A Culturally-diverse Multilingual Multimodal Video Benchmark & Model
Paper • 2506.07032 • Published
Fabian Farestam
northern-64bit
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A Culturally-diverse Multilingual Multimodal Video Benchmark & Model authored a paper 11 months ago
Kaleidoscope: In-language Exams for Massively Multilingual Vision
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All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Paper • 2411.16508 • Published • 10
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
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Unattainability of Common Knowledge in Asymmetric Games with Imperfect Information
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits (2402.17764)
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits (2402.17764)
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.
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