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formrecap-llama-3.2-3b-lora
jasonm4130/formrecap-llama-3.2-3b-lora
formrecap-llama-3.2-3b-lora is a machine learning model from jasonm4130. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
LoRA adapter fine-tuned on synthetic form abandonment event traces for 6-class classification.
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Apr 18, 2026
- Updated
- Apr 18, 2026
- SHA
- d711cbb7
Base models
Model card
From the Hugging Face model README
formrecap-llama-3.2-3b-lora
LoRA adapter fine-tuned on synthetic form abandonment event traces for 6-class classification.
Base Model
meta-llama/Llama-3.2-3B-Instruct
Task
Classifies form interaction event traces into one of six abandonment reasons:
| Code | Class | Description |
|---|---|---|
| 1 | validation_error | User hit a field error they couldn't resolve |
| 2 | distraction | User task-switched away |
| 3 | comparison_shopping | Browsing, not committing |
| 4 | accidental_exit | Closed tab / back button by mistake |
| 5 | bot | Automated non-human interaction |
| 6 | committed_leave | Intentionally chose not to complete |
Training
- Method: QLoRA (NF4 4-bit) + LoRA (r=16, alpha=32, DoRA=True)
- Data: 884 synthetic examples generated with Claude Sonnet, stratified 80/10/10 split
- Hardware: Modal L4 GPU, ~30 minutes, ~$0.40
- Framework: HuggingFace PEFT + TRL
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base, "jasonm4130/formrecap-llama-3.2-3b-lora")
tokenizer = AutoTokenizer.from_pretrained("jasonm4130/formrecap-llama-3.2-3b-lora")
Source
- Library
- peft
- License
- apache-2.0
- Created
- Apr 18, 2026
- Updated
- Apr 18, 2026