Model Card for librarian-qwen-extractor

This is the designated "Extraction Specialist" for the Dual-Student Paradigm in our Master's Thesis on PDF Distillation and Document Intelligence. It acts as the secondary routing engine, transforming raw markdown text grids into strict Pydantic JSON schemas.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "RMunshi/librarian-qwen-extractor"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "Extract the entities from this text into JSON:\nThe ACME 5000 is an industrial router running firmware 2.1."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Procedure & Pipeline Context

This model is fine-tuned explicitly to generate Chain-of-Thought (CoT) reasoning logs (<thought>) followed by structured JSON (json) extraction graphs.

In our intelligent routing architecture:

1. Llama-Vision 11B (Student 1) analyzes document visuals and returns raw formatted grid markdown.
2. Qwen-2.5 7B (Student 2 - This Model) digests that markdown and outputs high-fidelity JSON mappings.

Training Data

Trained on 870 pristine synthetically generated industrial PDF layouts distilled directly using GPT-4o and verified via OpenCV masking.

Training Curves

• Final Loss: Convergence at ~0.08
• Base Engine: Qwen 2.5 7B Instruct
• Optimization Framework: Unsloth + Weights & Biases telemetry