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wingit-extractor-v1 – AI Model by richierich007 | AlphaNeural AI

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richierich007/
wingit-extractor-v1

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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

python

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]))