RWKV-4 1.5B
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 1.5B is a L24-D2048 causal language model trained on the Pile. See
https://github.com/BlinkDL/RWKV-LM for details.
ctx_len = 1024
n_layer = 24
n_embd = 2048
RWKV-4-Pile-1B5-20220929-ctx4096.pth : Fine-tuned to ctx_len 4096.
- Likely better when ctxlen > 100. Please test.
RWKV-4-Pile-1B5-20220903-8040.pth : Trained on the Pile for 332B tokens.
- Pile loss 2.0415
- LAMBADA ppl 7.04, acc 56.43%
- PIQA acc 72.36%
- SC2016 acc 68.73%
- Hellaswag acc_norm 52.48%
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-1B5-Instruct-test2-20230209.pth
instruct-tuned on
https://huggingface.co/datasets/Muennighoff/flan & NIv2
Chinese models
RWKV-4-Pile-1B5-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)
RWKV-4-Pile-1B5-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)
Note: 4 / 4a / 4b models ARE NOT compatible. Use RWKV-4 unless you know what you are doing.
RWKV-4b-Pile-1B5-20230217-7954.pth (--my_testing 'a') with tiny amt of QKV attention to improve performance
- Pile loss 1.9947
- LAMBADA ppl 5.82, acc 62.35%
- PIQA acc 72.52%
- SC2016 acc 68.89%
- Hellaswag acc_norm 54.32%
