Explorer

22 technologies shown

• GPT model Family of decoder-only Transformer language models trained with a generative objective.
• Llama model Open-weight language model family from Meta, released for local and self-hosted use.
• BERT model Encoder-only Transformer pre-trained with masked language modelling.
• Qwen model Alibaba's open-weight model family, strong on multilingual and coding tasks.
• Mistral model European open-weight model family known for efficiency at small parameter counts.
• Claude model Anthropic's hosted model family, positioned around long-context work and safety.
• OpenAI Text Embedding 3 model Hosted embedding models with configurable output dimensions.
• Whisper model Speech recognition model trained on large-scale weakly supervised audio.
• CLIP model Model that places images and text in one embedding space.
• DeepSeek model Open-weight model family notable for efficient training at frontier scale.
• Gemma model Google's open-weight model family, released in small sizes.
• LLaVA model Vision-language model built by connecting a vision encoder to a language model.
• Cohere Embed model Hosted multilingual embedding model with separate query and document modes.
• Gemini model Google's hosted multimodal model family, integrated with its cloud platform.
• BGE model Embedding model family that ranks well on retrieval benchmarks.
• Phi model Microsoft's small models trained mainly on curated textbook-quality data.
• GTE model Embedding model family from Alibaba, trained for retrieval and ranking.
• E5 model Text embedding model family trained on weakly supervised pairs.
• Jina Embeddings model Embedding models designed around long inputs and multilingual coverage.
• Command R model Model family oriented toward retrieval and tool use.
• Nomic Embed model Open embedding model with an auditable training set and long context.
• Instructor model Embedding model that takes a task instruction alongside the text.