LLaMA-7B Model
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Auto-regressive Language Model |
| Architecture | Transformer-based |
| License | Non-commercial bespoke license |
| Training Data | CCNet (67%), C4 (15%), GitHub (4.5%), Wikipedia (4.5%), Books (4.5%), ArXiv (2.5%), Stack Exchange (2%) |
What is llama-7b-hf?
LLaMA-7B is a powerful foundation language model developed by Meta AI's FAIR team, designed specifically for research purposes. This particular version has been converted to work seamlessly with Hugging Face's Transformers library. The model represents a significant achievement in efficient language model design, trained on a diverse dataset of 1 trillion tokens.
Implementation Details
The model architecture features 4096 dimension size, 32 attention heads, and 32 layers. It was trained with a learning rate of 3.0E-04 and a batch size of 4M tokens. The model demonstrates impressive performance across various benchmarks, including common sense reasoning and natural language understanding tasks.
- Transformer-based architecture optimized for efficiency
- Supports 20 different languages, with primary focus on English
- Trained on a carefully curated mix of high-quality datasets
- Implements advanced attention mechanisms with 32 heads
Core Capabilities
- Strong performance in common sense reasoning (76.5% on BoolQ)
- Natural language understanding and reading comprehension
- Question answering capabilities (57.2% on OBQA)
- Multi-lingual text processing
- Research-focused applications
Frequently Asked Questions
Q: What makes this model unique?
LLaMA-7B stands out for its efficient architecture and strong performance despite its relatively smaller size compared to other modern language models. It achieves impressive results across multiple benchmarks while maintaining a manageable parameter count of 7 billion.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, including exploring language model capabilities, studying biases, and developing mitigation strategies for toxic content. It's important to note that it should not be used for direct downstream applications without proper evaluation and risk mitigation.
