---
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](https://huggingface.co/Qwen/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
```python
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.