Llama
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About
Llama is a collection of large language models (LLMs) developed by Meta. The models are designed to be accessible and scalable, with a focus on multimodality, efficiency, and long context windows. Llama models are available for download and can be used for various applications, including text generation, image understanding, and multilingual writing. The models are licensed under a commercial license that balances open access with responsible use. Llama offers models like Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth, each with unique capabilities and performance characteristics. Llama also provides resources and documentation to help developers get started and fine-tune the models for their specific needs.
Platform
Task
Features
• cost efficiency
• multilingual writing
• image grounding
• top performance
• optimized for easy deployment
• super long context windows
• mixture-of-experts models
• native multimodality
FAQs
Can I use the output of the Llama models to improve other LLMs?
For Llama 2 and Llama 3, the license restricts using any part of the Llama models, including the response outputs, to train another AI model (LLM or otherwise). Starting with Llama 3.1 and later model versions, this is allowed provided you include the correct attribution to Llama.
Do Llama models support other languages outside of English?
Llama 2 and Llama 3 were primarily trained on English language data with some additional data from other languages. We do not expect the same level of performance in these other languages as in English. Llama 3.1, Llama 3.2, Llama 3.3, and Llama 4 support additional languages and are considered multilingual.
If I’m a developer/business, how can I access the Llama models?
Models are available through multiple sources but the place to start is at https://www.llama.com.
How do I download Llama 3.1 70B? It appears to no longer be available through the download flow.
Llama 3.3 70B is a high-performance replacement for Llama 3.1 70B.
Can anyone access Llama models? What are the terms?
Llama models are broadly available to developers and licensees through a variety of hosting providers and on the Meta website. The Llama models are licensed under the applicable Llama Community License Agreement and accompanying Acceptable Use Policy.
What are the hardware requirements for deploying these models?
Hardware requirements vary based on the specific Llama model being used, latency, throughput and cost constraints. For the larger Llama models to achieve low latency, one would split the model across multiple inference chips (typically a GPU) with tensor parallelism.
Do Llama models provide traditional autoregressive text completion?
Llama models are auto-regressive language models, built on the transformer architecture. The core language models function by taking a sequence of words as input and predicting the next word, recursively generating text.
Do Llama models support adjusting sampling temperature or top-p threshold via request parameters?
The model itself supports these parameters, but whether they are exposed or not depends on implementation.
What is the most effective RAG method paired with Llama models?
There are many ways to use RAG with Llama. See the developer documentation page for reference implementations.
Should we start training with the base or instruct/chat model?
This depends on your application. The Llama pre-trained models were trained for general large language applications, whereas the Llama instruct or chat models were fine tuned for dialogue specific uses like chat bots.
How can I fine tune the Llama models?
You can find examples on how to fine tune the Llama models in the Llama Cookbook repository. See also the developer documentation page for reference implementations.
Am I allowed to develop derivative models through fine-tuning based on Llama models for languages other than those officially supported? Is this a violation of the Acceptable Use Policy?
Developers may fine-tune Llama models for languages beyond English or other officially supported languages (e.g., Llama 3.1, Llama 3.2 (for text to text applications), and Llama 3.3 support 8 languages; Llama 4 (for text to text applications) supports 12 languages), provided that such use complies with the applicable Llama Community License Agreement and Acceptable Use Policy.
Is an employee that lives in the EU but is directly employed by a non-EU based company restricted from using the Llama multimodal models?
The employee may use the Llama multimodal models within the scope of the employee’s work for the non-EU based company, since the license extends to the non-EU based company. However, the employee may not use the Llama multimodal model for their own individual purposes if they are domiciled in the EU, although they may use a product or service that incorporates Llama multimodal models as an end user.
Can a non-EU based company develop a product or service using the Llama multimodal models and distribute such product or service within the EU?
Yes, if you are a company with a principal place of business outside of the EU, you may distribute products or services that contain the Llama multimodal models in accordance with your standard global distribution business practices
Is a non-EU based company that is affiliated with an EU based company permitted to use the Llama multimodal models?
Yes. The non-EU based company is permitted to use the Llama multimodal models, even if it is a subsidiary of, or affiliated with, an EU based company. However, it is only the non-EU based company that is permitted to use the Llama multimodal models for any development or distribution (but, see the FAQ above).
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