RWKV-LM-main:之前23年2月份的版本,进去后可以直接训练 cd /RWKV-LM-main/RWKV-v4neo 5、训练: 预留了一个知乎的txt文本,比较乱,用来做测试使用,不用的话可以自己删掉 测试命令: python3 train.py --load_model "" --wandb "" --proj_dir "out" --data_file "/home/RWKV-LM/RWKV-v4neo/知乎...
21 changes: 21 additions & 0 deletions 21 RWKV-v4neo/cuda/wkv_op.cpp Original file line numberDiff line numberDiff line change @@ -0,0 +1,21 @@ #include <torch/extension.h> void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y); ...
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free
RWKV is a RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free s
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free