# Lightweight Llama

MicroLlama is a 300-million-parameter Llama model pretrained by individual developer keeeeenw within a $500 budget, focusing on English text generation tasks.

TinyLlama is a 1.1-billion-parameter small language model that adopts the same architecture and tokenizer as Llama 2, suitable for resource-constrained application scenarios.

TinyLlama is a compact language model with 1.1 billion parameters, adopting the same architecture and tokenizer as Llama 2, suitable for applications with limited computational and memory resources.

TinyLlama is a small language model with 1.1 billion parameters, adopting the same architecture and tokenizer as Llama 2, suitable for resource-constrained application scenarios.

LiteLlama-460M-1T is a lightweight mixture of experts model with 512 experts, suitable for efficient inference and text generation tasks.

A medical domain Q&A model fine-tuned based on TinyLlama-1.1B-Chat-v1.0, optimized for medical texts and Q&A scenarios

TinyLlama is a 1.1B parameter Llama model pretrained on 3 trillion tokens, designed to provide compact and efficient text generation capabilities.

TinyLlama is a compact 1.1B-parameter Llama model pretrained on 3 trillion tokens, designed for resource-constrained environments.

TinyLlama is a 1.1 billion parameter Llama model pre-trained on 3 trillion tokens, suitable for scenarios with limited computation and memory resources.

TinyLlama is a 1.1B parameter Llama model, planned to be pretrained on 3 trillion tokens, optimized to complete training in 90 days on 16 A100-40G GPUs.