LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. LLMLingu...
所以Llama 3 Mask了formatting tokens 的 loss,实验发现这些token如果算loss,可能会导致tail repetition和突然生成终止的token。 (b)第二个细节是,Llama 3给chosen sequence加上了一个negative log-likelihood(NLL) loss,从NLL loss和标准交叉熵损失的差别上看,可以简单把NLL loss理解为SFT loss: \text{nll_loss}(...
所以Llama 3 Mask了formatting tokens 的 loss,实验发现这些token如果算loss,可能会导致tail repetition和突然生成终止的token。 (b)第二个细节是,Llama 3给chosen sequence加上了一个negative log-likelihood(NLL) loss,从NLL loss和标准交叉熵损失的差别上看,可以简单把NLL loss理解为SFT loss: 加上NLL loss的好...
QuantizationMMLUCEval (val)GSM8KHumaneval Qwen-1.8B-Chat (BF16)43.355.633.726.2 Qwen-1.8B-...
performance on academic benchmarks with well-established evaluation setups. We have also shown that NF4 is more effective than FP4 and that double quantization does not degrade performance. Combined, this forms compelling evidence that 4-bit QLORA tuning reliably yields results matching 16-bit ...
TELL先给任务数据加上anchor prompt，注入一致的语义特征，应对第一个困难。然后基于quantization hypothesis...
Large Language Models (LLMs) have advanced rapidly but face significant memory demands. While quantization has shown promise for LLMs, current methods typically require lengthy training to alleviate the performance degradation from quantization loss. However, deploying LLMs across diverse scenarios with ...
如下图 Figure 1 所示,在 [2405.14428] Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMs [3] 中作者发现,各种 GLU 变体的激活函数容易在特定层(比如基于 SwiGLU 激活的 FFN 的最后一个 Liner 层的输入)出现激活的 Spike。此外,作者发现这些激活的 Spike 与中间层隐藏状态(Hidden St...
performance and accuracy, different low-precision solutions—such as SmoothQuant and weight-only-quantization—are also enabled, which allows the extension to support datatypes that include FP32, BF16, SmoothQuant for int8, and weight-only quantization for int8 and int4 (experimental). Typ...
Regularly validate your model's performance to ensure accuracy is maintained as you test lower precisions quantization recipes. Use pruning techniques to eliminate redundant weights, reducing the computational load. Consider model distillation to create a smaller, faster model that approximate...