AI models directory for capability-aware discovery

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.

Llama 3.3 is a 70B parameter multilingual large language model from Meta, optimized for instruction-tuned dialogue. It offers performance comparable to the Llama 3.1 405B model while being significantly smaller, making it suitable for local deployment via Ollama with a 128K context window.

Qwen2.5 is a family of large language models developed by Alibaba, pretrained on up to 18 trillion tokens with multilingual support and a context window of up to 128K tokens. Available in sizes from 0.5B to 72B parameters, it is optimized for local deployment via Ollama.

SDXL 1.0 is a state-of-the-art open-source text-to-image model developed by Stability AI, offering high-resolution image generation with improved composition, detail, and prompt adherence compared to its predecessor. It is designed for self-hosted deployment and integration into creative pipelines, making it a popular choice for teams seeking control over their image generation workflow.

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.

Qwen2.5-VL 72B is a large multimodal AI model from Alibaba Cloud's Qwen team, designed for vision-language tasks such as image understanding, document analysis, and visual reasoning. It is an open-weight model that can be self-hosted, making it suitable for teams seeking alternatives to hosted multimodal APIs. The model excels in processing screenshots, documents, and visual data, but requires significant GPU resources for deployment.

FLUX.1 Dev is an open, text-to-image generation model developed by Black Forest Labs. It is designed for high-quality, creative image synthesis and is particularly suited for developers and teams who want to self-host or customize their image generation pipeline. The model offers a balance of performance and accessibility, making it a strong candidate for open experimentation in visual AI workflows.

Llama 3.3 70B is a large language model from Meta, designed for general assistant and reasoning tasks. It is part of the Llama family and is known for its strong performance, open weights, and extensive ecosystem support. The model is suitable for self-hosted or customizable AI stacks, making it a popular choice for teams that need a capable open model with fine-tuning and inference flexibility.

XLM-RoBERTa (XLM-R) is a multilingual transformer model introduced by Facebook AI in 2020, pre-trained on 2.5TB of CommonCrawl data covering 100 languages. It uses a masked language modeling objective and achieves state-of-the-art results on cross-lingual benchmarks like XNLI and MLQA. The base version has 278M parameters and is available in PyTorch, TensorFlow, JAX, and ONNX formats via Hugging Face.

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.

Mixtral is a family of sparse Mixture-of-Experts (SMoE) large language models developed by Mistral AI, available in 8x7B (46.7B total, 12.9B active) and 8x22B (141B total, 39B active) sizes. It supports multilingual text (English, French, Italian, German, Spanish), a 64K token context window (8x22B), and is released under Apache 2.0. The model excels at reasoning, code generation, and instruction following, outperforming Llama 2 70B and GPT-3.5 on benchmarks. It can be run locally via Ollama or accessed through Mistral AI's API.

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.

ELECTRA-base-discriminator is a Transformer encoder pretrained with replaced token detection (RTD), achieving strong NLP performance efficiently. It is available under Apache 2.0 license.

Whisper Large v3 is a state-of-the-art open-source speech-to-text model developed by OpenAI, designed for robust multilingual transcription and translation. It excels in production audio workflows, offering high accuracy across diverse languages and acoustic conditions. The model is self-hostable, customizable, and widely used in voice applications, meeting accessibility, and batch processing pipelines.

Gemma 2 27B is an open-weight language model from Google, designed for self-hosted and customizable AI stacks. It offers strong performance for general assistant and reasoning tasks, backed by Google's ecosystem and available under a permissive license.

The `paraphrase-multilingual-MiniLM-L12-v2` model is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space for tasks like clustering, semantic search, and paraphrase identification. It is based on Microsoft's MiniLM-L12-H384-uncased architecture and fine-tuned on a large multilingual paraphrase corpus, supporting over 50 languages. The model is released under Apache 2.0 and is available in PyTorch, TensorFlow, ONNX, OpenVINO, and SafeTensors formats.

CLAP HTSAT-Fused is a contrastive language-audio pretraining model that learns a shared embedding space between audio and natural language descriptions. It uses HTSAT as the audio encoder and RoBERTa as the text encoder, with a feature fusion mechanism, trained on LAION-Audio-630K. It supports zero-shot audio classification, retrieval, and feature extraction.