This adds an option to compute perplexity over the prompt input similar to https://huggingface.co/docs/transformers/perplexity. It does so by chunking up the prompt into non-overlapping chunks of the context window size. It then runs the forward pass and computes the softmax probability of the...
// Example, we have a context window of 512, we will compute perplexity for each of the // last 256 tokens. Then, we split the input up into context window size chunks to // process the entire prompt. for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) { ...
虽然perplexity.ai 中优化的硬件利用率不是直接的测试时间计算策略,但它可以更有效地实施所有策略,尤其是混合方法。 通过将计算分布在运行不同 LLM 的不同硬件类型(CPU、GPU、专用 AI 芯片)上,perplexity.ai 有效地在硬件和模型级别实现了一种混合方法。这允许以最佳方式执行各种测试时间计算策略,可能并行运行不同的...
But this is a complete misinterpretation of scaling laws. What exactly is a “better” model? Scaling laws only quantify the decrease in perplexity, that is, improvement in how well models can predict the next word in a sequence. Of course, perplexity is more or less irrelevant to end users...
Customers like Perplexity AI elastically scale beyond hundreds of GPUs and minimize their downtime with SageMaker HyperPod. Deep-learning inference is another example of how AWS is continuing its cloud infrastructure innovations, including the low-cost, high-performance Amazon EC2 Inf2 instances ...
(e.g., configuring distributed training libraries, scaling training workloads across thousands of accelerators, detecting and repairing faulty instances), speeding up training by as much as 40%. Customers like Perplexity AIelastically scale beyond hundreds of GPUs and minimize their downtimewith ...
The current implementation constructs a list of the entire files content in memory just to (almost) discard it immediately. We found that this leads to high memory usage and can slow down the clust...
This cooldown phase initiates a sharp decrease in loss to match cosine; the training perplexity follows the same behavior. Figure: The cooldown schedule allows to perform scaling law experiments for a fraction of the compute. Instead of having to train from scratch (cosine), we launch one ...
(e.g., configuring distributed training libraries, scaling training workloads across thousands of accelerators, detecting and repairing faulty instances), speeding up training by as much as 40%. Customers like Perplexity AIelastically scale beyond hundreds of GPUs and minimize their downtimewith ...
Language Generation Loss/Perplexity 4 Llama3-8B (touvron2023llama) Image Classification Accuracy 3 ResNet-18 (he2016deep)Table 7: Llama-3 fine-tuned models merging GD IGD GD+IGD Arabic+French 0.0230.010 0.0350.018 0.0580.028 Chinese+Japanese 0.0140.013 0.0280.017 0.0410.026 # evals per ...