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

bobig/DeepScaleR-1.5B-6.5bit

This works well as a draft model for speculative decoding in LMstudio 3.10 beta

Try it with: mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-4.5bit

you should see 30% faster TPS for math/code prompts even with "thinking" slowing down the Specultive Decoding

The Model bobig/DeepScaleR-1.5B-6.5bit was converted to MLX format from agentica-org/DeepScaleR-1.5B-Preview using mlx-lm version 0.21.4.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("bobig/DeepScaleR-1.5B-6.5bit")

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)