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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: 90
      - task:
          type: text-generation
          name: Math Reasoning
        dataset:
          name: AIME 2025
          type: aime-2025
        metrics:
          - name: Pass@1 (avg16)
            type: pass@1
            value: 76.7

Mobile-Flash-v1-1.5B

Model Description

Mobile-Flash-v1-1.5B is a fine-tuned derivative of Qwen2.5-1.5B, optimized for reasoning tasks in mathematics generation. It supports up to 40K 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-Flash-v1-1.5B which starts to explore Self-RL learning besides of sparse reward learning in the reinforcement learning. In this work, I start to explore the self-rl training algorithm after pre-training, r1-reinforcement learning, r1-curriculumn reinforcement learning to reduce the difficulty of sparse reward and inefficiency in the RL-Post training stage.

Architecture: Dense decoder-only Transformer
Base Model: Qwen2.5-1.5B
Parameters: 1.5 billion
Version: v1 (released Feb 12, 2026)

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(1.5B)	AIME24	AIME25
Mobile-ReasoningLLM-v0-1.5B	60.0	45.0
Mobile-Flash-ReasoningLLM-v0-1.5B	70.0	60.0
Viber-Thinker-1.5B	78.0	70.0
Mobile-Flash-v1-1.5B	90.0	76.7
Model(>235B)	AIME24	AIME25
GPT-5.2	97.0+	97.0+
Grok-4	97.0+	97.0+
Gemini-3-Pro	97.0+	97.0+
GPT-OSS-120B	96.6	97.9
GPT-OSS-20B	96.0	98.7
Grok 3 Mini	95.8	93.3
o4-mini	93.4	92.7
o3	91.6	86.5
DeepSeek-R1-0528(671B)	91.4	87.5
Qwen-3(235B)	85.7	81.5

How to Use

Requirements

Library: transformers, torch, vLLM or TensorRT-LLM
Hardware: Trained and Tested on NVIDIA 8xA100-80GB GPUs
Environment: Python 3.10+ (e.g., Conda hug environment)

Inference Example

import transformers
import torch
model_id = "deepgo/Mobile-Flash-v1-1.5B"
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{}."""
max-length=40,000 is recommend.(reduced from 48,000 to 40,000)