Text Classification
PEFT
Safetensors
English
intent-classification
ecommerce
lora
qlora
function-calling
Instructions to use while-ai/ecommerce-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use while-ai/ecommerce-1b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-1b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "while-ai/ecommerce-1b") - Notebooks
- Google Colab
- Kaggle
Zero Proof AI commited on
Mark verified model card as v1.2
Browse files
README.md
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# Model Card for zeroproof-ecommerce-1b
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**Frontier-approaching e-commerce intent detection in a 1B model.** Given a live customer conversation, `zeroproof-ecommerce-1b` returns a single JSON object with the customer's payment intent and its structured details. It runs inline on live traffic, at a cost and latency where calling a frontier model is not an option.
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On a hard, held-out benchmark it reaches **75.3% macro-averaged intent-type accuracy**. On the 412-row frontier comparison subset it reaches **71.4%, 89% of GPT-5's 80.2%**, at **$0.18 per 1M output tokens** and about a second per response.
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# Model Card for zeroproof-ecommerce-1b
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**Release:** v1.2
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**Frontier-approaching e-commerce intent detection in a 1B model.** Given a live customer conversation, `zeroproof-ecommerce-1b` returns a single JSON object with the customer's payment intent and its structured details. It runs inline on live traffic, at a cost and latency where calling a frontier model is not an option.
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On a hard, held-out benchmark it reaches **75.3% macro-averaged intent-type accuracy**. On the 412-row frontier comparison subset it reaches **71.4%, 89% of GPT-5's 80.2%**, at **$0.18 per 1M output tokens** and about a second per response.
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