Llama 4 Scout 17B 16E Instruct is great at multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast codebases.
Llama 4 Maverick 17B 128E Instruct FP8 is great at precise image understanding and creative writing, offering high quality at a lower price compared to Llama 3.3 70B
Llama 4 Scout 17B 16E is great at multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast codebases.
Llama 3.3 70B Instruct offers enhanced reasoning, math, and instruction following with performance comparable to Llama 3.1 405B.
The Llama 3.1 instruction tuned text only models are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
Advanced image reasoning capabilities for visual understanding agentic apps.
Code Llama Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 34B version in the Hugging Face Transformers format. This model is designed for general code synthesis and und
Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our finetuned LLMs, called Llama2Chat, are optimized for dialogue use cases. Llam
Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our finetuned LLMs, called Llama2Chat, are optimized for dialogue use cases. Llam
Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our finetuned LLMs, called Llama2Chat, are optimized for dialogue use cases. Llam
The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billi
Model Details Note: Use of this model is governed by the Meta license. Click on View License above. Code Llama family of large language models (LLMs). Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This
Code Llama Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and unde
Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our finetuned LLMs, called Llama2Chat, are optimized for dialogue use cases. Llam
Model Information LLMpowered applications are susceptible to prompt attacks, which are prompts intentionally designed to subvert the developer’s intended behavior of the LLM. Categories of prompt attacks include prompt injection and jailbreaking: Prompt Injections are inputs that exploit t
The Llama 3.1 instruction tuned text only models are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
Excels in image reasoning capabilities on high-res images for visual understanding apps.
The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billi
DeiT (Dataefficient image Transformers) is an image transformer that do not require very large amounts of data for training. This is achieved through a novel distillation procedure using teacherstudent strategy, which results in high throughput and accuracy. DeiT is pretrained and finetuned on I
Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multil
Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instructiontuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instructiontuned text only models are optimized for multilingual dialogue use c
Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multil
Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the avai
A versatile 8-billion parameter model optimized for dialogue and text generation tasks.
Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instructiontuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instructiontuned text only models are optimized for multilingual dialogue use c
Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. CodeLlama70binstruct model is designed for general code synthesis and understanding. Limitations and Biases Code Llama and its variants are a new technology t
Llama Guard 31B Model Card Model Details Built with Llama Llama Guard 31B is a finetuned Llama3.21B pretrained model for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (r
Model Details Llama Guard 3 is a Llama3.18B pretrained model, finetuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generate
The Llama 3.1 instruction tuned text only models are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
Code Llama Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 13B version in the Hugging Face Transformers format. This model is designed for general code synthesis and un
Code Llama Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 34B version in the Hugging Face Transformers format. This model is designed for general code synthesis and und
Vision Transformer (basesized model) trained using DINOv2 Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper <a href="https://arxiv.org/abs/2304.07193"DINOv2: Learning Robust Visual Features without Supervision by Oquab et al.</a and first released in
Code Llama Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 34B version in the Hugging Face Transformers format. This model is designed for general code synthesis and und
A powerful 70-billion parameter model excelling in reasoning, coding, and broad language applications.
Llama Guard 311Bvision Model Card Model Details Built with Llama Llama Guard 3 Vision is a Llama3.211B pretrained model, finetuned for content safety classification. Similar to previous versions [13], it can be used to safeguard content for both LLM inputs (prompt classification) a
Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. CodeLlama70b model is designed for general code synthesis and understanding. Ethical Considerations and Limitations Code Llama and its variants are a new techn
Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. CodeLlama70bPython model is designed for general code synthesis and understanding. Limitations and Biases Code Llama and its variants are a new technology tha
Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instructiontuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instructiontuned text only models are optimized for multilingual dialogue use c
The Vision Transformer (ViT) is a transformer encoder model (BERTlike) pretrained on a large collection of images in a selfsupervised fashion with the DinoV2 method. Images are presented to the model as a sequence of fixedsize patches, which are linearly embedded. One also adds a [CLS] token to
The Vision Transformer (ViT) is a transformer encoder model (BERTlike) pretrained on a large collection of images in a selfsupervised fashion with the DinoV2 method. Images are presented to the model as a sequence of fixedsize patches, which are linearly embedded. One also adds a [CLS] token to
Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our finetuned LLMs, called Llama2Chat, are optimized for dialogue use cases. Llam
Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instructiontuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instructiontuned text only models are optimized for multilingual dialogue use c
Model Details Note: Use of this model is governed by the Meta license. Click on View License above. Code Llama family of large language models (LLMs). Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This
The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billi
Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our finetuned LLMs, called Llama2Chat, are optimized for dialogue use cases. Llam
Code Llama Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 34B version in the Hugging Face Transformers format. This model is designed for general code synthesis and und
Model Details Note: Use of this model is governed by the Meta license. Click on View License above. Code Llama family of large language models (LLMs). Code Llama is a collection of pretrained and finetuned generative text models ranging in scale from 7 billion to 34 billion parameters. This
Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the avai