Comments (1)
- Similar to usual GPT of the same size, because we are using parallelization to increase training speed. However, you can definitely train it like a RNN to save VRAM (but that will be much slower).
- More friendly than usual GPT. Because you don't need to keep a huge context (or kv cache). You just need the hidden state of the last single token.
- YES! Inference is very fast even on CPU. Please try run.py
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Related Issues (20)
- lora训练时出错 HOT 3
- 如何训练rwkv-5-0.1b,显示权重加载错误 HOT 1
- 想问一下能否提供一个CHN+JPNTuned的7B版本 HOT 1
- AttributeError: 'MyDataset' object has no attribute 'global_rank' HOT 3
- 出错 No such file or directory: 'cuda/wkv_op.cpp' HOT 1
- 可以给个requirements? HOT 4
- huggingface无法使用 HOT 1
- Got ImportError when using load() to load wkv_cuda HOT 1
- 如何将rwkv或者retnet用于ocr任务? HOT 1
- v5 train error HOT 2
- size mismatch for blocks.11.ffn.value.weight: copying a param with shape HOT 3
- v5 train error HOT 1
- 请教一下,训练RWKV-4-Pile-3B-20221008-8023,提示错误 HOT 3
- how to pretrain v5 other lang? HOT 3
- 训练RWKV-4,报错 HOT 1
- MoE support HOT 1
- 微调IndexError: list index out of range HOT 1
- AssertionError while finetuning RWKVv5 HOT 8
- demo-training-prepare libcudart woes HOT 2
- 'types.SimpleNamespace' object has no attribute 'time_first' HOT 1
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