DeepCoder-14B-Preview is a code reasoning LLM fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning (RL) to scale up to long context lengths.

microsoft/Phi-3-mini-4k-instruct

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

OpenCoder is an open and reproducible code LLM family which includes 1.5B and 8B base and chat models, supporting both English and Chinese languages.

Given a prompt and/or an input image, the model will generate a new image.

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

The 72B variant of the latest iteration of Qwen-VL model from Alibaba, representing nearly a year of innovation.

Text embeddings

test

A powerful multimodal model capable of processing both text and image inputs that supports multilingual, multi-turn conversations, tool use, and JSON mode.

A powerful multimodal model capable of processing both text and image inputs that supports multilingual, multi-turn conversations, tool use, and JSON mode.

OpenAI’s text embeddings measure the relatedness of text strings.

The BAAI/bge-m3 is an advanced multilingual embedding model supporting over 100 languages, designed for high-performance semantic similarity and retrieval tasks. By generating dense vector representations that capture semantic meaning across diverse linguistic contexts, the model enables sophisticated cross-lingual text understanding and comparison.

Qwen2-0.5B-Instruct is a compact 500 million parameter language model from Alibaba's Qwen series, designed for efficient instruction-following and conversational tasks with a lightweight architecture optimized for resource-constrained environments.

Qwen2-7B-Instruct is an advanced 7 billion parameter large language model from Alibaba's Qwen series, optimized for instructional and conversational tasks with strong multilingual capabilities and improved performance across reasoning, mathematics, and coding benchmarks. The model builds on the Qwen series' reputation for high-quality open-source AI models, offering enhanced instruction-following abilities and efficient text generation across multiple domains.

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning. The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework