当前的动态NTK缩放实际上是静态NTK缩放。对于需要处理大量并发请求的模型服务器来说，实现动态NTK可能会...
"Dynamic" Issue in LlamaDynamicNTKScalingRotaryEmbedding - Long context inference will impact short context inference.#25306 Closed Copy link ContributorAuthor NormXUcommentedAug 7, 2023• edited @ganteThe main difference betweenmy implementationsand huggingface's is as follows: ...
Weeks ago,u/emozillaproposed an improvement on NTK-Aware RoPR in thispost, later named DynamicNTKScalingRotaryEmbedding. The main idea behind Dynamic NTK involves incorporating a scaling factor relative to the present decoding sequence length to improve the base functionality. However, there is actua...
if scaling_type == "linear": self.rotary_emb = LlamaLinearScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "dynamic": self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(...
www.nature.com/scientificreports OPEN An extended patch-dynamic framework for food chains in fragmented landscapes received: 23 May 2016 accepted: 19 August 2016 Published: 09 September 2016 Jinbao Liao1, Jiehong Chen1, Zhixia Ying2, David E. Hiebeler3 & Ivan Nijs4 Habitat ...
是否有必要支持dynamic ntk main分支目前好像是不支持dynamic ntk,不过看这个帖子https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/ 从图中看,dynamic ntk能取到综合长短文本的最低ppl。 是否有必要支持dynamic ntk呢?
YaRN (Yet another RoPE extension method) combines the NTK-By-Parts Interpolation and Attention Scaling methods, improving upon existing RoPE interpolation methods for longer context window sizes. Fine-tuned models maintain their original performance across benchmarks while enabling efficient extrapolation an...
This large set of samples requires a high number of simulations, which by extension requires a very high computational time (exponentially scaling up to an unmanageable level). In order to control and reduce this large number, a fixed set of levels for every factor have been considered, ...
Table 1: Language Modeling Assessment: perplexity analysis of various context scaling methods on the PG19, Proof-Pile, and CodeParrot. FocusLLM successfully maintains low perplexity on extremely long sequences.PG19 Proof-Pile CodeParrot Method 4K 16K 32K 100K 4K 16K 32K 100K 4K 16K 32K 100K...
Image Set Cafe Candles FastCars Flag Gallery1 Gallery2 LibrarySide Shop1 Shop2 PeopleWalking Gradient Magnitude Difference NTK S C P 0.007 0.063 0.027 0.007 0.002 0.034 0.010 0.007 0.007 0.003 0.208 0.238 0.177 0.016 0.004 0.474 0.018 0.036 0.048 0.012 0.036 0.027 0.005 0.347 0.115 0.044 0.002 ...