AI models directory for capability-aware discovery

sentence-transformers/all-mpnet-base-v2 is a high-performance, English-only sentence embedding model fine-tuned from Microsoft’s MPNet base architecture. It maps text into a 768-dimensional dense vector space, widely recognized for its state-of-the-art performance on semantic textual similarity (STS) and retrieval benchmarks. Licensed under Apache 2.0, it serves as a foundational component for semantic search, clustering, and information retrieval systems.

sentence-transformers/all-MiniLM-L6-v2 is a highly efficient, open-source sentence embedding model based on the MiniLM architecture. Fine-tuned on over 1 billion sentence pairs, it maps text to a 384-dimensional vector space, making it ideal for semantic search, clustering, and similarity tasks. Licensed under Apache 2.0, it supports multiple deployment frameworks including Hugging Face Transformers, ONNX, and OpenVINO.

nomic-ai/nomic-embed-text-v1.5 is a high-performance, open-source text embedding model developed by Nomic AI. Built on the NomicBERT architecture, it is optimized for semantic search, clustering, and feature extraction tasks. The model stands out for its support of Matryoshka Representation Learning (MRL), allowing users to adjust embedding dimensions dynamically without retraining. It is released under the permissive Apache 2.0 license and is widely deployed via Hugging Face Transformers, sentence-transformers, ONNX Runtime, and Transformers.js.

Phi-4 is a compact open-weight language model from Microsoft, optimized for structured technical tasks like coding, analysis, and research. It balances efficiency and quality, making it suitable for self-hosted or resource-constrained deployments.

DeepSeek R1 is an open-weight reasoning model developed by DeepSeek, emphasizing chain-of-thought (CoT) style reasoning for complex problem-solving. It is designed for research and deployment, with weights released under a permissive license.

Mixtral 8x22B Instruct is a sparse mixture-of-experts (MoE) large language model developed by Mistral AI, designed for self-hosted deployment and fine-tuning. It balances high performance with open availability, making it a strong candidate for teams seeking control over their AI stack without relying on closed APIs.

Code Llama is a family of large language models for code, released by Meta AI, based on Llama 2 and fine-tuned on code-specific datasets. It comes in 7B, 13B, 34B, and 70B parameter sizes with base, Python-specialized, and instruction-following variants. The model supports code generation, completion, infilling, explanation, debugging, and documentation across multiple programming languages. It is available under a custom commercial license and can be deployed locally via Ollama, Hugging Face, or other frameworks.

Qwen2.5 72B Instruct is an open-weight large language model developed by Alibaba Cloud, optimized for instruction-following tasks with strong bilingual support in Chinese and English. It is designed for self-hosted or customizable AI stacks, general assistant and reasoning workloads, and coding assistants. The model offers practical deployment flexibility through open-weight or partner stacks, balancing control, cost, and compliance.

Qwen is a family of large language models developed by Alibaba Cloud, covering chat, code, and multimodal variants. Designed for enterprise deployment, Qwen models are available in various sizes (1.8B to 72B parameters) and support both open-source and commercial use under the Qwen License. They excel in natural language understanding, code generation, mathematical reasoning, and multimodal tasks, with options for local deployment, cloud API, and fine-tuning.

GLM-4 is the latest generation of the General Language Model (GLM) series developed by Zhipu AI. It is a bilingual (Chinese and English) large language model with 130 billion parameters, supporting both API access and open-source releases. The model excels in long-context understanding, multimodal tasks, and agent-based applications, with a context window of up to 128K tokens. It is designed for a wide range of NLP tasks including text generation, reasoning, and code synthesis.

The cross-encoder/ms-marco-MiniLM-L6-v2 is a cross-encoder model for text ranking, fine-tuned on the MS MARCO passage ranking dataset. Based on MiniLM with 6 layers, it balances efficiency and accuracy, with over 79.9 million downloads on Hugging Face. It outputs relevance scores for query-passage pairs and supports multiple frameworks including PyTorch, JAX, ONNX, SafeTensors, and OpenVINO. Licensed under Apache-2.0, it is widely used for search re-ranking, question answering, and information retrieval.