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]))
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