Meta

Meta AI 650M-parameter protein language model trained on UniRef50 sequences. Feed it an amino-acid sequence and the per-residue hidden states act as learned protein embeddings, used for structure prediction, variant-effect and function tasks. This 33-layer checkpoint is the common balance of quality and cost in the ESM-2 family.

Meta Segment Anything 2. Promptable segmentation across images and video with temporal memory. Zero-shot, point/box/mask prompts, fast on a single H100.

Meta's 13B Code Llama tuned for instruction following. A faster mid-size option for code generation and completion, supporting infilling for inserting code at a cursor position. Served on Replicate per call.

Meta's 34B Code Llama tuned for instruction following. A balance of size and quality for code generation, completion, and explanation, with strong coverage of Python, JavaScript, and other common languages. Runs on Replicate per call.

Meta's largest Code Llama, a 70B Llama-2 derivative specialized for programming and tuned to follow instructions in chat form. Handles code generation, completion, and explanation across common languages. Served on Replicate as a per-call endpoint.

Meta's smallest Code Llama at 7B parameters, tuned for instruction following. The cheapest and fastest member of the family for quick code generation, completion, and infilling. Served on Replicate per call.

Meta Llama 3.2 11B Vision served via Ollama on Replicate. Open-weights multimodal model for image captioning, document and chart reading, and visual question answering.

Meta Llama 3.2 90B Vision. Largest open-weights Llama vision model. Strong visual reasoning, chart, OCR and document understanding.

MAGNeT is Meta's masked, non-autoregressive audio generator. Instead of predicting tokens left to right it fills masked audio tokens in parallel over a few decoding steps, so generation is faster than autoregressive MusicGen at similar quality. This Replicate packaging exposes the text-to-music and text-to-sound variants.

Meta MAGNeT non-autoregressive music generator. Up to 7x faster than MusicGen with comparable quality via masked generative transformers.

Meta Mask2Former universal image-segmentation transformer. Single architecture for panoptic, instance and semantic segmentation tasks.

Meta's 3.3B-parameter MusicGen Large. Text-conditioned music generation with single-stage autoregressive transformer, supports melody conditioning.

Meta's SeamlessM4T multimodal translation model. Takes speech or text input and produces transcription or translation across about 100 languages, including speech-to-text and speech-to-speech. One model covers ASR plus cross-lingual translation without chaining separate systems.

Meta SeamlessM4T v2 Large speech mode. Speech-to-speech, speech-to-text, and text-to-speech translation across 100+ languages in a single unified model.

Meta SeamlessM4T v2 Large. Universal multilingual translation across 100+ languages with text-to-text mode for documents and chat.