Update README.md

README.md

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 - rwkv-code39-16012024.pth:  3B rocm-rwkv model trained with Slim pajama chunk1-10 for the first epoch and an aditional training with chunk1-8 after the first epoch; plus a little bit of code. This pth has a loss of 1.174 for code alone and 2.26 for text.
 - rwkv-HHMIX-63x1-47-29012024.pth: 3B rocm-rwkv model starting with rwkv-code39-16012024.pth plus a mix of multi-language and code. This model has a loss value of 2.065 for the code+multilingual dataset.
 - rwkv-coder-63x1-104-29012024.pth: 3B rocm-rwkv model starting with rwkv-HHMIX-63x1-47-29012024.pth plus more code (71.21 Gtokens of code). This model has a loss value of 1.090 for the code dataset.
-- rwkv-final_HHMIX_chuk3.pth: 3B rocm-rwkv model starting with rwkv-coder-63x1-104-29012024.pth plus a mix of multi-language and code. This model has a loss value of 1.836 for the code+multilingual dataset.
+- rwkv-final_HHMIX_chuk3.pth: 3B rocm-rwkv model starting with rwkv-coder-63x1-104-29012024.pth plus a mix of multi-language and code. This model has a loss value of 1.836 for the code+multilingual dataset.
+- rwkv-v5-stp2-N8.pth : 3B rocm-rwkv model starting with the previous one (I think maybe I added more code or random multilangual, I don't remember) plus aditional 3 chunks of my mix of multi-language(ramdom) and code + 3 chunks of my dataset soup multilangual(only languages with character different to the english or latin-greek alphabet,e.g. Japanise, Cherokee, etc) + code + math+ instruct+ chain of thought). This model is the second epoch (step) on the N8 dataset. This pth has a loss of 1.94 for N8.