Semanlink - Favoris ; Artificial Intelligence AND Quantization

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AI on consumer hardware - Apple M1/M2 - BERT - Cohere.ai - Continued pre-training - Frugal LLMs - Jeremy Howard - LLaMA - Money - Nils Reimers - Parameter-efficient fine-tuning (PEFT) - Quantization - RAG (Retrieval-Augmented Generation) - Task-specific models - Text Embeddings - Twitter thread - Vector database -

Favoris ; Artificial Intelligence AND Quantization

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QLoRA

7 Documents

Jeremy Howard sur X : "FSDP/QDoRA with Llama3 : I believe that this combination is likely to create better task-specific models than anything else at any cost."

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2024-04-23 About

LlamaIndex sur X : "Save Memory (and Money) in RAG pipeline with @Cohere 's Int8 and Binary Embeddings..."

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2024-03-30 About

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

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2024-03-23 About

Cohere int8 & binary Embeddings - Scale Your Vector Database to Large Datasets

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2024-03-19 About

Answer.AI - You can now train a 70b language model at home

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2024-03-09 About

ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++

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2023-06-04 About

skeskinen/bert.cpp: ggml implementation of BERT

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2023-05-09 About