleR-1.5B-Preview-Q8-mlx was converted to MLX format from agentica-org/DeepScaleR-1.5B-Preview using mlx-lm version 0.20.5.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Alejandroolmedo/DeepScaleR-1.5B-Preview-Q8-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)