Browse AI Models

Microsoft

> [!IMPORTANT] > To fully take advantage of the model's capabilities, inference must use `temperature=0.8`, `top_k=50`, `top_p=0.95`, and `do_sample=True`. For more complex queries, set `max_new_tokens=32768` to allow for longer chain-of-thought (CoT).

Alibaba

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

Cohere

Command R is Cohere's retrieval-augmented generation model optimized for enterprise use. Excels at long-context document processing, tool use, and grounded generation with citation support.

Jina AI

jina-embeddings-v3: Multilingual Embeddings With Task LoRA

Meta

Llama 3.2 11B Vision is Meta's multimodal model that processes both text and images. Supports visual question answering, image captioning, and document understanding alongside standard text generation.

Mistral

Building upon Mistral Small 3.1 (2503), with added reasoning capabilities, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters.

Mistral

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest

01.AI

- they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?" pipeline_tag: text-generation

BAAI

For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding