File size: 3,526 Bytes
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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: 90.0
- 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](https://huggingface.co/Qwen/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
```python
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) |