TinyLlama-1.1B-intermediate-step-715k-1.5T
| Property | Value |
|---|---|
| Parameter Count | 1.1B |
| License | Apache 2.0 |
| Training Progress | 1.5T tokens (intermediate checkpoint) |
| Architecture | LLaMA-compatible |
What is TinyLlama-1.1B-intermediate-step-715k-1.5T?
TinyLlama-1.1B is an ambitious project aiming to create a compact yet powerful language model by pretraining a 1.1B parameter model on 3 trillion tokens. This particular checkpoint represents progress at 715,000 training steps, having processed 1.49T tokens. The model adopts the same architecture and tokenizer as Llama 2, ensuring compatibility with existing Llama-based projects while maintaining a smaller computational footprint.
Implementation Details
The model is being trained using 16 A100-40G GPUs over a planned 90-day period. It leverages the SlimPajama-627B and starcoderdata datasets, implementing the proven Llama 2 architecture at a smaller scale.
- Compatible with transformers>=4.31
- Supports both CPU and GPU inference
- Implements float16 precision for optimal performance
- Includes comprehensive tokenizer integration
Core Capabilities
- Strong performance on benchmark tasks (51.29% average across multiple evaluations)
- Efficient text generation with customizable parameters
- Balanced performance across various NLP tasks including HellaSwag (53.68%), WinoGrande (58.33%), and PIQA (71.65%)
- Compatibility with existing Llama-based workflows
Frequently Asked Questions
Q: What makes this model unique?
TinyLlama stands out for its efficient architecture that maintains Llama 2 compatibility while requiring significantly fewer computational resources. The 1.1B parameter size makes it accessible for applications with limited resources while still delivering competitive performance.
Q: What are the recommended use cases?
As an intermediate checkpoint, this model is primarily recommended for research and development purposes rather than production deployment. It's particularly suitable for testing and experimenting with Llama-compatible applications where computational efficiency is a priority.
