README.md

metadata

license: cc-by-4.0
language:
  - en
base_model: Qwen/Qwen2.5-1.5B
pipeline_tag: text-generation
library_name: transformers
tags:
  - DeepMiddleGo
  - math-reasoning
  - fine-tuned
  - qwen
model-index:
  - name: Mobile-ReasoningLLM-v0-1.5B
    results:
      - task:
          type: text-generation
          name: Math Reasoning
        dataset:
          name: AIME 2024
          type: aime-2024
        metrics:
          - name: Pass@1 (avg16)
            type: pass@1
            value: 73.7
      - task:
          type: text-generation
          name: Math Reasoning
        dataset:
          name: AIME 2025
          type: aime-2025
        metrics:
          - name: Pass@1 (avg16)
            type: pass@1
            value: 63.8

Mobile-Flash-ReasoningLLM-v0-1.5B

Model Description

Mobile-ReasoningLLM-v0-1.5B is a fine-tuned derivative of Qwen2.5-1.5B, optimized for reasoning tasks in mathematics generation. It supports up to 48K output tokens for math problems. This model is designed for both commercial and non-commercial research use. This repository contains the evluation code of Mobile-ReasoningLLM-v0.1(Mobile-Flash-ReasoningLLM-v0-1.5B) which starts to explore experience learning besides of sparse reward learning in the reinforcement learning after R1-Like reinforcement learning and it's variants including curriculumn learning. In this work, I start to explore the rl training algorithm after pre-training, r1-reinforcement learning, r1-curriculumn reinforcement learning to reduce the difficulty of sparse reward in the RL-Post training stage. It takes about 4 days to update Mobile-ReasoningLLM-v0 to Mobile-Flash-ReasoningLLM-v0-1.5B on 8 NVIDIA A800 80G GPUs.

• Architecture: Dense decoder-only Transformer
• Base Model: Qwen2.5-1.5B
• Parameters: 1.5 billion
• Version: v0 (released October 29, 2025)

Intended Use

• Primary Use: Solving complex math problems.
• Applications: Research, education, software development, and math reasoning tasks.
• Limitations: May not handle ambiguous or poorly formatted inputs well. Ethical use is encouraged to avoid harmful applications.

Benchmarks

The model was post-trained on a hybrid dataset (automated, human, synthetic) including:

• Math datasets: AIME 2024, AIME 2025

Evaluation

The model was evaluated on the following benchmarks, achieving strong performance pass1@avg16:

Model	AIME24	AIME25
Qwen3-0.6B-base	11.3	17.0
MobileLLM-R1-1B	15.5	16.3
DeepSeek-Qwen-1.5B	29.1	23.4
FastCurl-1.5B-V3	49.6	32.9
Open-Nemotron-1.5B	49.7	40.4
Mobile-ReasoningLLM-v0-1.5B	63.1	49.6
Mobile-Flash-ReasoningLLM-v0-1.5B	73.7	63.8
Qwen3-1.7B	47.0	37.0

How to Use

Requirements

• Library: transformers, torch, vLLM or TensorRT-LLM
• Hardware: Tested on NVIDIA 8xA800-80GB GPUs
• Environment: Python 3.10+ (e.g., Conda hug environment)

Inference Example

import transformers
import torch
model_id = "deepgo/Mobile-ReasoningLLM-v0.1-1.5B"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)
# Math problem prompt
prompt = """Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}."""
temperature=0.7 max-length=48,000 is recommend.