Model Card for zeroproof-ecommerce-0.5b

E-commerce intent detection in a 0.5B model, the smallest member of the ZeroProof family. Given a live customer conversation, zeroproof-ecommerce-0.5b returns a single JSON object with the customer's payment intent and its structured details, at the lowest cost and latency in the family.

On a hard, held-out benchmark it reaches 60.4% exact-match core-intent accuracy, roughly 86% of our 1B, from a base model that scores 14.5%.

Model Details

Model Description

• Developed by: ZeroProof
• Model type: E-commerce payment-intent classifier; structured JSON output over seven intent types
• Language: English
• License: Apache-2.0 (inherited from the base model)
• Finetuned from: Qwen/Qwen2.5-0.5B-Instruct, via 4-bit QLoRA

Model Sources

• Repository: https://huggingface.co/zero-proof-ai/zeroproof-ecommerce-0.5b

Uses

Direct Use

Drop-in intent detection for e-commerce and payments support. Feed the conversation so far; the model returns one JSON object per message: whether an actionable payment intent is present, which of seven types it is, and the extracted details. The smallest, cheapest member of the family, built to run inline on every turn.

Out-of-Scope Use

English-language e-commerce and payments only. It is a narrow intent classifier, not a general assistant, and should not be the sole authority for executing a payment without a downstream verification step.

Bias, Risks, and Limitations

Trained and evaluated on synthetic role-play conversations distilled from frontier models under a locked labeling policy; live-traffic distribution may differ. Accuracy is strongest on refunds, bill-pay, and transfers; asset-exchange and subscriptions are the focus of the current data round.

Recommendations

Pair the model with a verification layer before any payment executes, and re-benchmark on your own traffic before relying on the reported numbers in production.

How to Get Started with the Model

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained("zero-proof-ai/zeroproof-ecommerce-0.5b")
tok   = AutoTokenizer.from_pretrained("zero-proof-ai/zeroproof-ecommerce-0.5b")

Use greedy decoding (temperature=0). The system prompt and per-intent detail schemas ship in the repo.

Training Details

Training Data

The differentiator is the data. ZeroProof builds e-commerce intent training data as randomized conversational simulations: role-played customers with independently sampled personas, situations, and behaviors including adversarial actors, distilled from frontier models under a locked labeling policy. Generation is label-blind, and every candidate passes a structural data gate with zero train/eval leakage. This 0.5B was trained on a class-balanced split so it routes across all intent types rather than defaulting to the majority.

Training Procedure

Training Hyperparameters

• Method: 4-bit QLoRA (via unsloth)
• Epochs: 1
• Base: Qwen2.5-0.5B-Instruct

Evaluation

Testing Data, Factors & Metrics

Held-out eval of 1,977 conversations, zero train/eval leakage, macro-averaged, scored by exact match against gold. The frontier panel is scored on a 412-row balanced subset of the same eval (reused, not re-run).

Results

• Fine-tuning takes core-intent accuracy from 14.5% to 60.4%, about 4x the base, at ~86% of our 1B and a fraction of frontier size.
• On structured detail extraction the 0.5B beats GPT-5 (54.3% vs 41.4%) and edges the 1B.
• The smallest member of the family, built to run inline on live traffic at the lowest cost and latency.

Technical Specifications

Model Architecture and Objective

A LoRA adapter over Qwen2.5-0.5B-Instruct, trained response-only to emit one structured intent object per turn.

Model Card Contact

ZeroProof, https://huggingface.co/zero-proof-ai