language:
- en
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- the_pile
RWKV-4 7B
Model Description
RWKV-4 7B is a L32-D4096 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.
ctx_len = 1024 n_layer = 32 n_embd = 4096
RWKV-4-Pile-7B-20230109-ctx4096.pth : Fine-tuned to ctx_len 4096.
- Likely the best. Please test.
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"Raven": RWKV 7B alpaca-style model: https://huggingface.co/BlinkDL/rwkv-4-pile-7b/blob/main/RWKV-4-Pile-7B-Instruct-test5-20230329-ctx4096.pth
This is a strong chat model too. It's recommended to use +i for "Alpaca Instruct" in latest ChatRWKV v2. Examples:
+i Explain the following metaphor: "Life is like cats".
+i write a python function to read data from an excel file.
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RWKV-4-Pile-7B-20230xxx-ctx8192-testxxx : Fine-tuned to ctx_len 8192.
- Slightly weaker than ctx4096 model when ctxlen < 3k.
RWKV-4-Pile-7B-20221115-8047.pth : Trained on the Pile for 332B tokens.
- Pile loss 1.8415T
- LAMBADA ppl 4.38, acc 67.18%
- PIQA acc 76.06%
- SC2016 acc 73.44%
- Hellaswag acc_norm 65.51%
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-7B-Instruct-test1 instruct-tuned on https://huggingface.co/datasets/bigscience/xP3all/viewer/en/train
RWKV-4-Pile-7B-Instruct-test2 instruct-tuned on https://huggingface.co/datasets/Muennighoff/flan & NIv2
Chinese models
RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)
RWKV-4-Pile-7B-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)
RWKV-4-Pile-7B-EngChn-test5 is tuned on more ChatGPT-like data and it's pretty decent. Try "+i 开题报告" "+i 世界各国美食" in latest ChatRWKV v2.