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Model: usersina/math-llm-sit-7b
Source: Original Platform

README.md

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+---
+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