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Showing 785 open source projects for "context"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Kimi K2.6
Multimodal agent model for coding, orchestration, and autonomy
...One of its most distinctive capabilities is horizontal agent scaling, supporting up to 300 sub-agents and 4,000 coordinated steps in a single run, which enables parallel task decomposition and end-to-end completion of outputs such as documents, websites, and spreadsheets. Architecturally, it uses a 1T-parameter Mixture-of-Experts design with 32B activated parameters, a MoonViT vision encoder, and a 256K context window. -
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Qwen2.5-VL-7B-Instruct
Multimodal 7B model for image, video, and text understanding tasks
Qwen2.5-VL-7B-Instruct is a multimodal vision-language model developed by the Qwen team, designed to handle text, images, and long videos with high precision. Fine-tuned from Qwen2.5-VL, this 7-billion-parameter model can interpret visual content such as charts, documents, and user interfaces, as well as recognize common objects. It supports complex tasks like visual question answering, localization with bounding boxes, and structured output generation from documents. The model is also... -
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Ministral 3 8B Instruct 2512
Compact 8B multimodal instruct model optimized for edge deployment
Ministral 3 8B Instruct 2512 is a balanced, efficient model in the Ministral 3 family, offering strong multimodal capabilities within a compact footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment,... -
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Llama-3.2-1B-Instruct
Instruction-tuned 1.2B LLM for multilingual text generation by Meta
Llama-3.2-1B-Instruct is Meta’s multilingual, instruction-tuned large language model with 1.24 billion parameters, optimized for dialogue, summarization, and retrieval tasks. It builds upon the Llama 3.1 architecture and incorporates fine-tuning techniques like SFT, DPO, and quantization-aware training for improved alignment, efficiency, and safety. The model supports eight primary languages (including English, Spanish, Hindi, and Thai) and was trained on a curated mix of publicly available... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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granite-timeseries-ttm-r2
Tiny pre-trained IBM model for multivariate time series forecasting
...It can integrate exogenous variables, static categorical features, and perform channel-mixing for richer multivariate forecasting. The get_model() utility makes it easy to auto-select the best TTM model for specific context and prediction lengths. These models significantly outperform benchmarks like Chronos, GPT4TS, and Moirai while demanding a fraction of the compute. -
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Laguna M.1
Flagship Poolside model for agentic coding and software engineering
...Laguna M.1 was designed to compete with leading frontier coding models on benchmarks such as SWE-Bench, Terminal-Bench, and other agentic engineering evaluations. It supports reasoning, tool calling, and long-context workflows, making it suitable for autonomous coding agents, software maintenance, debugging, and large-scale development projects. -
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Mistral Large 3 675B Instruct 2512 Eagle
Speculative-decoding accelerator for the 675B Mistral Large 3
...Built on the same frontier-scale multimodal Mixture-of-Experts architecture, it complements a system featuring 41B active parameters and a 2.5B-parameter vision encoder. The Eagle variant is specialized rather than standalone, serving as a performance accelerator for production-grade assistants, agentic workflows, long-context applications, and retrieval-augmented reasoning pipelines. It supports the same multilingual, system-prompt-aligned, and function-calling behavior as the main instruct model when used in the recommended server-client configuration. -
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Ministral 3 3B Instruct 2512
Ultra-efficient 3B multimodal instruct model built for edge deployment
Ministral 3 3B Instruct 2512 is the smallest model in the Ministral 3 family, offering a lightweight yet capable multimodal architecture designed for edge and low-resource deployments. It includes a 3.4B-parameter language model paired with a 0.4B vision encoder, enabling it to understand both text and visual inputs. As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its... -
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Ministral 3 14B Instruct 2512
Efficient 14B multimodal instruct model with edge deployment and FP8
Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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VaultGemma
VaultGemma: 1B DP-trained Gemma variant for private NLP tasks
VaultGemma is a sub-1B parameter variant of Google’s Gemma family that is pre-trained from scratch with Differential Privacy (DP), providing mathematically backed guarantees that its outputs do not reveal information about any single training example. Using DP-SGD with a privacy budget across a large English-language corpus (web documents, code, mathematics), it prioritizes privacy over raw utility. The model follows a Gemma-2–style architecture, outputs text from up to 1,024 input tokens,...
