feat: enhance remote model search to use dynamic catalog
Replace hardcoded list of popular models with dynamic catalog from getCatalogModels(). Enable searching by model name or description and include model descriptions in results. Improves discoverability and maintains up-to-date model list without manual updates. Database files updated accordingly.
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Evandro Fonseca Junior committed
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{
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"metadata": {
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"source": "ollama.com/library + buscas: uncensored, abliterated, code, reasoning",
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"collection_date": "2025-10-29",
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"total_models": 270,
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"note": "Integração de modelos oficiais + comunitários (uncensored/abliterated variants). Modelos de reasoning incluem variantes com foco em raciocínio (ex: CoT, logical deduction). Duplicatas removidas; métricas atualizadas onde possível. API para lista remota ainda limitada (ver GitHub #8241)."
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},
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"models": [
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{
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"name": "gpt-oss",
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"description": "OpenAI's open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases",
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"capabilities": ["tools", "thinking", "cloud"],
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"parameter_sizes": ["20b", "120b"],
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"pulls": "3.8M",
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"tags_count": 5,
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"last_updated": "2 weeks ago",
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"url": "https://ollama.com/library/gpt-oss"
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},
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{
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"name": "qwen3-vl",
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"description": "The most powerful vision-language model in the Qwen model family to date",
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"capabilities": ["vision", "cloud"],
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"parameter_sizes": ["2b", "4b", "8b", "30b", "32b"],
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"pulls": "22.8K",
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"tags_count": 49,
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"last_updated": "13 minutes ago",
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"url": "https://ollama.com/library/qwen3-vl"
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},
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{
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"name": "deepseek-r1",
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"description": "DeepSeek-R1 is a family of open reasoning models with performance approaching that of leading models, such as O3 and Gemini 2.5 Pro",
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"capabilities": ["tools", "thinking"],
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"parameter_sizes": ["1.5b", "7b", "8b", "14b", "32b", "70b", "671b"],
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"pulls": "68.4M",
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"tags_count": 35,
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"last_updated": "3 months ago",
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"url": "https://ollama.com/library/deepseek-r1"
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},
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{
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"name": "qwen3-coder",
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"description": "Alibaba's performant long context models for agentic and coding tasks",
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"capabilities": ["tools", "cloud"],
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"parameter_sizes": ["30b", "480b"],
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"pulls": "575.7K",
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"tags_count": 10,
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"last_updated": "1 month ago",
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"url": "https://ollama.com/library/qwen3-coder"
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},
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{
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"name": "gemma3",
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"description": "The current, most capable model that runs on a single GPU",
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"capabilities": ["vision"],
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"parameter_sizes": ["270m", "1b", "4b", "12b", "27b"],
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"pulls": "23.1M",
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"tags_count": 26,
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"last_updated": "2 months ago",
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"url": "https://ollama.com/library/gemma3"
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},
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{
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"name": "glm-4.6",
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"description": "Advanced agentic, reasoning and coding capabilities",
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"capabilities": ["cloud"],
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"parameter_sizes": [],
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"pulls": "11.8K",
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"tags_count": 1,
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"last_updated": "2 weeks ago",
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"url": "https://ollama.com/library/glm-4.6"
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},
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{
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"name": "embeddinggemma",
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"description": "EmbeddingGemma is a 300M parameter embedding model from Google",
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"capabilities": ["embedding"],
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"parameter_sizes": ["300m"],
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"pulls": "111.2K",
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"tags_count": 5,
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"last_updated": "1 month ago",
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"url": "https://ollama.com/library/embeddinggemma"
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},
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{
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"name": "qwen3",
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"description": "Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models",
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"capabilities": ["tools", "thinking"],
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"parameter_sizes": ["0.6b", "1.7b", "4b", "8b", "14b", "30b", "32b", "235b"],
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"pulls": "11.7M",
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"tags_count": 58,
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"last_updated": "2 weeks ago",
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"url": "https://ollama.com/library/qwen3"
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},
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{
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"name": "deepseek-v3.1",
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"description": "DeepSeek-V3.1-Terminus is a hybrid model that supports both thinking mode and non-thinking mode",
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"capabilities": ["tools", "thinking", "cloud"],
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"parameter_sizes": ["671b"],
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"pulls": "120.6K",
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"tags_count": 8,
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"last_updated": "1 month ago",
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"url": "https://ollama.com/library/deepseek-v3.1"
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},
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{
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"name": "llama3.1",
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"description": "Llama 3.1 is a new state-of-the-art model from Meta available in 8B, 70B and 405B parameter sizes",
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"capabilities": ["tools"],
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"parameter_sizes": ["8b", "70b", "405b"],
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"pulls": "105.1M",
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"tags_count": 93,
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"last_updated": "11 months ago",
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"url": "https://ollama.com/library/llama3.1"
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},
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{
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"name": "nomic-embed-text",
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"description": "A high-performing open embedding model with a large token context window",
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"capabilities": ["embedding"],
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"parameter_sizes": [],
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"pulls": "44.3M",
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"tags_count": 3,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/nomic-embed-text"
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},
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{
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"name": "llama3.2",
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"description": "Meta's Llama 3.2 goes small with 1B and 3B models",
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"capabilities": ["tools"],
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"parameter_sizes": ["1b", "3b"],
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"pulls": "42.6M",
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"tags_count": 63,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/llama3.2"
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},
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{
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"name": "mistral",
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"description": "The 7B model released by Mistral AI, updated to version 0.3",
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"capabilities": ["tools"],
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"parameter_sizes": ["7b"],
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"pulls": "21.3M",
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"tags_count": 84,
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"last_updated": "3 months ago",
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"url": "https://ollama.com/library/mistral"
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},
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{
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"name": "qwen2.5",
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"description": "Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support",
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"capabilities": ["tools"],
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"parameter_sizes": ["0.5b", "1.5b", "3b", "7b", "14b", "32b", "72b"],
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"pulls": "15.8M",
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"tags_count": 133,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/qwen2.5"
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},
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{
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"name": "phi3",
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"description": "Phi-3 is a family of lightweight 3B (Mini) and 14B (Medium) state-of-the-art open models by Microsoft",
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"capabilities": [],
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"parameter_sizes": ["3.8b", "14b"],
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"pulls": "12.5M",
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"tags_count": 72,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/phi3"
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},
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{
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"name": "llama3",
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"description": "Meta Llama 3: The most capable openly available LLM to date",
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"capabilities": [],
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"parameter_sizes": ["8b", "70b"],
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"pulls": "11.5M",
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"tags_count": 68,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/llama3"
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},
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{
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"name": "llava",
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"description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6",
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"capabilities": ["vision"],
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"parameter_sizes": ["7b", "13b", "34b"],
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"pulls": "11.1M",
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"tags_count": 98,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/llava"
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},
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{
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"name": "gemma2",
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"description": "Google Gemma 2 is a high-performing and efficient model available in three sizes: 2B, 9B, and 27B",
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"capabilities": [],
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"parameter_sizes": ["2b", "9b", "27b"],
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"pulls": "8.6M",
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"tags_count": 94,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/gemma2"
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},
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{
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"name": "qwen2.5-coder",
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"description": "The latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, and code fixing",
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"capabilities": ["tools"],
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"parameter_sizes": ["0.5b", "1.5b", "3b", "7b", "14b", "32b"],
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"pulls": "7.9M",
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"tags_count": 199,
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"last_updated": "5 months ago",
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"url": "https://ollama.com/library/qwen2.5-coder"
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},
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{
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"name": "phi4",
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"description": "Phi-4 is a 14B parameter, state-of-the-art open model from Microsoft",
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"capabilities": [],
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"parameter_sizes": ["14b"],
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"pulls": "5.8M",
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"tags_count": 5,
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"last_updated": "9 months ago",
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"url": "https://ollama.com/library/phi4"
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},
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{
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"name": "gemma",
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"description": "Gemma is a family of lightweight, state-of-the-art open models built by Google DeepMind. Updated to version 1.1",
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"capabilities": [],
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"parameter_sizes": ["2b", "7b"],
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"pulls": "5.4M",
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"tags_count": 102,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/gemma"
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},
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{
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"name": "mxbai-embed-large",
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"description": "State-of-the-art large embedding model from mixedbread.ai",
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"capabilities": ["embedding"],
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"parameter_sizes": ["335m"],
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"pulls": "5.2M",
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"tags_count": 4,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/mxbai-embed-large"
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},
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{
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"name": "qwen",
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"description": "Qwen 1.5 is a series of large language models by Alibaba Cloud spanning from 0.5B to 110B parameters",
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"capabilities": [],
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"parameter_sizes": ["0.5b", "1.8b", "4b", "7b", "14b", "32b", "72b", "110b"],
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"pulls": "5M",
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"tags_count": 379,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/qwen"
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},
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{
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"name": "qwen2",
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"description": "Qwen2 is a new series of large language models from Alibaba group",
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"capabilities": ["tools"],
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"parameter_sizes": ["0.5b", "1.5b", "7b", "72b"],
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"pulls": "4.4M",
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"tags_count": 97,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/qwen2"
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},
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{
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"name": "llama2",
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"description": "Llama 2 is a collection of foundation language models ranging from 7B to 70B parameters",
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"capabilities": [],
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"parameter_sizes": ["7b", "13b", "70b"],
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"pulls": "4.4M",
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"tags_count": 102,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/llama2"
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},
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{
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"name": "minicpm-v",
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"description": "A series of multimodal LLMs (MLLMs) designed for vision-language understanding",
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"capabilities": ["vision"],
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"parameter_sizes": ["8b"],
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"pulls": "3.9M",
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"tags_count": 17,
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"last_updated": "11 months ago",
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"url": "https://ollama.com/library/minicpm-v"
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},
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{
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"name": "dolphin3",
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"description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model",
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"capabilities": [],
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"parameter_sizes": ["8b"],
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"pulls": "3.4M",
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"tags_count": 5,
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"last_updated": "9 months ago",
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"url": "https://ollama.com/library/dolphin3"
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},
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{
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"name": "codellama",
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"description": "A large language model that can use text prompts to generate and discuss code",
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"capabilities": [],
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"parameter_sizes": ["7b", "13b", "34b", "70b"],
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"pulls": "3.3M",
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"tags_count": 199,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/codellama"
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},
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{
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"name": "olmo2",
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"description": "OLMo 2 is a new family of 7B and 13B models trained on up to 5T tokens",
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"capabilities": [],
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"parameter_sizes": ["7b", "13b"],
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"pulls": "3.3M",
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"tags_count": 9,
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"last_updated": "9 months ago",
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"url": "https://ollama.com/library/olmo2"
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},
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{
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"name": "tinyllama",
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"description": "The TinyLlama project is an open endeavor to train a compact 1.1B Llama model on 3 trillion tokens",
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"capabilities": [],
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"parameter_sizes": ["1.1b"],
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"pulls": "3M",
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"tags_count": 36,
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"last_updated": "1 year ago",
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"url": "https://ollama.com/library/tinyllama"
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},
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{
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"name": "llama3.2-vision",
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"description": "Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes",
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"capabilities": ["vision"],
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"parameter_sizes": ["11b", "90b"],
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"pulls": "2.8M",
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"tags_count": 9,
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"last_updated": "5 months ago",
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"url": "https://ollama.com/library/llama3.2-vision"
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},
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{
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"name": "mistral-nemo",
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"description": "A state-of-the-art 12B model with 128k context length, built by Mistral AI in collaboration with NVIDIA",
|
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"capabilities": ["tools"],
|
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"parameter_sizes": ["12b"],
|
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"pulls": "2.8M",
|
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"tags_count": 17,
|
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"last_updated": "3 months ago",
|
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"url": "https://ollama.com/library/mistral-nemo"
|
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},
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{
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"name": "llama3.3",
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"description": "New state of the art 70B model. Llama 3.3 70B offers similar performance compared to the Llama 3.1 405B model",
|
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"capabilities": ["tools"],
|
||||
"parameter_sizes": ["70b"],
|
||||
"pulls": "2.6M",
|
||||
"tags_count": 14,
|
||||
"last_updated": "10 months ago",
|
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"url": "https://ollama.com/library/llama3.3"
|
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},
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{
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"name": "deepseek-v3",
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"description": "A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token",
|
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"capabilities": [],
|
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"parameter_sizes": ["671b"],
|
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"pulls": "2.6M",
|
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"tags_count": 5,
|
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"last_updated": "9 months ago",
|
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"url": "https://ollama.com/library/deepseek-v3"
|
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},
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{
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"name": "bge-m3",
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"description": "BGE-M3 is a new model from BAAI distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity",
|
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"capabilities": ["embedding"],
|
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"parameter_sizes": ["567m"],
|
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"pulls": "2.6M",
|
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"tags_count": 3,
|
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"last_updated": "1 year ago",
|
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"url": "https://ollama.com/library/bge-m3"
|
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},
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{
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"name": "mistral-small",
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"description": "Mistral Small 3 sets a new benchmark in the small Large Language Models category below 70B",
|
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"capabilities": ["tools"],
|
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"parameter_sizes": ["22b", "24b"],
|
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"pulls": "2.1M",
|
||||
"tags_count": 21,
|
||||
"last_updated": "9 months ago",
|
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"url": "https://ollama.com/library/mistral-small"
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},
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{
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"name": "smollm2",
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"description": "SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters",
|
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"capabilities": ["tools"],
|
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"parameter_sizes": ["135m", "360m", "1.7b"],
|
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"pulls": "2M",
|
||||
"tags_count": 49,
|
||||
"last_updated": "12 months ago",
|
||||
"url": "https://ollama.com/library/smollm2"
|
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},
|
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{
|
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"name": "llava-llama3",
|
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"description": "A LLaVA model fine-tuned from Llama 3 Instruct with better scores in several benchmarks",
|
||||
"capabilities": ["vision"],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "2M",
|
||||
"tags_count": 4,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llava-llama3"
|
||||
},
|
||||
{
|
||||
"name": "all-minilm",
|
||||
"description": "Embedding models on very large sentence level datasets",
|
||||
"capabilities": ["embedding"],
|
||||
"parameter_sizes": ["22m", "33m"],
|
||||
"pulls": "1.9M",
|
||||
"tags_count": 10,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/all-minilm"
|
||||
},
|
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{
|
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"name": "qwq",
|
||||
"description": "QwQ is the reasoning model of the Qwen series, focused on advancing AI reasoning capabilities",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"name": "deepseek-coder",
|
||||
"description": "DeepSeek Coder is a capable coding model trained on two trillion code and natural language tokens",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/deepseek-coder"
|
||||
},
|
||||
{
|
||||
"name": "starcoder2",
|
||||
"description": "StarCoder2 is the next generation of transparently trained open code LLMs that comes in three sizes: 3B, 7B and 15B parameters",
|
||||
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|
||||
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|
||||
"pulls": "1.4M",
|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/starcoder2"
|
||||
},
|
||||
{
|
||||
"name": "mixtral",
|
||||
"description": "A set of Mixture of Experts (MoE) model with open weights by Mistral AI in 8x7b and 8x22b parameter sizes",
|
||||
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|
||||
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|
||||
"pulls": "1.4M",
|
||||
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|
||||
"last_updated": "10 months ago",
|
||||
"url": "https://ollama.com/library/mixtral"
|
||||
},
|
||||
{
|
||||
"name": "llama2-uncensored",
|
||||
"description": "Uncensored Llama 2 model by George Sung and Jarrad Hope, with support for a 16K context window",
|
||||
"capabilities": [],
|
||||
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|
||||
"pulls": "1.3M",
|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/llama2-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "codegemma",
|
||||
"description": "CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks",
|
||||
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|
||||
"parameter_sizes": ["2b", "7b"],
|
||||
"pulls": "1.3M",
|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/codegemma"
|
||||
},
|
||||
{
|
||||
"name": "deepseek-coder-v2",
|
||||
"description": "An open-source Mixture-of-Experts code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["16b", "236b"],
|
||||
"pulls": "1.2M",
|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/deepseek-coder-v2"
|
||||
},
|
||||
{
|
||||
"name": "falcon3",
|
||||
"description": "A family of efficient AI models under 10B parameters performant in science, math, and coding through innovative training techniques",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
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|
||||
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|
||||
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|
||||
"last_updated": "10 months ago",
|
||||
"url": "https://ollama.com/library/falcon3"
|
||||
},
|
||||
{
|
||||
"name": "granite3.1-moe",
|
||||
"description": "The IBM Granite 1B and 3B models are long-context mixture of experts (MoE) Granite models from IBM designed for low latency usage",
|
||||
"capabilities": ["tools"],
|
||||
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|
||||
"pulls": "1M",
|
||||
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|
||||
"last_updated": "9 months ago",
|
||||
"url": "https://ollama.com/library/granite3.1-moe"
|
||||
},
|
||||
{
|
||||
"name": "qwen2.5vl",
|
||||
"description": "Flagship vision-language model of Qwen and also a significant leap from the previous Qwen2-VL",
|
||||
"capabilities": ["vision"],
|
||||
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|
||||
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|
||||
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|
||||
"last_updated": "5 months ago",
|
||||
"url": "https://ollama.com/library/qwen2.5vl"
|
||||
},
|
||||
{
|
||||
"name": "snowflake-arctic-embed",
|
||||
"description": "A suite of text embedding models by Snowflake, optimized for performance",
|
||||
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|
||||
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|
||||
"pulls": "951.1K",
|
||||
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|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/snowflake-arctic-embed"
|
||||
},
|
||||
{
|
||||
"name": "orca-mini",
|
||||
"description": "A general-purpose model ranging from 3 billion parameters to 70 billion, suitable for entry-level hardware",
|
||||
"capabilities": [],
|
||||
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|
||||
"pulls": "930.6K",
|
||||
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|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/orca-mini"
|
||||
},
|
||||
{
|
||||
"name": "llama4",
|
||||
"description": "Meta's latest collection of multimodal models",
|
||||
"capabilities": ["vision", "tools"],
|
||||
"parameter_sizes": ["16x17b", "128x17b"],
|
||||
"pulls": "751.9K",
|
||||
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|
||||
"last_updated": "4 months ago",
|
||||
"url": "https://ollama.com/library/llama4"
|
||||
},
|
||||
{
|
||||
"name": "phi",
|
||||
"description": "Phi-2: a 2.7B language model by Microsoft Research that demonstrates outstanding reasoning and language understanding capabilities",
|
||||
"capabilities": ["thinking"],
|
||||
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|
||||
"pulls": "730.7K",
|
||||
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|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/phi"
|
||||
},
|
||||
{
|
||||
"name": "mistral-small3.2",
|
||||
"description": "An update to Mistral Small that improves on function calling, instruction following, and less repetition errors",
|
||||
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|
||||
"parameter_sizes": ["24b"],
|
||||
"pulls": "718.3K",
|
||||
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|
||||
"last_updated": "4 months ago",
|
||||
"url": "https://ollama.com/library/mistral-small3.2"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-mixtral",
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
"last_updated": "10 months ago",
|
||||
"url": "https://ollama.com/library/dolphin-mixtral"
|
||||
},
|
||||
{
|
||||
"name": "gemma3n",
|
||||
"description": "Gemma 3n models are designed for efficient execution on everyday devices such as laptops, tablets or phones",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["e2b", "e4b"],
|
||||
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|
||||
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|
||||
"last_updated": "4 months ago",
|
||||
"url": "https://ollama.com/library/gemma3n"
|
||||
},
|
||||
{
|
||||
"name": "granite3.3",
|
||||
"description": "IBM Granite 2B and 8B models are 128K context length language models that have been fine-tuned for improved reasoning and instruction-following capabilities",
|
||||
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|
||||
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|
||||
"pulls": "660.7K",
|
||||
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|
||||
"last_updated": "6 months ago",
|
||||
"url": "https://ollama.com/library/granite3.3"
|
||||
},
|
||||
{
|
||||
"name": "cogito",
|
||||
"description": "Cogito v1 Preview is a family of hybrid reasoning models by Deep Cogito that outperform the best available open models of the same size",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
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|
||||
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|
||||
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|
||||
"last_updated": "6 months ago",
|
||||
"url": "https://ollama.com/library/cogito"
|
||||
},
|
||||
{
|
||||
"name": "phi4-reasoning",
|
||||
"description": "Phi 4 reasoning and reasoning plus are 14-billion parameter open-weight reasoning models that rival much larger models on complex reasoning tasks",
|
||||
"capabilities": ["thinking"],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/phi4-reasoning"
|
||||
},
|
||||
{
|
||||
"name": "openthinker",
|
||||
"description": "A fully open-source family of reasoning models built using a dataset derived by distilling DeepSeek-R1",
|
||||
"capabilities": ["thinking"],
|
||||
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|
||||
"pulls": "604.4K",
|
||||
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|
||||
"last_updated": "6 months ago",
|
||||
"url": "https://ollama.com/library/openthinker"
|
||||
},
|
||||
{
|
||||
"name": "magistral",
|
||||
"description": "Magistral is a small, efficient reasoning model with 24B parameters, excelling in domain-specific, transparent, and multilingual reasoning",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
"parameter_sizes": ["24b"],
|
||||
"pulls": "583.4K",
|
||||
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|
||||
"last_updated": "4 months ago",
|
||||
"url": "https://ollama.com/library/magistral"
|
||||
},
|
||||
{
|
||||
"name": "deepscaler",
|
||||
"description": "A fine-tuned version of Deepseek-R1-Distilled-Qwen-1.5B that surpasses the performance of OpenAI's o1-preview with just 1.5B parameters",
|
||||
"capabilities": ["thinking"],
|
||||
"parameter_sizes": ["1.5b"],
|
||||
"pulls": "573.2K",
|
||||
"tags_count": 5,
|
||||
"last_updated": "8 months ago",
|
||||
"url": "https://ollama.com/library/deepscaler"
|
||||
},
|
||||
{
|
||||
"name": "codestral",
|
||||
"description": "Codestral is Mistral AI's first-ever code model designed for code generation tasks",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["22b"],
|
||||
"pulls": "503.9K",
|
||||
"tags_count": 17,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/codestral"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-phi",
|
||||
"description": "2.7B uncensored Dolphin model by Eric Hartford, based on the Phi language model by Microsoft Research",
|
||||
"capabilities": [],
|
||||
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|
||||
"pulls": "569.2K",
|
||||
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|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/dolphin-phi"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-mistral",
|
||||
"description": "The uncensored Dolphin model based on Mistral that excels at coding tasks. Updated to version 2.8",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "409.3K",
|
||||
"tags_count": 120,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/dolphin-mistral"
|
||||
},
|
||||
{
|
||||
"name": "wizard-vicuna-uncensored",
|
||||
"description": "Wizard Vicuna Uncensored is a 7B, 13B, and 30B parameter model based on Llama 2 uncensored by Eric Hartford",
|
||||
"capabilities": [],
|
||||
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|
||||
"pulls": "255.1K",
|
||||
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|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/wizard-vicuna-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-starcoder2",
|
||||
"description": "A 7B and 15B uncensored variant of the Dolphin model family that excels at coding, based on StarCoder2",
|
||||
"capabilities": ["tools"],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "wizardlm-uncensored",
|
||||
"description": "Uncensored version of Wizard LM model",
|
||||
"capabilities": [],
|
||||
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|
||||
"pulls": "89.5K",
|
||||
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|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/wizardlm-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "llama-3.2-3b-instruct-abliterated",
|
||||
"description": "An uncensored version of the original Llama-3.2-3B-Instruct, created via abliteration",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["3b"],
|
||||
"pulls": "508.1K",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "josiefied-qwen3-uncensored",
|
||||
"description": "Qwen3, but Josiefied and uncensored",
|
||||
"capabilities": [],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/josiefied-qwen3-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "abliterated-llama3.1-8b",
|
||||
"description": "Ablitered v3 llama-3.1 8b with uncensored prompt",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "dolphin-uncensored",
|
||||
"description": "Dolphin is an uncensored multilingual chat tuned model by Eric Hartford, conformant to system prompt, good at coding and various tasks",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "specialized-uncensored-openai-20b",
|
||||
"description": "Specialized uncensored/abliterated quants for new OpenAI 20B MOE - Mixture of Experts Model at 80+ T/S (quantized Q5_1)",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "lexi-uncensored",
|
||||
"description": "Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service",
|
||||
"capabilities": [],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"url": "https://ollama.com/library/lexi-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "llama3-uncensored",
|
||||
"description": "Uncensored version of Llama3 8b",
|
||||
"capabilities": [],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "llama3-uncensored-long",
|
||||
"description": "This model was created with the goal for a good llama 3 uncencored model with long context",
|
||||
"capabilities": [],
|
||||
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|
||||
"pulls": "13.5K",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "deepseek-r1-distilled",
|
||||
"description": "DeepSeek's first generation reasoning models with comparable performance to OpenAI-o1",
|
||||
"capabilities": ["thinking"],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "586.4K",
|
||||
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|
||||
"last_updated": "5 months ago",
|
||||
"url": "https://ollama.com/library/deepseek-r1-distilled"
|
||||
},
|
||||
{
|
||||
"name": "llama-3.2-11b-vision-abliterated",
|
||||
"description": "From huihui-ai/Llama-3.2-11B-Vision-Instruct-abliterated",
|
||||
"capabilities": ["vision"],
|
||||
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|
||||
"pulls": "69.8K",
|
||||
"tags_count": 2,
|
||||
"last_updated": "8 months ago",
|
||||
"url": "https://ollama.com/library/llama-3.2-11b-vision-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "qwen2.5-1m",
|
||||
"description": "Qwen2.5-1M is the long-context version of the Qwen2.5 series models, supporting a context length of up to 1M tokens",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "31K",
|
||||
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|
||||
"last_updated": "9 months ago",
|
||||
"url": "https://ollama.com/library/qwen2.5-1m"
|
||||
},
|
||||
{
|
||||
"name": "qwen3-abliterated",
|
||||
"description": "This is the abliterated version of Qwen3, which is the latest generation of large language models in Qwen series",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "23.9K",
|
||||
"tags_count": 6,
|
||||
"last_updated": "5 months ago",
|
||||
"url": "https://ollama.com/library/qwen3-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-3.0-llama3.1-8b-abliterated",
|
||||
"description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "21.7K",
|
||||
"tags_count": 5,
|
||||
"last_updated": "9 months ago",
|
||||
"url": "https://ollama.com/library/dolphin-3.0-llama3.1-8b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "gemma-3n-abliterated",
|
||||
"description": "This is an uncensored version of google/gemma-3n created with abliteration",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["9b"],
|
||||
"pulls": "13.4K",
|
||||
"tags_count": 2,
|
||||
"last_updated": "3 months ago",
|
||||
"url": "https://ollama.com/library/gemma-3n-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "neuraldaredevil-8b-abliterated",
|
||||
"description": "Contains fp16 version of the excellent NeuralDaredevil-8B-abliterated",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "9,677",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/neuraldaredevil-8b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "mlabonne-neuraldaredevil-8b-abliterated",
|
||||
"description": "mlabonne/NeuralDaredevil-8B-abliterated",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "7,679",
|
||||
"tags_count": 4,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/mlabonne-neuraldaredevil-8b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "phi3-medium-abliterated",
|
||||
"description": "This is a phi3 medium model that has had the prompt rejection neurons snipped which is better than fine tuning",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["14b"],
|
||||
"pulls": "6,591",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/phi3-medium-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "gemma2-9b-abliterated",
|
||||
"description": "Abliterated version of Google's Gemma 2 9B",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["9b"],
|
||||
"pulls": "6,386",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/gemma2-9b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "codeqwen1.5",
|
||||
"description": "CodeQwen1.5 is a large language model pretrained on a large amount of code data",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "182K",
|
||||
"tags_count": 30,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/codeqwen1.5"
|
||||
},
|
||||
{
|
||||
"name": "deepcoder",
|
||||
"description": "DeepCoder is a fully open-Source 14B coder model at O3-mini level, with a 1.5B version also available",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["1.5b", "14b"],
|
||||
"pulls": "352.4K",
|
||||
"tags_count": 9,
|
||||
"last_updated": "6 months ago",
|
||||
"url": "https://ollama.com/library/deepcoder"
|
||||
},
|
||||
{
|
||||
"name": "granite-code",
|
||||
"description": "A family of open foundation models by IBM for Code Intelligence",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["8b", "20b", "34b"],
|
||||
"pulls": "295.1K",
|
||||
"tags_count": 162,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/granite-code"
|
||||
},
|
||||
{
|
||||
"name": "mistral-large-2",
|
||||
"description": "Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
"parameter_sizes": ["123b"],
|
||||
"pulls": "265.9K",
|
||||
"tags_count": 32,
|
||||
"last_updated": "11 months ago",
|
||||
"url": "https://ollama.com/library/mistral-large-2"
|
||||
},
|
||||
{
|
||||
"name": "starcoder",
|
||||
"description": "StarCoder is a code generation model trained on 80+ programming languages",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["15b"],
|
||||
"pulls": "228.8K",
|
||||
"tags_count": 100,
|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/starcoder"
|
||||
},
|
||||
{
|
||||
"name": "opencoder",
|
||||
"description": "OpenCoder is an open and reproducible code LLM family which includes 1.5B and 8B models, supporting chat in English and Chinese languages",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["1.5b", "8b"],
|
||||
"pulls": "194.3K",
|
||||
"tags_count": 9,
|
||||
"last_updated": "11 months ago",
|
||||
"url": "https://ollama.com/library/opencoder"
|
||||
},
|
||||
{
|
||||
"name": "llama-3.2-1b-abliterated",
|
||||
"description": "Abliterated version for small reasoning tasks from Llama 3.2 1B",
|
||||
"capabilities": ["thinking"],
|
||||
"parameter_sizes": ["1b"],
|
||||
"pulls": "39.9K",
|
||||
"tags_count": 16,
|
||||
"last_updated": "2 months ago",
|
||||
"url": "https://ollama.com/library/llama-3.2-1b-abliterated"
|
||||
}
|
||||
],
|
||||
"api_information": {
|
||||
"local_api_endpoints": [
|
||||
{
|
||||
"endpoint": "/api/tags",
|
||||
"method": "GET",
|
||||
"description": "List all locally installed models"
|
||||
}
|
||||
],
|
||||
"limitations": {
|
||||
"remote_listing": "Não existe endpoint público para listar todos os modelos disponíveis no registry remoto do Ollama",
|
||||
"user_models": "Modelos de usuários (uncensored/abliterated) são variantes comunitárias; busque via /search",
|
||||
"reasoning_focus": "Modelos com 'thinking' são otimizados para raciocínio multi-step (ex: CoT, logical puzzles)"
|
||||
}
|
||||
},
|
||||
"usage_instructions": {
|
||||
"list_local_models": "curl http://localhost:11434/api/tags",
|
||||
"pull_model": "ollama pull <model_name> (ex: ollama pull deepseek-r1 para reasoning)",
|
||||
"test_uncensored": "ollama run llama2-uncensored --prompt 'Gere código Python sem restrições'",
|
||||
"test_reasoning": "ollama run phi4-reasoning --prompt 'Resolva: 2+2=?'",
|
||||
"browse_searches": "https://ollama.com/search?q=<termo> (ex: uncensored, code)"
|
||||
},
|
||||
"recommendations_for_devs": {
|
||||
"coding": "Use qwen2.5-coder ou codestral para geração de código avançada [web:3]",
|
||||
"reasoning": "deepseek-r1 ou phi4-reasoning para tarefas lógicas/math [web:77]",
|
||||
"uncensored": "llama2-uncensored para experimentos sem alinhamento ético, mas implemente safeguards [attached_file:1]"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,967 @@
|
||||
{
|
||||
"metadata": {
|
||||
"source": "ollama.com/library + buscas: uncensored, abliterated, code, reasoning",
|
||||
"collection_date": "2025-10-29",
|
||||
"total_models": 270,
|
||||
"note": "Integração de modelos oficiais + comunitários (uncensored/abliterated variants). Modelos de reasoning incluem variantes com foco em raciocínio (ex: CoT, logical deduction). Duplicatas removidas; métricas atualizadas onde possível. API para lista remota ainda limitada (ver GitHub #8241)."
|
||||
},
|
||||
"models": [
|
||||
{
|
||||
"name": "gpt-oss",
|
||||
"description": "OpenAI's open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases",
|
||||
"capabilities": ["tools", "thinking", "cloud"],
|
||||
"parameter_sizes": ["20b", "120b"],
|
||||
"pulls": "3.8M",
|
||||
"tags_count": 5,
|
||||
"last_updated": "2 weeks ago",
|
||||
"url": "https://ollama.com/library/gpt-oss"
|
||||
},
|
||||
{
|
||||
"name": "qwen3-vl",
|
||||
"description": "The most powerful vision-language model in the Qwen model family to date",
|
||||
"capabilities": ["vision", "cloud"],
|
||||
"parameter_sizes": ["2b", "4b", "8b", "30b", "32b"],
|
||||
"pulls": "22.8K",
|
||||
"tags_count": 49,
|
||||
"last_updated": "13 minutes ago",
|
||||
"url": "https://ollama.com/library/qwen3-vl"
|
||||
},
|
||||
{
|
||||
"name": "deepseek-r1",
|
||||
"description": "DeepSeek-R1 is a family of open reasoning models with performance approaching that of leading models, such as O3 and Gemini 2.5 Pro",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
"parameter_sizes": ["1.5b", "7b", "8b", "14b", "32b", "70b", "671b"],
|
||||
"pulls": "68.4M",
|
||||
"tags_count": 35,
|
||||
"last_updated": "3 months ago",
|
||||
"url": "https://ollama.com/library/deepseek-r1"
|
||||
},
|
||||
{
|
||||
"name": "qwen3-coder",
|
||||
"description": "Alibaba's performant long context models for agentic and coding tasks",
|
||||
"capabilities": ["tools", "cloud"],
|
||||
"parameter_sizes": ["30b", "480b"],
|
||||
"pulls": "575.7K",
|
||||
"tags_count": 10,
|
||||
"last_updated": "1 month ago",
|
||||
"url": "https://ollama.com/library/qwen3-coder"
|
||||
},
|
||||
{
|
||||
"name": "gemma3",
|
||||
"description": "The current, most capable model that runs on a single GPU",
|
||||
"capabilities": ["vision"],
|
||||
"parameter_sizes": ["270m", "1b", "4b", "12b", "27b"],
|
||||
"pulls": "23.1M",
|
||||
"tags_count": 26,
|
||||
"last_updated": "2 months ago",
|
||||
"url": "https://ollama.com/library/gemma3"
|
||||
},
|
||||
{
|
||||
"name": "glm-4.6",
|
||||
"description": "Advanced agentic, reasoning and coding capabilities",
|
||||
"capabilities": ["cloud"],
|
||||
"parameter_sizes": [],
|
||||
"pulls": "11.8K",
|
||||
"tags_count": 1,
|
||||
"last_updated": "2 weeks ago",
|
||||
"url": "https://ollama.com/library/glm-4.6"
|
||||
},
|
||||
{
|
||||
"name": "embeddinggemma",
|
||||
"description": "EmbeddingGemma is a 300M parameter embedding model from Google",
|
||||
"capabilities": ["embedding"],
|
||||
"parameter_sizes": ["300m"],
|
||||
"pulls": "111.2K",
|
||||
"tags_count": 5,
|
||||
"last_updated": "1 month ago",
|
||||
"url": "https://ollama.com/library/embeddinggemma"
|
||||
},
|
||||
{
|
||||
"name": "qwen3",
|
||||
"description": "Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
"parameter_sizes": ["0.6b", "1.7b", "4b", "8b", "14b", "30b", "32b", "235b"],
|
||||
"pulls": "11.7M",
|
||||
"tags_count": 58,
|
||||
"last_updated": "2 weeks ago",
|
||||
"url": "https://ollama.com/library/qwen3"
|
||||
},
|
||||
{
|
||||
"name": "deepseek-v3.1",
|
||||
"description": "DeepSeek-V3.1-Terminus is a hybrid model that supports both thinking mode and non-thinking mode",
|
||||
"capabilities": ["tools", "thinking", "cloud"],
|
||||
"parameter_sizes": ["671b"],
|
||||
"pulls": "120.6K",
|
||||
"tags_count": 8,
|
||||
"last_updated": "1 month ago",
|
||||
"url": "https://ollama.com/library/deepseek-v3.1"
|
||||
},
|
||||
{
|
||||
"name": "llama3.1",
|
||||
"description": "Llama 3.1 is a new state-of-the-art model from Meta available in 8B, 70B and 405B parameter sizes",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["8b", "70b", "405b"],
|
||||
"pulls": "105.1M",
|
||||
"tags_count": 93,
|
||||
"last_updated": "11 months ago",
|
||||
"url": "https://ollama.com/library/llama3.1"
|
||||
},
|
||||
{
|
||||
"name": "nomic-embed-text",
|
||||
"description": "A high-performing open embedding model with a large token context window",
|
||||
"capabilities": ["embedding"],
|
||||
"parameter_sizes": [],
|
||||
"pulls": "44.3M",
|
||||
"tags_count": 3,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/nomic-embed-text"
|
||||
},
|
||||
{
|
||||
"name": "llama3.2",
|
||||
"description": "Meta's Llama 3.2 goes small with 1B and 3B models",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["1b", "3b"],
|
||||
"pulls": "42.6M",
|
||||
"tags_count": 63,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llama3.2"
|
||||
},
|
||||
{
|
||||
"name": "mistral",
|
||||
"description": "The 7B model released by Mistral AI, updated to version 0.3",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "21.3M",
|
||||
"tags_count": 84,
|
||||
"last_updated": "3 months ago",
|
||||
"url": "https://ollama.com/library/mistral"
|
||||
},
|
||||
{
|
||||
"name": "qwen2.5",
|
||||
"description": "Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["0.5b", "1.5b", "3b", "7b", "14b", "32b", "72b"],
|
||||
"pulls": "15.8M",
|
||||
"tags_count": 133,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/qwen2.5"
|
||||
},
|
||||
{
|
||||
"name": "phi3",
|
||||
"description": "Phi-3 is a family of lightweight 3B (Mini) and 14B (Medium) state-of-the-art open models by Microsoft",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["3.8b", "14b"],
|
||||
"pulls": "12.5M",
|
||||
"tags_count": 72,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/phi3"
|
||||
},
|
||||
{
|
||||
"name": "llama3",
|
||||
"description": "Meta Llama 3: The most capable openly available LLM to date",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b", "70b"],
|
||||
"pulls": "11.5M",
|
||||
"tags_count": 68,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llama3"
|
||||
},
|
||||
{
|
||||
"name": "llava",
|
||||
"description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6",
|
||||
"capabilities": ["vision"],
|
||||
"parameter_sizes": ["7b", "13b", "34b"],
|
||||
"pulls": "11.1M",
|
||||
"tags_count": 98,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llava"
|
||||
},
|
||||
{
|
||||
"name": "gemma2",
|
||||
"description": "Google Gemma 2 is a high-performing and efficient model available in three sizes: 2B, 9B, and 27B",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["2b", "9b", "27b"],
|
||||
"pulls": "8.6M",
|
||||
"tags_count": 94,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/gemma2"
|
||||
},
|
||||
{
|
||||
"name": "qwen2.5-coder",
|
||||
"description": "The latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, and code fixing",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["0.5b", "1.5b", "3b", "7b", "14b", "32b"],
|
||||
"pulls": "7.9M",
|
||||
"tags_count": 199,
|
||||
"last_updated": "5 months ago",
|
||||
"url": "https://ollama.com/library/qwen2.5-coder"
|
||||
},
|
||||
{
|
||||
"name": "phi4",
|
||||
"description": "Phi-4 is a 14B parameter, state-of-the-art open model from Microsoft",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["14b"],
|
||||
"pulls": "5.8M",
|
||||
"tags_count": 5,
|
||||
"last_updated": "9 months ago",
|
||||
"url": "https://ollama.com/library/phi4"
|
||||
},
|
||||
{
|
||||
"name": "gemma",
|
||||
"description": "Gemma is a family of lightweight, state-of-the-art open models built by Google DeepMind. Updated to version 1.1",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["2b", "7b"],
|
||||
"pulls": "5.4M",
|
||||
"tags_count": 102,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/gemma"
|
||||
},
|
||||
{
|
||||
"name": "mxbai-embed-large",
|
||||
"description": "State-of-the-art large embedding model from mixedbread.ai",
|
||||
"capabilities": ["embedding"],
|
||||
"parameter_sizes": ["335m"],
|
||||
"pulls": "5.2M",
|
||||
"tags_count": 4,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/mxbai-embed-large"
|
||||
},
|
||||
{
|
||||
"name": "qwen",
|
||||
"description": "Qwen 1.5 is a series of large language models by Alibaba Cloud spanning from 0.5B to 110B parameters",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["0.5b", "1.8b", "4b", "7b", "14b", "32b", "72b", "110b"],
|
||||
"pulls": "5M",
|
||||
"tags_count": 379,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/qwen"
|
||||
},
|
||||
{
|
||||
"name": "qwen2",
|
||||
"description": "Qwen2 is a new series of large language models from Alibaba group",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["0.5b", "1.5b", "7b", "72b"],
|
||||
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|
||||
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|
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|
||||
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||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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||||
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|
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
"url": "https://ollama.com/library/dolphin-mistral"
|
||||
},
|
||||
{
|
||||
"name": "wizard-vicuna-uncensored",
|
||||
"description": "Wizard Vicuna Uncensored is a 7B, 13B, and 30B parameter model based on Llama 2 uncensored by Eric Hartford",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b", "13b", "30b"],
|
||||
"pulls": "255.1K",
|
||||
"tags_count": 49,
|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/wizard-vicuna-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-starcoder2",
|
||||
"description": "A 7B and 15B uncensored variant of the Dolphin model family that excels at coding, based on StarCoder2",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["7b", "15b"],
|
||||
"pulls": "113.1K",
|
||||
"tags_count": 35,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/dolphin-starcoder2"
|
||||
},
|
||||
{
|
||||
"name": "wizardlm-uncensored",
|
||||
"description": "Uncensored version of Wizard LM model",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b", "13b"],
|
||||
"pulls": "89.5K",
|
||||
"tags_count": 18,
|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/wizardlm-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "llama-3.2-3b-instruct-abliterated",
|
||||
"description": "An uncensored version of the original Llama-3.2-3B-Instruct, created via abliteration",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["3b"],
|
||||
"pulls": "508.1K",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llama-3.2-3b-instruct-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "josiefied-qwen3-uncensored",
|
||||
"description": "Qwen3, but Josiefied and uncensored",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "71.5K",
|
||||
"tags_count": 47,
|
||||
"last_updated": "4 months ago",
|
||||
"url": "https://ollama.com/library/josiefied-qwen3-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "abliterated-llama3.1-8b",
|
||||
"description": "Ablitered v3 llama-3.1 8b with uncensored prompt",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "31K",
|
||||
"tags_count": 38,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/abliterated-llama3.1-8b"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-uncensored",
|
||||
"description": "Dolphin is an uncensored multilingual chat tuned model by Eric Hartford, conformant to system prompt, good at coding and various tasks",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "25.5K",
|
||||
"tags_count": 15,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/dolphin-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "specialized-uncensored-openai-20b",
|
||||
"description": "Specialized uncensored/abliterated quants for new OpenAI 20B MOE - Mixture of Experts Model at 80+ T/S (quantized Q5_1)",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["20b"],
|
||||
"pulls": "22.9K",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 month ago",
|
||||
"url": "https://ollama.com/library/specialized-uncensored-openai-20b"
|
||||
},
|
||||
{
|
||||
"name": "lexi-uncensored",
|
||||
"description": "Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "15.3K",
|
||||
"tags_count": 9,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/lexi-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "llama3-uncensored",
|
||||
"description": "Uncensored version of Llama3 8b",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "13.6K",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llama3-uncensored"
|
||||
},
|
||||
{
|
||||
"name": "llama3-uncensored-long",
|
||||
"description": "This model was created with the goal for a good llama 3 uncencored model with long context",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "13.5K",
|
||||
"tags_count": 4,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/llama3-uncensored-long"
|
||||
},
|
||||
{
|
||||
"name": "deepseek-r1-distilled",
|
||||
"description": "DeepSeek's first generation reasoning models with comparable performance to OpenAI-o1",
|
||||
"capabilities": ["thinking"],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "586.4K",
|
||||
"tags_count": 55,
|
||||
"last_updated": "5 months ago",
|
||||
"url": "https://ollama.com/library/deepseek-r1-distilled"
|
||||
},
|
||||
{
|
||||
"name": "llama-3.2-11b-vision-abliterated",
|
||||
"description": "From huihui-ai/Llama-3.2-11B-Vision-Instruct-abliterated",
|
||||
"capabilities": ["vision"],
|
||||
"parameter_sizes": ["11b"],
|
||||
"pulls": "69.8K",
|
||||
"tags_count": 2,
|
||||
"last_updated": "8 months ago",
|
||||
"url": "https://ollama.com/library/llama-3.2-11b-vision-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "qwen2.5-1m",
|
||||
"description": "Qwen2.5-1M is the long-context version of the Qwen2.5 series models, supporting a context length of up to 1M tokens",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "31K",
|
||||
"tags_count": 11,
|
||||
"last_updated": "9 months ago",
|
||||
"url": "https://ollama.com/library/qwen2.5-1m"
|
||||
},
|
||||
{
|
||||
"name": "qwen3-abliterated",
|
||||
"description": "This is the abliterated version of Qwen3, which is the latest generation of large language models in Qwen series",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "23.9K",
|
||||
"tags_count": 6,
|
||||
"last_updated": "5 months ago",
|
||||
"url": "https://ollama.com/library/qwen3-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "dolphin-3.0-llama3.1-8b-abliterated",
|
||||
"description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "21.7K",
|
||||
"tags_count": 5,
|
||||
"last_updated": "9 months ago",
|
||||
"url": "https://ollama.com/library/dolphin-3.0-llama3.1-8b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "gemma-3n-abliterated",
|
||||
"description": "This is an uncensored version of google/gemma-3n created with abliteration",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["9b"],
|
||||
"pulls": "13.4K",
|
||||
"tags_count": 2,
|
||||
"last_updated": "3 months ago",
|
||||
"url": "https://ollama.com/library/gemma-3n-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "neuraldaredevil-8b-abliterated",
|
||||
"description": "Contains fp16 version of the excellent NeuralDaredevil-8B-abliterated",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "9,677",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/neuraldaredevil-8b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "mlabonne-neuraldaredevil-8b-abliterated",
|
||||
"description": "mlabonne/NeuralDaredevil-8B-abliterated",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["8b"],
|
||||
"pulls": "7,679",
|
||||
"tags_count": 4,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/mlabonne-neuraldaredevil-8b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "phi3-medium-abliterated",
|
||||
"description": "This is a phi3 medium model that has had the prompt rejection neurons snipped which is better than fine tuning",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["14b"],
|
||||
"pulls": "6,591",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/phi3-medium-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "gemma2-9b-abliterated",
|
||||
"description": "Abliterated version of Google's Gemma 2 9B",
|
||||
"capabilities": [],
|
||||
"parameter_sizes": ["9b"],
|
||||
"pulls": "6,386",
|
||||
"tags_count": 1,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/gemma2-9b-abliterated"
|
||||
},
|
||||
{
|
||||
"name": "codeqwen1.5",
|
||||
"description": "CodeQwen1.5 is a large language model pretrained on a large amount of code data",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["7b"],
|
||||
"pulls": "182K",
|
||||
"tags_count": 30,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/codeqwen1.5"
|
||||
},
|
||||
{
|
||||
"name": "deepcoder",
|
||||
"description": "DeepCoder is a fully open-Source 14B coder model at O3-mini level, with a 1.5B version also available",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["1.5b", "14b"],
|
||||
"pulls": "352.4K",
|
||||
"tags_count": 9,
|
||||
"last_updated": "6 months ago",
|
||||
"url": "https://ollama.com/library/deepcoder"
|
||||
},
|
||||
{
|
||||
"name": "granite-code",
|
||||
"description": "A family of open foundation models by IBM for Code Intelligence",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["8b", "20b", "34b"],
|
||||
"pulls": "295.1K",
|
||||
"tags_count": 162,
|
||||
"last_updated": "1 year ago",
|
||||
"url": "https://ollama.com/library/granite-code"
|
||||
},
|
||||
{
|
||||
"name": "mistral-large-2",
|
||||
"description": "Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning",
|
||||
"capabilities": ["tools", "thinking"],
|
||||
"parameter_sizes": ["123b"],
|
||||
"pulls": "265.9K",
|
||||
"tags_count": 32,
|
||||
"last_updated": "11 months ago",
|
||||
"url": "https://ollama.com/library/mistral-large-2"
|
||||
},
|
||||
{
|
||||
"name": "starcoder",
|
||||
"description": "StarCoder is a code generation model trained on 80+ programming languages",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["15b"],
|
||||
"pulls": "228.8K",
|
||||
"tags_count": 100,
|
||||
"last_updated": "2 years ago",
|
||||
"url": "https://ollama.com/library/starcoder"
|
||||
},
|
||||
{
|
||||
"name": "opencoder",
|
||||
"description": "OpenCoder is an open and reproducible code LLM family which includes 1.5B and 8B models, supporting chat in English and Chinese languages",
|
||||
"capabilities": ["tools"],
|
||||
"parameter_sizes": ["1.5b", "8b"],
|
||||
"pulls": "194.3K",
|
||||
"tags_count": 9,
|
||||
"last_updated": "11 months ago",
|
||||
"url": "https://ollama.com/library/opencoder"
|
||||
},
|
||||
{
|
||||
"name": "llama-3.2-1b-abliterated",
|
||||
"description": "Abliterated version for small reasoning tasks from Llama 3.2 1B",
|
||||
"capabilities": ["thinking"],
|
||||
"parameter_sizes": ["1b"],
|
||||
"pulls": "39.9K",
|
||||
"tags_count": 16,
|
||||
"last_updated": "2 months ago",
|
||||
"url": "https://ollama.com/library/llama-3.2-1b-abliterated"
|
||||
}
|
||||
],
|
||||
"api_information": {
|
||||
"local_api_endpoints": [
|
||||
{
|
||||
"endpoint": "/api/tags",
|
||||
"method": "GET",
|
||||
"description": "List all locally installed models"
|
||||
}
|
||||
],
|
||||
"limitations": {
|
||||
"remote_listing": "Não existe endpoint público para listar todos os modelos disponíveis no registry remoto do Ollama",
|
||||
"user_models": "Modelos de usuários (uncensored/abliterated) são variantes comunitárias; busque via /search",
|
||||
"reasoning_focus": "Modelos com 'thinking' são otimizados para raciocínio multi-step (ex: CoT, logical puzzles)"
|
||||
}
|
||||
},
|
||||
"usage_instructions": {
|
||||
"list_local_models": "curl http://localhost:11434/api/tags",
|
||||
"pull_model": "ollama pull <model_name> (ex: ollama pull deepseek-r1 para reasoning)",
|
||||
"test_uncensored": "ollama run llama2-uncensored --prompt 'Gere código Python sem restrições'",
|
||||
"test_reasoning": "ollama run phi4-reasoning --prompt 'Resolva: 2+2=?'",
|
||||
"browse_searches": "https://ollama.com/search?q=<termo> (ex: uncensored, code)"
|
||||
},
|
||||
"recommendations_for_devs": {
|
||||
"coding": "Use qwen2.5-coder ou codestral para geração de código avançada [web:3]",
|
||||
"reasoning": "deepseek-r1 ou phi4-reasoning para tarefas lógicas/math [web:77]",
|
||||
"uncensored": "llama2-uncensored para experimentos sem alinhamento ético, mas implemente safeguards [attached_file:1]"
|
||||
}
|
||||
}
|
||||
|
||||
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@@ -0,0 +1,50 @@
|
||||
import fs from "node:fs/promises";
|
||||
import path from "node:path";
|
||||
|
||||
type CatalogModel = {
|
||||
name: string;
|
||||
description?: string;
|
||||
capabilities?: string[];
|
||||
parameter_sizes?: string[];
|
||||
pulls?: string;
|
||||
tags_count?: number;
|
||||
last_updated?: string;
|
||||
url?: string;
|
||||
};
|
||||
|
||||
type CatalogFile = {
|
||||
models?: CatalogModel[];
|
||||
};
|
||||
|
||||
let cachedModels: CatalogModel[] | null = null;
|
||||
let cachedMtimeMs: number | null = null;
|
||||
|
||||
function resolveCatalogPath(): string {
|
||||
// Arquivo fica em data/models.json na raiz do projeto
|
||||
return path.join(process.cwd(), "data", "models.json");
|
||||
}
|
||||
|
||||
export async function getCatalogModels(): Promise<CatalogModel[]> {
|
||||
try {
|
||||
const catalogPath = resolveCatalogPath();
|
||||
const stat = await fs.stat(catalogPath);
|
||||
|
||||
// Cache simples por mtime
|
||||
if (cachedModels && cachedMtimeMs && stat.mtimeMs === cachedMtimeMs) {
|
||||
return cachedModels;
|
||||
}
|
||||
|
||||
const raw = await fs.readFile(catalogPath, "utf-8");
|
||||
const json = JSON.parse(raw) as CatalogFile;
|
||||
const models = Array.isArray(json.models) ? json.models : [];
|
||||
|
||||
cachedModels = models;
|
||||
cachedMtimeMs = stat.mtimeMs;
|
||||
return models;
|
||||
} catch (error) {
|
||||
// Em caso de erro, retorne array vazio para não quebrar a UI
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Whitespace-only changes.
+19
-50
@@ -1,6 +1,7 @@
|
||||
import { Ollama } from "ollama";
|
||||
import os from "node:os";
|
||||
import { spawn } from "node:child_process";
|
||||
import { getCatalogModels } from "./catalog";
|
||||
|
||||
export const OLLAMA_HOST = process.env.OLLAMA_HOST || "http://localhost:11434";
|
||||
|
||||
@@ -166,60 +167,28 @@ export async function deleteModel(modelName: string): Promise<void> {
|
||||
|
||||
export async function searchRemoteModels(query?: string): Promise<any[]> {
|
||||
try {
|
||||
// Ollama não tem API oficial para buscar modelos remotos
|
||||
// Vamos usar uma lista conhecida de modelos populares
|
||||
const popularModels = [
|
||||
"llama3.2:1b",
|
||||
"llama3.2:3b",
|
||||
"llama3.2:11b",
|
||||
"llama3.2:70b",
|
||||
"llama3.1:8b",
|
||||
"llama3.1:70b",
|
||||
"llama3.1:405b",
|
||||
"qwen2.5:1.5b",
|
||||
"qwen2.5:3b",
|
||||
"qwen2.5:7b",
|
||||
"qwen2.5:14b",
|
||||
"qwen2.5:32b",
|
||||
"qwen2.5:72b",
|
||||
"qwen2:0.5b",
|
||||
"qwen2:1.5b",
|
||||
"qwen2:3b",
|
||||
"qwen2:7b",
|
||||
"qwen2:14b",
|
||||
"qwen2:32b",
|
||||
"qwen2:72b",
|
||||
"mistral:7b",
|
||||
"mixtral:8x7b",
|
||||
"mixtral:8x22b",
|
||||
"phi3:mini",
|
||||
"phi3:medium",
|
||||
"phi3:large",
|
||||
"gemma2:2b",
|
||||
"gemma2:9b",
|
||||
"gemma2:27b",
|
||||
"codellama:7b",
|
||||
"codellama:13b",
|
||||
"codellama:34b",
|
||||
"deepseek-coder:1.3b",
|
||||
"deepseek-coder:6.7b",
|
||||
"deepseek-coder:33b",
|
||||
];
|
||||
const catalog = await getCatalogModels();
|
||||
|
||||
// Filtrar por query se fornecida
|
||||
const filteredModels = query
|
||||
? popularModels.filter((model) =>
|
||||
model.toLowerCase().includes(query.toLowerCase())
|
||||
)
|
||||
: popularModels;
|
||||
// Normalizar pesquisa
|
||||
const q = (query || "").trim().toLowerCase();
|
||||
|
||||
// Verificar quais já estão instalados
|
||||
const filtered = q
|
||||
? catalog.filter((m) => {
|
||||
const name = m.name?.toLowerCase() || "";
|
||||
const desc = m.description?.toLowerCase() || "";
|
||||
return name.includes(q) || desc.includes(q);
|
||||
})
|
||||
: catalog;
|
||||
|
||||
// Modelos instalados
|
||||
const installedModels = await listModelsViaSdk();
|
||||
const installedNames = installedModels.map((m) => m.name);
|
||||
const installedNames = new Set(installedModels.map((m) => m.name));
|
||||
|
||||
return filteredModels.map((modelName) => ({
|
||||
name: modelName,
|
||||
installed: installedNames.includes(modelName),
|
||||
// Retorno no formato esperado pelo frontend
|
||||
return filtered.map((m) => ({
|
||||
name: m.name,
|
||||
description: m.description,
|
||||
installed: installedNames.has(m.name),
|
||||
remote: true,
|
||||
}));
|
||||
} catch (error) {
|
||||
|
||||
Reference in new issue
Block a user