---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- 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

```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]))