Search Results for "context" - Page 73
Sort By:
Showing 1844 open source projects for "context"
View related business solutions-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
cafetiere
Rule-based information extraction.
UIMA-compliant text analytics using a rule language in which to express context-sensitive constraints on syntactic and semantic text elements. -
2
Malabar is a simple, context-agnostic authentication library for Java applications. It strives to be equally usable in J2EE/Web, GUI, and other applications requiring user authentication.
-
3
Altar-1
Pruned GLM-5.3 model for self-hosted cybersecurity AI and coding
...Its routed experts use INT4 W4A16 AWQ quantization, while attention, shared experts, dense layers, and the output head remain BF16. The resulting weights occupy about 328 GB and are designed for production deployment on four NVIDIA H200 GPUs through vLLM, including 128K-context workloads. -
4
Qwen3.8-27B
Dense 27B multimodal model for coding, agents, and visual reasoning
...Agent capabilities emphasize autonomous planning, environment feedback, computer and browser use, and reliable completion of complex multi-step workflows. Qwen3.8-27B supports a native 262,144-token context window that can be extended to one million tokens. Thinking is enabled by default, with low, medium, and xhigh reasoning-effort settings and preserved reasoning across conversations. It also delivers substantial improvements over Qwen3.6-27B on coding and agent benchmarks while remaining deployment-friendly and compatible with Transformers, vLLM, SGLang, etc. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
5
Muse Glimmer
Local multimodal 30B model for autonomous agents, coding, and tools
...Distilled from the larger Muse Spark, it combines multi-step reasoning, reliable tool use, coding, failure recovery, and image understanding in a dense 29.6B-parameter architecture with a dedicated 1.8B-parameter perception encoder. It supports more than 100 languages and a 131K+ token context window, allowing agents to maintain coherent plans across extended workflows. Muse Glimmer can interpret screenshots, charts, documents, and images alongside text, while configurable reasoning strength lets developers balance response quality and speed. Its quantized variants reduce the model below 20 GB for operation on systems with 24–32 GB of memory, and DFlash speculative decoding can substantially accelerate generation. -
6
Inkling
Frontier multimodal MoE model for coding and AI agent workflows
...It contains 975B total parameters with 41B active parameters per token, balancing frontier-level capability with efficient sparse inference. The model natively processes text, images, audio, and video within a unified architecture and supports an exceptionally large 1 million token context window for long-document reasoning, repository-scale coding, and agentic execution. Trained from scratch on approximately 45 trillion multimodal tokens, Inkling introduces controllable reasoning effort, allowing users to trade off latency and reasoning depth depending on the task. It is optimized for software engineering, tool use, and large-scale autonomous workflows, with strong performance on coding and agent benchmarks. -
7
Hy3
Open code agent for Lean 4 proofs and formal software verification
...Built as part of the Mistral Small 4 family, it combines multimodal capabilities with an efficient Mixture-of-Experts architecture containing 119B total parameters and 6.5B activated per token. The model uses 128 experts with four active for each token and supports a 256K-token context window, making it suitable for extended formal reasoning and large verification tasks. Leanstral accepts text and image inputs and produces text output, enabling multimodal workflows around mathematics, code, and specifications. It supports configurable reasoning effort, allowing users to disable reasoning or enable high-effort reasoning for complex prompts. ... -
8
Leanstral 1.5
Open code agent for Lean 4 proofs and formal software verification
...Built as part of the Mistral Small 4 family, it combines multimodal capabilities with an efficient Mixture-of-Experts architecture containing 119B total parameters and 6.5B activated per token. The model uses 128 experts with four active for each token and supports a 256K-token context window, making it suitable for extended formal reasoning and large verification tasks. Leanstral accepts text and image inputs and produces text output, enabling multimodal workflows around mathematics, code, and specifications. It supports configurable reasoning effort, allowing users to disable reasoning or enable high-effort reasoning for complex prompts. ... -
9
Laguna XS.2
Open agentic coding model optimized for local deployment
...Laguna XS.2 supports native reasoning with interleaved thinking between tool calls, enabling more capable autonomous coding agents and multi-step workflows. The model features a 262K-token context window, preserved reasoning across interactions, FP8 KV-cache optimization, and compatibility with local deployment ecosystems such as Ollama and vLLM. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
Gemma 4
Google’s flagship dense multimodal model for coding and reasoning
...The model contains approximately 30.7B parameters and supports text and image inputs with text generation output, while also processing video as image-frame sequences. Built as the most capable model in the Gemma 4 family, it combines strong reasoning performance with a large 256K-token context window and configurable thinking modes. Gemma 4 31B supports native function calling, structured outputs, and more than 140 languages, making it suitable for enterprise assistants, coding agents, document analysis, and multilingual applications. Google positions it as a frontier-level model that can run on consumer GPUs and workstations while achieving leading results across reasoning, mathematics, coding, and multimodal benchmarks. -
11
Command A+
4-bit Command A+ model for enterprise agents and multilingual tasks
...Cohere recommends W4A4 for most users because it offers a smaller hardware footprint with negligible benchmark differences compared to BF16 and FP8 versions. The model supports a 128K input context and 64K output length, covers 48 languages, and includes conversational tool-use capabilities with JSON-schema tools and optional citation grounding. -
12
DeepSeek-V4-Pro
Flagship MoE model for advanced reasoning, coding, and agents
...It features approximately 1.6 trillion total parameters with around 49B activated during inference, enabling strong efficiency while maintaining frontier-level capability. The model supports an ultra-long context window of up to 1 million tokens, making it highly suitable for long-document reasoning, large codebases, and complex multi-step tasks. Architecturally, it introduces optimizations to reduce compute and memory costs while improving stability across long sequences. DeepSeek-V4-Pro is positioned as the high-end variant of the V4 family, outperforming most open-source models in areas such as agentic coding, STEM reasoning, and world knowledge, and approaching the performance of leading closed-source systems. ... -
13
Devstral 2
Agentic 123B coding model optimized for large-scale engineering
...It generalizes well across diverse prompts, languages, and development environments, making it adaptable to a wide range of coding workflows. Devstral 2 supports a 256k context window, enabling deep understanding of large repositories, long diffs, and extended technical discussions. It integrates seamlessly with agent frameworks and developer tools, including the Mistral Vibe CLI, enabling terminal-based automation and interactive development workflows. -
14
DeepSeek-V3.2-Speciale
High-compute ultra-reasoning model surpassing model surpassing GPT-5
DeepSeek-V3.2-Speciale is the high-compute, ultra-reasoning variant of DeepSeek-V3.2, designed specifically to push the boundaries of mathematical, logical, and algorithmic intelligence. It builds on the DeepSeek Sparse Attention (DSA) framework, delivering dramatically improved long-context efficiency while preserving full model quality. Unlike the standard version, Speciale is tuned exclusively for deep reasoning and therefore does not support tool-calling, focusing its full capacity on pure cognitive performance. The model uses a scaled reinforcement learning framework that allows it to surpass GPT-5 in several evaluations and reach reasoning performance comparable to Gemini-3.0-Pro. ... -
15
DeepSeek-V3.2
High-efficiency reasoning and agentic intelligence model
DeepSeek-V3.2 is a cutting-edge large language model developed by DeepSeek-AI, focused on achieving high reasoning accuracy and computational efficiency for agentic tasks. It introduces DeepSeek Sparse Attention (DSA), a new attention mechanism that dramatically reduces computational overhead while maintaining strong long-context performance. Built with a scalable reinforcement learning framework, it reaches near-GPT-5 levels of reasoning and outperforms comparable models like DeepSeek-V3.1 and Gemini-3.0-Pro in advanced benchmarks. The model was notably used in competitive AI challenges such as the 2025 International Mathematical Olympiad (IMO) and IOI, achieving top-tier results. ... -
16
Mellum-4b-base
JetBrains’ 4B parameter code model for completions
...Built with 4 billion parameters and a LLaMA-style architecture, it was trained on over 4.2 trillion tokens across multiple programming languages, including datasets such as The Stack, StarCoder, and CommitPack. With a context window of 8,192 tokens, it excels at code completion, fill-in-the-middle tasks, and intelligent code suggestions for professional developer tools and IDEs. The model is efficient for both cloud inference with vLLM and local deployment using llama.cpp or Ollama, thanks to its bf16 precision and AMP training. While the base model is not fine-tuned for downstream tasks, it is designed to be easily adapted through supervised fine-tuning (SFT) or reinforcement learning (RL). ... -
17
This project is a collection of three different programs, regarding: 1) a program to include context-sensitive ranking when querying on a database, 2) a program for estimating relative importance in networks. 3)Search of services in a sensor network
-
18
Ontology-based user context manager supporting uncertainty and handling of histories based on a database implementation
-
19
MiMo-V2.6-Flash
Efficient 309B omnimodal MoE for coding, agents, vision, and audio
...Its sparse Mixture-of-Experts architecture contains 309B total parameters while activating only 15B per token, using 256 routed experts with eight active per token. The model natively processes text, images, video, and audio and supports a 1M-token context window for large repositories, extended tool traces, and multi-session agent workflows. Training uses a unified mixed RL process rather than separate domain-specific runs, alongside asynchronous GRPO and groupwise agentic grading that rewards higher-quality and more efficient solutions. Its architecture combines sliding-window and global attention with a 681M-parameter vision encoder and dedicated audio encoders. ... -
20
MiMo-V2.6-Pro
1T omnimodal MoE model for coding, agents, and long-horizon reasoning
...Its sparse Mixture-of-Experts architecture contains 1.02T total parameters with 42B activated per token, using 384 routed experts with eight active per token. The model natively processes text, images, video, and audio and supports a 1M-token context window for large repositories, extended tool traces, and multi-session agent workflows. MiMo-V2.6-Pro-RL uses a unified mixed reinforcement learning process rather than separate domain-specific runs, alongside groupwise agentic grading that rewards higher-quality and more efficient solutions. Its architecture combines sliding-window and global attention, a 681M-parameter vision encoder, dedicated audio encoders, and a five-layer multi-token speculative decoder. ... -
21
Smaug Flash
304B MoE model optimized for agentic coding and long-running workflows
...Its attention system combines Multi-Head Latent Attention with a sparse token indexer, while DSpark multi-token prediction provides speculative decoding for faster generation. Smaug-Flash preserves the base model’s 1,048,576-token context window and low, high, and max reasoning-effort settings. Training specifically targets common agent failures such as looping, stalling, and inefficient task completion, producing substantial improvements across agentic coding benchmarks. The model uses block-FP8 attention and packed-FP4 experts and supports deployment through vLLM and SGLang while retaining compatibility with DeepSeek-V4-Flash. -
22
Ministral 3 3B Base 2512
Small 3B-base multimodal model ideal for custom AI on edge hardware
...The model is fully optimized for edge deployment and can run locally on a single GPU, fitting in 16GB VRAM in BF16 or less than 8GB when quantized. It supports dozens of languages, making it practical for multilingual, global, or distributed environments. With a large 256k token context window, it can handle long documents, extended inputs, or multi-step processing workflows even at its small size. -
23
Ministral 3 8B Reasoning 2512
Efficient 8B multimodal model tuned for advanced reasoning tasks.
...It supports dozens of languages, adheres reliably to system prompts, and provides native function calling and structured JSON output—key capabilities for agentic and automation workflows. The model also includes a 256k context window, allowing it to handle long documents and extended reasoning chains. -
24
Ministral 3 14B Reasoning 2512
High-precision 14B multimodal model built for advanced reasoning tasks
...It maintains robust system-prompt adherence, supports dozens of languages, and provides native function calling with clean JSON output for agentic workflows. The model's architecture also delivers a 256k context window, unlocking large-document analysis and long-form reasoning. -
25
The complete portal solution for college from both student and operational management perspective. portal should give custom user experience depending on role and context. If you are looking for similar work, Please let us know your views using forum.
