---
language:
- en
license: mit
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- math
- reasoning
- fine-tuned
- specialized-intelligence
library_name: transformers
pipeline_tag: text-generation
---

# math-llm-sit-7b

Fine-tuned math reasoning model based on [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).

Trained using the **Specialized Intelligence Theory** 4-phase pipeline:
SFT -> Feedback -> Posterior -> Framework.

## Training Details

| Property | Value |
|----------|-------|
| Base model | `Qwen/Qwen2.5-7B-Instruct` |
| Training method | LoRA + 4-phase SIT pipeline |
| Upload date | 2026-04-02 |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("usersina/math-llm-sit-7b")
tokenizer = AutoTokenizer.from_pretrained("usersina/math-llm-sit-7b")

prompt = "Solve: What is the integral of x^2 from 0 to 1?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Pipeline

This model was trained using the 4-phase Specialized Intelligence Theory pipeline:

1. **SFT (Supervised Fine-Tuning)**: Foundation math reasoning
2. **Feedback**: Weighted feedback integration
3. **Posterior**: Internal posterior calibration
4. **Framework**: Full framework integration with resource allocation