copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre . . . Specifically, based on the well-known LLaMA-2 7B model, we obtain an MoE model by: (1) Expert Construction, which partitions the parameters of original Feed-Forward Networks (FFNs) into multiple experts; (2) Continual Pre-training, which further trains the transformed MoE model and additional gate networks
GitHub - pjlab-sys4nlp llama-moe: ⛷️ LLaMA-MoE: Building Mixture-of . . . LLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based on LLaMA and SlimPajama We build LLaMA-MoE with the following two steps: Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts
LLaMA-MoE - Hugging Face LLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based on LLaMA and SlimPajama We build LLaMA-MoE with the following two steps: Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts
llama-7b - ModelScope Specifically, we replace the original heavy-weight ViT-H encoder (632M) with a much smaller Tiny-ViT (5M) Running on a single GPU, MobileSAM processes each image in about 12ms: 8ms on the image encoder and 4ms on the mask decoder Image Encoder | Original SAM | MobileSAM
GitHub - OpenSparseLLMs LLaMA-MoE-v2: LLaMA-MoE v2: Exploring . . . LLaMA-MoE-v2 is a series of open-sourced Mixture-of-Expert (MoE) models based on LLaMA3 We build LLaMA-MoE-v2 with the following two steps: Partition LLaMA's FFN layers or Attention layers into sparse experts and insert top-K gate for each layer of experts
LLaMA 7B | Open Laboratory LLaMA 7B is a 7-billion parameter transformer-based language model developed by Meta AI and released in February 2023 Built using architectural improvements including RMSNorm, SwiGLU activation, and rotary positional embeddings, the model was trained on approximately one trillion tokens from publicly available datasets