RWKV-4 3B
Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
Model Description
RWKV-4 3B is a L32-D2560 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
Use https://github.com/BlinkDL/ChatRWKV to run it.
RWKV-4-Pile-3B-20221110-ctx4096.pth (RECOMMENDED) : Fine-tuned to ctx_len 4096.
- LAMBADA ppl 5.25, acc 63.96%
- PIQA acc 74.16%
- SC2016 acc 70.71%
- Hellaswag acc_norm 59.89%
- ctx_len = 4096 n_layer = 32 n_embd = 2560
RWKV-4-Pile-3B-20221008-8023.pth : Trained on the Pile for 331B tokens.
- Pile loss 1.9469
- LAMBADA ppl 5.24, acc 63.94%
- PIQA acc 73.72%
- SC2016 acc 70.28%
- Hellaswag acc_norm 59.63%
- ctx_len = 1024 n_layer = 32 n_embd = 2560
Instruct-test models: only useful if you construct your prompt following dataset templates
Note I am using "Q: instruct\n\nA: result" prompt for all instructs.
RWKV-4-Pile-3B-Instruct-test1 instruct-tuned on https://huggingface.co/datasets/bigscience/xP3all/viewer/en/train
RWKV-4-Pile-3B-Instruct-test2 instruct-tuned on https://huggingface.co/datasets/Muennighoff/flan & NIv2
Chinese models
RWKV-4-Pile-3B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)
RWKV-4-Pile-3B-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)