Audience
Developers interested in a small language model
About TinyLlama
The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs. We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
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Code Llama is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts.
Code Llama is free for research and commercial use.
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Code Llama, the foundational code model;
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Llama 2
The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.
Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations.
Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests.
Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations.
We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.
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Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field.
Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices.
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Pricing
Starting Price:
Free
Pricing Details:
Open source
Free Version:
Free Version available.
Integrations
Company Information
TinyLlama
github.com/jzhang38/TinyLlama
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Product Details
Platforms Supported
Windows
Mac
Linux
Training
Documentation
TinyLlama Frequently Asked Questions
Q: What kinds of users and organization types does TinyLlama work with?
TinyLlama works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does TinyLlama support in their product?
TinyLlama supports these languages: English.
Q: What other applications or services does TinyLlama integrate with?
TinyLlama integrates with: Runpod.
Q: What type of training does TinyLlama provide?
TinyLlama provides training in the form of documentation.
Q: How much does TinyLlama cost?
Pricing for TinyLlama starts at Free.
