---
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`](https://huggingface.co/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)
```python
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}
```