language, license, base_model, tags, library_name, pipeline_tag

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

math-llm-sit-7b

Fine-tuned math reasoning model based on 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

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