SlashLabs is at the forefront of artificial intelligence research and benchmarking. Founded by a team of ex Redhat|IBM|AWS and Meta engineers, we're dedicated to advancing the field through rigorous experimentation, innovative methodologies, and transparent benchmarks. Our mission is to push the boundaries of what's possible in AI while ensuring that advancements are measured accurately, compared fairly, and deployed responsibly.

- State-of-the-art model development - Performance optimization - Cross-model benchmarking - Bias detection and mitigation - Resource-efficient AI deployment - Open-source tools and frameworks

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# Research Highlights ## Metadata-Augmented Embeddings The technique of enhancing LLM embeddings with metadata is commonly referred to as "Metadata-Augmented Embeddings" or "Hybrid Embeddings." In this approach, traditional vector embeddings generated by large language models are enriched with structured metadata to improve retrieval and relevance. The metadata typically includes information like document creation date, author, categories, tags, or any other relevant contextual information that might not be captured in the semantic vector representation alone. ## Enhanced Contextual Embedding This technique is particularly valuable in retrieval-augmented generation (RAG) systems, where it allows for more precise document retrieval by filtering and ranking results based on both semantic similarity and metadata attributes. Some implementations refer to this as "Metadata-Enhanced Retrieval" or "Structured Augmented Embeddings" depending on the specific implementation details and the retrieval system being used. ## Reference Architectures Scaleable reference architectures for deploying embedding and retrieval systems in both on-premises environments and major cloud platforms. These reference implementations include infrastructure-as-code templates, configuration guides, and performance optimization recommendations tailored to different deployment scenarios and scaling requirements.

# DABAR Framework Distributed AI Benchmarking And Reporting (DABAR) is our decentralized Python framework designed to evaluate AI systems' performance across multiple dimensions. By leveraging a distributed testing architecture and comprehensive reporting tools, DABAR provides an extensive platform for benchmarking AI models in varied computational environments.

Available Benchmark Engines: - MMLU (Massive Multitask Language Understanding) - MT-Bench - ARC (AI2 Reasoning Challenge) - HellaSwag - TruthfulQA - WinoGrande - GSM8K (Grade School Math 8K)

Core Components: 1. Distributed Testing Architecture - Decentralized Testing Nodes - Parallel Execution - Coordinated Starts - Secret Store for API keys and tokens 2. Environmental Reporting - System Utilization Monitoring (GPU, CPU, RAM, VRAM) - Machine Health Checks - Resource Utilization Analysis - Host OS Discovery - Hardware Discovery 3. Comprehensive Benchmarking - Pluggable Benchmark Engine Support - Standardized Evaluation Protocols - Configurable via YAML

Visit our GitHub repositories at https://github.com/slashlabs-ai

"expertise": "Distributed Systems Architecture", "expertise": "AI Benchmarking and Fine Tuning", "expertise": "Cloud Deplpoyment and Architecture ",

SlashLabs is always looking for exceptional researchers, engineers, and thought leaders to join our team. We value creativity, rigor, and a commitment to advancing AI capabilities responsibly. Current openings: - Research Scientist (Reasoning) - ML Engineer (Systems)

Contact Us: [email protected] - Job applications and career opportunities and inquries Location: SlashLabs Research Center San Francisco, CA 94107