TinyLLaMA is an open-source language model by jzhang38’s team, designed as a lightweight yet capable alternative to larger LLaMA models. Its 1.1 B base model is trained on an impressive corpus of 3 trillion tokens following the original LLaMA architecture and tokenizer . The project includes fully reproducible checkpoints, a chat-finetuned variant, and shared evaluation benchmarks.As a schlacker-optimized, lightweight model, TinyLLaMA serves as a practical alternative to larger models like LLaMA‑3.1 or GPT‑NeoX when computational resources are limited, without sacrificing strong performance
Key features include:
- 1‑2 B parameter model retrained with LLaMA‑architecture on 3 T tokens
- Fully open artifacts: code, training checkpoints, data, and evaluation logs
- Chat-finetuned version available for dialogue applications
- Apache 2.0 license, permitting commercial use
- Plug-and-play compatibility with LLaMA ecosystem tools and pipelines
Use cases include:
- Deploying efficient LLMs on edge or constrained hardware (e.g., ~637 MB 4‑bit quantized model)
- Research and benchmarking on compact LLaMA‑style models
- Integration into chatbots, assistant tools, or on-device NLP systems
