Update ReadMe.md

README.md

@@ -1,3 +1,98 @@
----
-license: apache-2.0
----
+---
+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-Flash-v1.5-1.5B
+    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: 80.0
+---
+# Mobile-Flash-v1.5-1.5B
+
+## 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-v1.5-1.5B which **starts to continue 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 with it's only **trajectory** 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**: v1.5 (released April 21, 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** | **80.0** |
+| 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-v1.5-1.5B"
+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)