wingit-extractor-v1/README.md

license, language, pipeline_tag, tags

license	language	pipeline_tag	tags
apache-2.0	en	text-generation	unsloth lora sft dpo extraction wingit

WingIt Extractor v1

Model Description

This is a specialized model for extracting high-value dialogue, concepts, and principles from dating/pickup transcripts.

It was trained using:

• SFT: 1,372 instruction examples (Prompt 3)
• DPO: Preference optimization for correct classification

Use Cases

• Extracting dialogues from raw transcripts
• Identifying behavioral concepts (frame control, abundance, push-pull, etc.)
• Classifying content as dialogue/concept/principle
• Generating structured JSONL training data

Training Data

• Prompt 3_cleaned.jsonl: 1,372 instruction examples
• Base model: unsloth/llama-3.1-8b-instruct-bnb-4bit

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("richierich007/wingit-extractor-v1")
tokenizer = AutoTokenizer.from_pretrained("richierich007/wingit-extractor-v1")

prompt = "Extract dialogue and concepts from this transcript..."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))