Search Results for "parallel language"
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Showing 4 open source projects for "parallel language"
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Empower Your Contact Center with Human-Like AI ConversationsEnterprise Bot, based in Switzerland, is a pioneer in Conversational AI, Process Automation, and Generative AI. With the trust of esteemed enterprise giants across industries like Generali, SIX, SBB, DHL, and SWICA, Enterprise Bot is revolutionizing both customer and employee experiences. Through its advanced integration with Large Language Models (LLM) such as ChatGPT and Llama 2, and its unique patent-pending DocBrain technology, the company delivers unparalleled personalization, active engagement, and omnichannel solutions across platforms like email, voice, and chat. Furthermore, Enterprise Bot integrates with existing core systems, such as SAP, CRMs, Confluence and more, and with its proprietary middleware, Blitzico, enables the AI to not only respond to queries but also take action to resolve them. This dedication to innovation in four main use case areas, Customer Support, Sales and Marketing, Knowledge Management and Digital Coworker, elevates both CX and employee productivity.
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1
vLLM
A high-throughput and memory-efficient inference and serving engine
vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more. -
2
Petals
Run 100B+ language models at home, BitTorrent-style
Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch. You can also host BLOOMZ, a version of BLOOM fine-tuned to follow human instructions in the zero-shot regime — just replace bloom-petals with bloomz-petals. -
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Medusa
Framework for Accelerating LLM Generation with Multiple Decoding Heads
Medusa is a framework aimed at accelerating the generation capabilities of Large Language Models (LLMs) by employing multiple decoding heads. This approach allows for parallel processing during text generation, significantly enhancing throughput and reducing response times. Medusa is designed to be simple to implement and integrates with existing LLM infrastructures, making it a practical solution for scaling LLM applications. -
4
GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we... -
Ango Hub | All-in-one data labeling platformAI-Assisted features of the Ango Hub will automate your AI data workflows to improve data labeling efficiency and model RLHF, all while allowing domain experts to focus on providing high-quality data.
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