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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: 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. |