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--- |
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language: |
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- en |
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tags: |
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- pytorch |
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- text-generation |
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- causal-lm |
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- rwkv |
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license: apache-2.0 |
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datasets: |
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- the_pile |
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--- |
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# RWKV-4 7B |
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## Model Description |
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RWKV-4 7B is a L32-D4096 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. |
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Use https://github.com/BlinkDL/ChatRWKV to run it. |
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ctx_len = 1024 |
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n_layer = 32 |
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n_embd = 4096 |
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RWKV-4-Pile-7B-20230109-ctx4096.pth : Fine-tuned to ctx_len 4096. |
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* Likely the best. Please test. |
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################################ |
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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 |
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This is a strong chat model too. It's recommended to use +i for "Alpaca Instruct" in latest ChatRWKV v2. Examples: |
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``` |
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+i Explain the following metaphor: "Life is like cats". |
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+i write a python function to read data from an excel file. |
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``` |
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################################ |
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RWKV-4-Pile-7B-20230xxx-ctx8192-testxxx : Fine-tuned to ctx_len 8192. |
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* Slightly weaker than ctx4096 model when ctxlen < 3k. |
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RWKV-4-Pile-7B-20221115-8047.pth : Trained on the Pile for 332B tokens. |
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* Pile loss 1.8415T |
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* LAMBADA ppl 4.38, acc 67.18% |
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* PIQA acc 76.06% |
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* SC2016 acc 73.44% |
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* Hellaswag acc_norm 65.51% |
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### Instruct-test models: only useful if you construct your prompt following dataset templates |
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Note I am using "Q: instruct\n\nA: result" prompt for all instructs. |
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RWKV-4-Pile-7B-Instruct-test1 |
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instruct-tuned on https://huggingface.co/datasets/bigscience/xP3all/viewer/en/train |
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RWKV-4-Pile-7B-Instruct-test2 |
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instruct-tuned on https://huggingface.co/datasets/Muennighoff/flan & NIv2 |
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### Chinese models |
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RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.) |
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RWKV-4-Pile-7B-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.) |
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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. |
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