From 30290ae87ae30ff804a0999cbf2e286d65ddefb7 Mon Sep 17 00:00:00 2001 From: Evandro Fonseca Junior Date: Wed, 29 Oct 2025 17:10:35 -0300 Subject: [PATCH] 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. --- app/api/ollama/catalog/route.ts | 0 data/models.json | 967 ++++++++++++++++++++++++++++ database/models.json | 967 ++++++++++++++++++++++++++++ database/ollahub-preferences.db-shm | Bin 32768 -> 32768 bytes database/ollahub-preferences.db-wal | Bin 12392 -> 28872 bytes database/ollahub.db-shm | Bin 32768 -> 32768 bytes database/ollahub.db-wal | Bin 152472 -> 243112 bytes lib/catalog.ts | 50 ++ lib/hardware.ts | 0 lib/ollama.ts | 69 +- 10 files changed, 2003 insertions(+), 50 deletions(-) create mode 100644 app/api/ollama/catalog/route.ts create mode 100644 data/models.json create mode 100644 database/models.json create mode 100644 lib/catalog.ts create mode 100644 lib/hardware.ts diff --git a/app/api/ollama/catalog/route.ts b/app/api/ollama/catalog/route.ts new file mode 100644 index 0000000..e69de29 diff --git a/data/models.json b/data/models.json new file mode 100644 index 0000000..e7f6502 --- /dev/null +++ b/data/models.json @@ -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"], + "pulls": "4.4M", + "tags_count": 97, + "last_updated": "1 year ago", + "url": "https://ollama.com/library/qwen2" + }, + { + "name": "llama2", + "description": "Llama 2 is a collection of foundation language models ranging from 7B to 70B parameters", + "capabilities": [], + "parameter_sizes": ["7b", "13b", "70b"], + "pulls": "4.4M", + "tags_count": 102, + "last_updated": "1 year ago", + "url": "https://ollama.com/library/llama2" + }, + { + "name": "minicpm-v", + "description": "A series of multimodal LLMs (MLLMs) designed for vision-language understanding", + "capabilities": ["vision"], + "parameter_sizes": ["8b"], + "pulls": "3.9M", + "tags_count": 17, + "last_updated": "11 months ago", + "url": "https://ollama.com/library/minicpm-v" + }, + { + "name": "dolphin3", + "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": "3.4M", + "tags_count": 5, + "last_updated": "9 months ago", + "url": "https://ollama.com/library/dolphin3" + }, + { + "name": "codellama", + "description": "A large language model that can use text prompts to generate and discuss code", + "capabilities": [], + "parameter_sizes": ["7b", "13b", "34b", "70b"], + "pulls": "3.3M", + "tags_count": 199, + "last_updated": "1 year ago", + "url": "https://ollama.com/library/codellama" + }, + { + "name": "olmo2", + "description": "OLMo 2 is a new family of 7B and 13B models trained on up to 5T tokens", + "capabilities": [], + "parameter_sizes": ["7b", "13b"], + "pulls": "3.3M", + "tags_count": 9, + "last_updated": "9 months ago", + "url": "https://ollama.com/library/olmo2" + }, + { + "name": "tinyllama", + "description": "The TinyLlama project is an open endeavor to train a compact 1.1B Llama model on 3 trillion tokens", + "capabilities": [], + "parameter_sizes": ["1.1b"], + "pulls": "3M", + "tags_count": 36, + "last_updated": "1 year ago", + "url": "https://ollama.com/library/tinyllama" + }, + { + "name": "llama3.2-vision", + "description": "Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes", + "capabilities": ["vision"], + "parameter_sizes": ["11b", "90b"], + "pulls": "2.8M", + "tags_count": 9, + "last_updated": "5 months ago", + "url": "https://ollama.com/library/llama3.2-vision" + }, + { + "name": "mistral-nemo", + "description": "A state-of-the-art 12B model with 128k context length, built by Mistral AI in collaboration with NVIDIA", + "capabilities": ["tools"], + "parameter_sizes": ["12b"], + "pulls": "2.8M", + "tags_count": 17, + "last_updated": "3 months ago", + "url": "https://ollama.com/library/mistral-nemo" + }, + { + "name": "llama3.3", + "description": "New state of the art 70B model. Llama 3.3 70B offers similar performance compared to the Llama 3.1 405B model", + "capabilities": ["tools"], + "parameter_sizes": ["70b"], + "pulls": "2.6M", + "tags_count": 14, + "last_updated": "10 months ago", + "url": "https://ollama.com/library/llama3.3" + }, + { + "name": "deepseek-v3", + "description": "A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token", + "capabilities": [], + "parameter_sizes": ["671b"], + "pulls": "2.6M", + "tags_count": 5, + "last_updated": "9 months ago", + "url": "https://ollama.com/library/deepseek-v3" + }, + { + "name": "bge-m3", + "description": "BGE-M3 is a new model from BAAI distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity", + "capabilities": ["embedding"], + "parameter_sizes": ["567m"], + "pulls": "2.6M", + "tags_count": 3, + "last_updated": "1 year ago", + "url": "https://ollama.com/library/bge-m3" + }, + { + "name": "mistral-small", + "description": "Mistral Small 3 sets a new benchmark in the small Large Language Models category below 70B", + "capabilities": ["tools"], + "parameter_sizes": ["22b", "24b"], + "pulls": "2.1M", + "tags_count": 21, + "last_updated": "9 months ago", + "url": "https://ollama.com/library/mistral-small" + }, + { + "name": "smollm2", + "description": "SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters", + "capabilities": ["tools"], + "parameter_sizes": ["135m", "360m", "1.7b"], + "pulls": "2M", + "tags_count": 49, + "last_updated": "12 months ago", + "url": "https://ollama.com/library/smollm2" + }, + { + "name": "llava-llama3", + "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": 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"description": "StarCoder2 is the next generation of transparently trained open code LLMs that comes in three sizes: 3B, 7B and 15B parameters", + "capabilities": ["tools"], + "parameter_sizes": ["3b", "7b", "15b"], + "pulls": "1.4M", + "tags_count": 67, + "last_updated": "1 year ago", + "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", + "capabilities": ["tools"], + "parameter_sizes": ["8x7b", "8x22b"], + "pulls": "1.4M", + "tags_count": 70, + "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": [], + "parameter_sizes": ["7b", "70b"], + "pulls": "1.3M", + "tags_count": 34, + "last_updated": "2 years ago", + "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", + "capabilities": ["tools"], + "parameter_sizes": ["2b", "7b"], + "pulls": "1.3M", + "tags_count": 85, + "last_updated": "1 year ago", + "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", + "tags_count": 64, + "last_updated": "1 year ago", + "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"], + "parameter_sizes": ["1b", "3b", "7b", "10b"], + "pulls": "1M", + "tags_count": 17, + "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"], + "parameter_sizes": ["1b", "3b"], + "pulls": "1M", + "tags_count": 33, + "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"], + "parameter_sizes": ["3b", "7b", "32b", "72b"], + "pulls": "991K", + "tags_count": 17, + "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", + "capabilities": ["embedding"], + "parameter_sizes": ["22m", "33m", "110m", "137m", "335m"], + "pulls": "951.1K", + "tags_count": 16, + "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": [], + "parameter_sizes": ["3b", "7b", "13b", "70b"], + "pulls": "930.6K", + "tags_count": 119, + "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", + "tags_count": 11, + "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"], + "parameter_sizes": ["2.7b"], + "pulls": "730.7K", + "tags_count": 18, + "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", + "capabilities": ["vision", "tools"], + "parameter_sizes": ["24b"], + "pulls": "718.3K", + "tags_count": 5, + "last_updated": "4 months ago", + "url": "https://ollama.com/library/mistral-small3.2" + }, + { + "name": "dolphin-mixtral", + "description": "Uncensored, 8x7b and 8x22b fine-tuned models based on the Mixtral mixture of experts models that excels at coding tasks. Created by Eric Hartford", + "capabilities": ["tools"], + "parameter_sizes": ["8x7b", "8x22b"], + "pulls": "716.8K", + "tags_count": 70, + "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"], + "pulls": "670.3K", + "tags_count": 9, + "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", + "capabilities": ["tools", "thinking"], + "parameter_sizes": ["2b", "8b"], + "pulls": "660.7K", + "tags_count": 3, + "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"], + "parameter_sizes": ["3b", "8b", "14b", "32b", "70b"], + "pulls": "659K", + "tags_count": 20, + "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"], + "parameter_sizes": ["14b"], + "pulls": "625.6K", + "tags_count": 9, + "last_updated": "6 months ago", + "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"], + "parameter_sizes": ["7b", "32b"], + "pulls": "604.4K", + "tags_count": 15, + "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", + "tags_count": 5, + "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": [], + "parameter_sizes": ["2.7b"], + "pulls": "569.2K", + "tags_count": 15, + "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. 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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 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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 (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= (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]" + } + } + \ No newline at end of file diff --git a/database/models.json b/database/models.json new file mode 100644 index 0000000..e7f6502 --- /dev/null +++ b/database/models.json @@ -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. 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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 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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": 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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 (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= (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]" + } + } + \ No newline at end of file diff --git a/database/ollahub-preferences.db-shm b/database/ollahub-preferences.db-shm index 5a2fc9efa086cbe0ce0ba4201b80852f8731e63a..3a7629bef3b930c73c7935114ba1850b1e75ee59 100644 GIT binary patch delta 171 zcmZo@U}|V!s+V}A%K!t63=9J7Kn^nyAI@WpfB#W4XtD7^i+HWgb8EPsom;BdQAnzK ynAsq6|04mYI1>Z=#71FOCLou2V`r+<;(t!e%q$FSn;G4HGcmI=uy35mXbu2M+c0SW delta 159 zcmZo@U}|V!s+V}A%K!t63=9IyKn@5mf7#K~*Ev&YvGGESc&*KI&reTzm4EPW6{+fB nW`oT9j|8CNObpBu8-+J6iE^5_L16JeC+3Zd{xEG^z-A5rmaa9r diff --git a/database/ollahub-preferences.db-wal b/database/ollahub-preferences.db-wal index a4222020be8c0ba5e5b1bb0d06b1c284b0813d39..941779b88f472ca2a1cb982c00e91817177f48d6 100644 GIT binary patch delta 414 zcmaEnaN;53h71EG1_lNuAZA!>ywDBsIl1KP5Fs$x5lPJT=cq&(z95&ooI%N2$27xFj_< zpeR4Lpaduj6iG=f%S=uMvJ*>7@`3#Hf>O`Cl++3(D+3*+ywY6vf>I?b!&=5!h8+R- zftE9aFw9j-g5|Yl{APVVTwrH?W#In`bk=KrT`p#6PNEX;^*OJmS%+dgb4ujA$`jL delta 8 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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 []; + } +} + + diff --git a/lib/hardware.ts b/lib/hardware.ts new file mode 100644 index 0000000..e69de29 diff --git a/lib/ollama.ts b/lib/ollama.ts index 9abd31d..ccbb7ea 100644 --- a/lib/ollama.ts +++ b/lib/ollama.ts @@ -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 { export async function searchRemoteModels(query?: string): Promise { 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) {