AraBART
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
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This is a large language model that was released by Meta on 2024-09-25. This edition of LLaMA packs a lot of quality in a size small enough to run on most computers with 4GB+ of RAM. See also its sister model Llama-3.2-1B-Instruct-llamafile. Mozilla packaged the LLaMA 3.2 models into executable weights that we call llamafiles. This gives you the easiest fastest way to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD and NetBSD systems you control on both AMD64 and ARM64. To get started, you need both the LLaMA 3.2 weights, and the llamafile software. Both of them are included in a single file, which can be downloaded and run as follows:
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These llamafiles are pre-packaged with support for CPU, Metal GPUs on Mac and CUDA+Vulkan on linux and Windows. If you have a different GPU or OS, you can download the corresponding library in the Files and versions section and save it in your home directory for llamafile to automatically find it. For more information about our project, check out our github repo. To learn how to use llamafiles, check our documentation! *NOTE*: While the llamafile project is Apache 2.0-licensed, the licenses of models we bundle with it might differ. Use the table above for reference.
**Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.