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Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval
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aamirshakir, tomaarsen, SeanLee97
• • 134Prabhat Kumar Gupta
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LLM, AI Agents, NLP, Visual LM
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SetFit: Efficient Few-Shot Learning Without Prompts
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Unso, lewtun, luketheduke, danielkorat, orenpereg, moshew
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Mixture of Experts (MoEs) in Transformers
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ariG23498, pcuenq, merve, IlyasMoutawwakil, ArthurZ, sergiopaniego, Molbap
• • 160 commented on Assisted Generation: a new direction toward low-latency text generation 3 months ago
Great Blog. The accuracy is totally dependent on few factors like use of Greedy decoding and high quality of Assistant models.
Also the latency gains is significant with smaller assistant models which creates a tradeoff between its accuracy vs speed
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Assisted Generation: a new direction toward low-latency text generation
joaogante
• • 78 Article
We Got Claude to Build CUDA Kernels and teach open models!
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burtenshaw, evalstate, merve, pcuenq
• • 156 updated a
dataset 6 months ago
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dataset 6 months ago
commented on Supercharge your OCR Pipelines with Open Models 7 months ago
Superb insights and breaking down the benchmarks. I will be using the datasets here to do the evaluations.