GeneralPurposeJerry/README.md

metadata

language:
  - en
license: mit
tags:
  - modernbert
  - int4
  - qat
  - nli
  - zero-shot-classification
  - sentiment-analysis
  - jerry
pipeline_tag: zero-shot-classification

General Purpose Jerry (Universal NLU & Zero-Shot Engine — INT4 W4A16)

General Purpose Jerry is the fine-tuned 4-bit NLU & Zero-Shot foundation model in the Jerry Model Family hosted at Amogh1221/Jerry.

• Model Size: 91.1 MB (vs 220 MB FP16 base model — 75% reduction).
• Quantization: W4A16 (Weight-Only INT4 with Group-Wise QAT, group_size=64).
• Pre-Trained Base: 150k steps on ~9.8B tokens.
• Fine-Tuning: Multi-Genre Natural Language Inference (MNLI).
• Validation Accuracy: 80.77%.

Built-In Capabilities

1. Zero-Shot Sentiment Analysis:
   • Classifies positive, negative, and neutral sentiments with 0 training samples.
2. Zero-Shot Intent & Topic Classification:
   • Compares user prompts against custom candidate labels.
3. General NLU & Transfer Learning:
   • Plug-and-play feature extractor for high-speed downstream NLP pipelines.

Inference Example (Zero-Shot Sentiment)

import torch
from jerry_infer import load_general_jerry, predict_zero_shot_sentiment

classifier = load_general_jerry("general_jerry_int4.pt")
review = "The battery life on this laptop easily lasts 14 hours and the screen is gorgeous."
result = predict_zero_shot_sentiment(classifier, review)
print(result) # {'label': 'Positive', 'confidence': 0.94}