README.md

---
tags:
- rocm
- amd-gpus
- amd-ai
- rocm-ai
- rocm-rwkv
- 3B-rwkv
---
3B rocm-rwkv pth record. 
- rwkv-final-chnk5.pth: 3B rocm-rwkv model trained with Slim pajama chunk1-5 and with a loss of 2.456.
- rwkv-final-chnk17.pth: 3B rocm-rwkv model trained with Slim pajama chunk1-10 for the first epoch and an aditional training with chunk1-7 after the first epoch  and with a loss of 2.281
- 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-1epoch_N8_wrong_lr.pth: 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 has 1 epoch (step) on the N8 dataset but with --lr_init 5e-7 --lr_final 5e-8. This pth has a loss of 1.978 for N8. 
- rwkv-v5-stp2-N8.pth : 3B rocm-rwkv model starting with the previous one + two epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.94 for N8.
- rwkv-v5-stp5-N8.pth : 3B rocm-rwkv model starting with the previous but now with 5 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.90 for N8.