
Mastering Data Compression with LLMs via LMCompress
LMCompress uses large language models to achieve state of the art, lossless compression across text,
LMCompress uses large language models to achieve state of the art, lossless compression across text,
AlphaEvolve by DeepMind evolves and optimizes code using LLMs and evolutionary algorithms, enabling breakthroughs in
J1 by Meta AI is a reasoning-focused LLM judge trained with synthetic data and verifiable
Portkey enables observability and tracing in multi-modal, multi-agent systems for enhanced understanding and development.
PydanticAI Agents leverage Pydantic’s validation to build reliable, type-safe AI decision-making systems.
Nexus is a lightweight Python framework for building scalable, reusable LLM-based multi-agent systems.
DRAMA enhances dense retrieval by leveraging LLM-based data augmentation and pruning to create efficient, high-performance
AI co-scientists powered by Gemini 2.0 accelerate scientific discovery by generating and ranking hypotheses using
SWE-Lancer benchmarks AI models on 1,400+ real freelance software engineering tasks worth $1M, evaluating their
Mixture-of-Mamba enhances State Space Models for efficient multi-modal data processing across text, images, and speech.
Step-Video-T2V, a cutting-edge text-to-video model with 30B parameters, enhances video quality using Video-VAE, Video-DPO, and
Nomic Embed Text V2 revolutionizes text embeddings with Mixture-of-Experts (MoE), enhancing efficiency, multilingual support, and
DeepSearch revolutionizes question-answering in LLMs, enhancing precision, completeness, and efficiency in information retrieval.
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