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

Alejandroolmedo/DeepScaleR-1.5B-Preview-Q4-mlx

The Model Alejandroolmedo/DeepScaleR-1.5B-Preview-Q4-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-Q4-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)