Search Results for "document tracking system"
Sort By:
Showing 14 open source projects for "document tracking system"
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.
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
1
DeepSeek-OCR 2
Visual Causal Flow
DeepSeek-OCR-2 is the second-generation optical character recognition system developed to improve document understanding by introducing a “visual causal flow” mechanism, enabling the encoder to reorder visual tokens in a way that better reflects semantic structure rather than strict raster scan order. It is designed to handle complex layouts and noisy documents by giving the model causal reasoning capabilities that mimic human visual scanning behavior, enhancing OCR performance on documents with rich spatial structure. ... -
2
J-Space Cognition Suite V3.6
AI cognitive-enhancement Skills based on Anthropic's J-space
J-Space Cognition Suite is a model-agnostic inference-time control system for improving deep reasoning, long-horizon work, tool use, verification, and recovery in AI agents. It is distributed as a cross-platform Skill that leaves model weights and training unchanged. The suite manages an agent's accessible working representations through selective loading instead of applying every mechanism to every task. Its fast, full, and loop modes scale from simple checks to multi-stage work requiring... -
3
MoveNet
A CNN model that predicts human joints from RGB images of a person
The MoveNet model is an efficient, real-time human pose estimation system designed for detecting and tracking keypoints of human bodies. It utilizes deep learning to accurately locate 17 key points across the body, providing precise tracking even with fast movements. Optimized for mobile and embedded devices, MoveNet can be integrated into applications for fitness tracking, augmented reality, and interactive systems. -
4
MuJoCo MPC
Real-time behaviour synthesis with MuJoCo, using Predictive Control
...MJPC integrates a high-performance GUI and multiple predictive control algorithms, including iLQG, gradient descent, and Predictive Sampling — a competitive, derivative-free method that achieves robust real-time control. The system supports multi-shooting optimization, enabling precise motion planning across diverse domains like quadruped locomotion, humanoid tracking, and dexterous manipulation. In addition to its C++ core, MJPC includes an experimental Python API, enabling integration with custom models and MuJoCo tasks for flexible scripting and experimentation. -
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.
-
5
Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
...It is trained from scratch and built using a hybrid architecture that integrates Transformer attention layers with Mamba-style sequence modeling components inside a Mixture-of-Experts framework. This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following. -
6
Ministral 3 14B Reasoning 2512
High-precision 14B multimodal model built for advanced reasoning tasks
...This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. Despite its scale, the model is engineered for practical deployment and can run locally on 32GB of VRAM in BF16 or under 24GB when quantized. 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. -
7
Mistral Large 3 675B Instruct 2512
Frontier-scale 675B multimodal instruct MoE model for enterprise AIMis
...It incorporates a massive 673B-parameter language MoE backbone and a 2.5B-parameter vision encoder, enabling rich multimodal understanding across text and images. The model supports dozens of languages and maintains strong system-prompt adherence, making it suitable for global and structured enterprise use. Designed for high performance, it runs on a single node of B200 or H200 GPUs in FP8, and can also operate in NVFP4 mode on H100 or A100 hardware. With a 256k context window, it excels at long-document comprehension, deep retrieval workflows, and complex knowledge-intensive tasks. -
8
MiniMax-M2.7
Self-evolving AI model for agents, coding, and complex workflows
...This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30% improvement in programming scaffolds. M2.7 excels in real-world engineering scenarios, including debugging, log analysis, system monitoring, and root cause investigation, demonstrating strong system-level reasoning comparable to SRE workflows. It also supports multi-agent collaboration through Agent Teams, allowing coordinated problem-solving across roles. Beyond engineering, it handles structured document editing (Word, Excel, PowerPoint) with high fidelity and maintains strong performance. -
9
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... -
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.
-
10
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,... -
11
Ministral 3 8B Reasoning 2512
Efficient 8B multimodal model tuned for advanced reasoning tasks.
Ministral 3 8B Reasoning 2512 is a balanced midsize model in the Ministral 3 family, delivering strong multimodal reasoning capabilities within an efficient footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling it to process both text and images for advanced reasoning tasks. This version is specifically post-trained for reasoning, making it well-suited for math, coding, and STEM applications requiring multi-step logic and problem-solving. Despite its... -
12
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... -
13
Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require... -
14
Mistral Large 3 675B Instruct 2512 NVFP4
Quantized 675B multimodal instruct model optimized for NVFP4
Mistral Large 3 675B Instruct 2512 NVFP4 is a frontier-scale multimodal Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. This NVFP4 checkpoint is a post-training-activation quantized version of the original instruct model, created through a collaboration between Mistral AI, vLLM, and Red Hat using llm-compressor. It retains the same instruction-tuned behavior as the FP8 model, making it ideal for production...
- Previous
- You're on page 1
- Next
