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yunhe2019  published a dataset about 9 hours ago
meta-llama/AdvancedIF
yunhe2019  updated a dataset about 23 hours ago
meta-llama/AdvancedIF
lauragustafson  authored a paper 1 day ago
SAM 3: Segment Anything with Concepts
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The Llama Family

From Meta

Welcome to the official Hugging Face organization for Llama, Llama Guard, and Prompt Guard models from Meta!

In order to access models here, please visit a repo of one of the three families and accept the license terms and acceptable use policy. Requests are processed hourly.

In this organization, you can find models in both the original Meta format as well as the Hugging Face transformers format. You can find:

Current:

Llama 4: The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.

These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts.

History:

  • Llama 3.3: The Llama 3.3 is a text only instruct-tuned model in 70B size (text in/text out).
  • Llama 3.2: The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out).
  • Llama 3.2 Vision: The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text + images in / text out)
  • Llama 3.1: a collection of pretrained and fine-tuned text models with sizes ranging from 8 billion to 405 billion parameters pre-trained on ~15 trillion tokens.
  • Llama 3.1 Evals: a collection that provides detailed information on how we derived the reported benchmark metrics for the Llama 3.1 models, including the configurations, prompts and model responses used to generate evaluation results.
  • Llama Guard 3: a Llama-3.1-8B pretrained model, aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities.
  • Prompt Guard: a mDeBERTa-v3-base (86M backbone parameters and 192M word embedding parameters) fine-tuned multi-label model that categorizes input strings into 3 categories - benign, injection, and jailbreak. It is suitable to run as a filter prior to each call to an LLM in an application.
  • Llama 2: a collection of pretrained and fine-tuned text models ranging in scale from 7 billion to 70 billion parameters.
  • Code Llama: a collection of code-specialized versions of Llama 2 in three flavors (base model, Python specialist, and instruct tuned).
  • Llama Guard: a 8B Llama 3 safeguard model for classifying LLM inputs and responses.

Learn more about the models at https://ai.meta.com/llama/

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Llama 4
Llama 4 release
Llama 4
Llama 4 release
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