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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 1.5B |
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## Model Description |
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RWKV-4 1.5B is a L24-D2048 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. |
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** Note: It's a BF16 model, and it may overflow if you are using FP16 (probably fixable by rescaling the weights). ** |
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At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-LM) to run it. |
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ctx_len = 1024 |
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n_layer = 24 |
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n_embd = 2048 |
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New checkpoint: RWKV-4-Pile-1B5-20220929-ctx4096.pth : Fine-tuned to ctx_len = 4096 |
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Final checkpoint: RWKV-4-Pile-1B5-20220903-8040.pth : Trained on the Pile for 332B tokens. |
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* Pile loss 2.0415 |
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* LAMBADA ppl 7.04, acc 56.43% |
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* PIQA acc 72.36% |
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* SC2016 acc 68.73% |
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* Hellaswag acc_norm 52.48% |
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Preview checkpoint: RWKV-4-Pile-1B5-20220822-5809.pth : Trained on the Pile for 240B tokens. |
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* Pile loss 2.0518 |
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* LAMBADA ppl 7.14, acc 56.36% |
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* PIQA acc 71.71% |
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* SC2016 acc 68.15% |
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* Hellaswag acc_norm 52.04% |
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Preview checkpoint: RWKV-4-Pile-1B5-20220814-4526.pth : Trained on the Pile for 187B tokens. |
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* Pile loss 2.0635 |
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* LAMBADA ppl 7.34, acc 55.64% |
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* PIQA acc 71.44% |
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* SC2016 acc 68.25% |
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* Hellaswag acc_norm 51.60% |
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