How Does Quantization Affect Multilingual LLMs? | Cohere | Research | TechiSeek

AddAdd your Listings

Research

How Does Quantization Affect Multilingual LLMs?

Quantization techniques are widely used to improve inference speed and deployment of large language models.

Research Cohere

ResearchUnderstanding and Mitigating Language Confusion in LLMsCohere ResearchContrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashionCohere ResearchBigScience: A Case Study in the Social Construction of a Multilingual Large Language ModelCohere ResearchAya Vision: Multilingual Multimodal AI AdvancementsCohere ResearchSEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian LanguagesCohere ResearchOne Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual TokenizersCohere BlogsInside the Go Playground - The Go Programming LanguageGo BlogsHow Ramp automated receipt processing with fine-tuned LLMsModal BlogsCSS Hell | CSS-TricksCss Tricks BlogsHow AI Assistants Can Decode GitHub Repos for UI WritersDocker BlogsHow Walsall Council Delivers Improved Resident Experiences Through Multilingual AI Videos in Over 50Synthesia BlogsAI-generated Awareness Campaigns: Global ImpactHeygen BlogsHow to structure documentation for both AI and human readersMintlify BlogsSuperGLUE: Understanding a Sticky Benchmark for LLMsDeepgram LearnFlux Multilingual Technical Deep Dive: Multilingual Speech-to-Text Without the Routing MessDeepgram BlogsChatGPT: Putting the “AI” in “Plagiarism” worldwide?Deepgram BlogsHow Cision uses AI video to scale multilingual support and improve client experienceSynthesia BlogsWhat is RetoolGPT? How we built an internal AI assistantRetool BlogsJust Enough SQL to Safely Use AI for Data AnalysisMotherduck BlogsCerebras 2024 Predictions for Generative AI, LLMs, and HPCCerebras