Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding https://
arxiv.org/abs/2309.08168
Jun Zhang
Jue Wang
Huan Li
Lidan Shou
Ke Chen
Gang Chen
Sharad Mehrotra
@cohere
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Draft & Verify: Lossless LLM Acceleration via Self-Speculative Decoding
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Headless Language Models: Learning Without Predicting
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Headless Language Models: Learning without Predicting with Contrastive Weight Tying https://
arxiv.org/abs/2309.08351 @nthngdy Éric de la Clergerie @bensagot -
Data Pruning Strategies for Large Language Model Pretraining
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When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale https://
cohere.com/research/paper
s/when-less-is-more-investigating-data-pruning-for-pretraining-llms-at-scale-2023-09-08
… @maxdoesresearch @ahmetustun89 @luizapzbn @W4ngatang @mziizm @sarahookr -
Mixture of Experts Parameter Efficiency for Instruction Tuning
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Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning https://
cohere.com/research/paper
s/pushing-mixture-of-experts-to-the-limit-extremely-parameter-efficient-moe-for-instruction-tuning-2023-09-11
… @tedzadouri Ahmet Üstün @aahmadian_ @beyzaermis @acyr_l @sarahookr -
Software Portability Myth and Machine Learning Progress Implications
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The Grand Illusion: The Myth of Software Portability and Implications for ML Progress https://
cohere.com/research/paper
s/the-grand-illusion-the-myth-of-software-portability-and-implications-for-ml-progress-2023-09-12
… @fraser_mince @dzungdinhh @jonas_kg @ProfNeilT @sarahookr -
Top NLP Research Papers September 2023 Curated by Cohere
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Stay up-to-date with the latest research in NLP! Here are some of the top recent papers curated by the Cohere for AI community! https://
txt.cohere.com/top-nlp-papers
-september-2023/
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Featuring the following papers: (thread) -
Cohere Releases Embed v3 Advanced Text Embedding Model
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We just released Embed v3, our latest and most advanced text embedding model. Embed v3 delivers:
– SOTA performance on trusted benchmarks like MTEB and BEIR
– Robustness to noisy datasets
– Compressed embeddings to save on storage costs Read more here: http://
txt.cohere.com/introducing-em
bed-v3
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Building LLM Chatbots with Cohere Chat Endpoint
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Building LLM chatbots can feel overwhelming, but it doesn’t have to be. The Chat endpoint provides a simple API to build LLM chatbots. In this LLM University chapter, learn how to build a chatbot with the Chat endpoint. https://
txt.cohere.com/chatbot-chat-e
ndpoint/
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Chain-of-Thought Prompting: Guide to Better AI Responses
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Struggling to get accurate responses from AI? Chain-of-thought prompting can help.
— Cohere (@cohere) 31 octobre 2023
This method provides step-by-step context to guide the AI's logic. NLP expert @MeorAmer1 explains how it works. Share your best tips for improving AI interactions! pic.twitter.com/fDmzFNK6JUStruggling to get accurate responses from AI? Chain-of-thought prompting can help. This method provides step-by-step context to guide the AI's logic. NLP expert @MeorAmer1 explains how it works. Share your best tips for improving AI interactions!
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Building RAG-Powered Chatbots with LLM Chat Endpoints
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What can you build with LLM chatbots powered by retrieval-augmented generation (RAG)? The Chat endpoint makes it easy to build RAG-powered chatbots. In this LLM University chapter, learn the foundations of LLM chatbots and the Chat endpoint. https://
txt.cohere.com/exploring-chat
-rag/
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