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rocm-rwkv

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rocm-rwkv
3B-rwkv
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tags:
  - rocm
  - amd-gpus
  - amd-ai
  - rocm-ai
  - rocm-rwkv
  - 3B-rwkv

3B rocm-rwkv pth record. This 3B is a little different than the usual 3B. This 3B model have 48 Layers, embd of 2048 and Ctxt of 16384 (I think that all pth have the same ctxt size).

  • 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.
  • rwkv-v5-stp18-N8.pth : 3B rocm-rwkv model starting with the previous but now with 18 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.827 for N8 and 13.377 GTokens.
  • rwkv-v5-stp32-N8.pth : 3B rocm-rwkv model starting with the previous but now with 32 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.810 for N8 and 22.46 GTokens.
  • rwkv-v5-stp46-N8.pth : 3B rocm-rwkv model starting with the previous but now with 46 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.800 for N8 and 31.874 GTokens.
  • rwkv-v5-stp62-N8.pth : 3B rocm-rwkv model starting with the previous but now with 62 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.790 for N8 and 42.538 GTokens.
  • rwkv-v5-stp76-N8.pth : 3B rocm-rwkv model starting with the previous but now with 62 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.780 for N8 and 51.763 GTokens.
  • rwkv-v5-stp118-N8.pth : 3B rocm-rwkv model starting with the previous but now with 118 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.750 for N8 and 79.508 GTokens.
  • rwkv-v5-stp146-N8.pth : 3B rocm-rwkv model starting with the previous but now with 146 epochs of N8 dataset with --lr_init 7e-6 --lr_final 7e-6. This pth has a loss of 1.758 for N8 and 97.982 GTokens.
  • rwkv-v5-final-N8.pth : 3B rocm-rwkv model starting with the previous but now with the full N8 dataset epoch with --lr_init 3e-8 --lr_final 1e-8 This pth has a loss of 1.73 for the full N8 dataset with 106.098327552 GTokens.
  • rwkv-3B-stp634-N8-3.pth : 3B rocm-rwkv model starting with the previous but now with the 104 GTokens of the N8-3 dataset with ctxt=4k. This pth has a loss of 1.92 for the N8-3 dataset.
  • rwkv-3B-4K-stp802-N8-3.pth: Using rwkv-3B-stp634-N8-3.pth I added 7 more GTokens of N8-3.

7B rocm-rwkv pth record: I called this model Tlanuwa since I added an extra training focusing on cherokee after each run.

  • rwkv-7BTlanuwa-1k-soup91-Final.pth: 7B model 32 layers embd=4096 ctx= 16384. This have all the same training as the 3B but only Slim pajama from 1-9 probably more than 2T tokens but a loss of 2.834 with respect to the the full soup91. I am working on getting a lower loss.

9B rocm-rwkv pth record: 40 layers embd=4096 ctx= 16384 I am calling this model Quetzal. I called this model Quetzal since it is a green model that flies and I am adding an extra training focusing on Spanish and the dataset Axolotl-Spanish-Nahuatl after each run.

  • rwkv-9Q-stp101-N8.pth: 9B rocm-rwkv model trained with Slim pajama chunk1-10 for the first epoch and an aditional training with chunk1-2 and a mix of multi-language and code after that I am using the N8 dataset. I am currendly with the N8 dataset 4.222 GTokes. This pth has a loss of 1.904 regarding the N8 dataset.
  • rwkv-9Q-1k-stp307-1k-N8.pth: 9B rocm-rwkv model trained with Slim pajama chunk1-10 for the first epoch and an aditional training with chunk1-2 and a mix of multi-language and code after that I am using the N8 dataset. I am currendly with the N8 dataset 12.706 GTokes. This pth has a loss of 1.871 regarding the N8 dataset.
  • rwkv-9Q-Soup91-step298.pth : Using the rwkv-9Q-1k-stp307-1k-N8.pth I added 298 epoch steps of my soup of data (code + math+ instruct+ chain of thought) 12.283 Gtokens with a loss of 2.242.
  • rwkv-9Q-Soup91-Final.pth : Using the rwkv-9Q-Soup91-step298.pth I added 298 -> 1035 epoch steps of my soup of data (code + math+ instruct+ chain of thought) 42.733 Gtokens with a loss of 2.222.
  • rwkv-9Q-stp1447-N8.pth : Using rwkv-9Q-Soup91-Final.pth I added 1447 steps of N8 59.733 Gtokens with a loss of 1.827.
  • rwkv-9Q-Final-N8-1k.pth : Using rwkv-9Q-stp1447-N8.pth I added 2569 steps of N8 which are 106 Gtokens with a loss of 1.801.
  • rwkv-9Q-1k-stp706-N8-0.pth: Using rwkv-9Q-1k-stp706-N8-0.pth I added 706 new steps and 29.13 Gtokens of N8-0 with a loss of 1.78
  • rwkv-9Q-4k-stp248.pth: Using rwkv-9Q-1k-stp706-N8-0.pth I added 2048 new steps with 40.66 Gtokens with a loss of 1.717 Nathan-0 datase and Ctx=4096.
  • rwkv-9Q-16k-step6-0-4.pth: Using rwkv-9Q-4k-stp248.pth I added N-0 and N-8 and a Ctx=16384 loss=1.65. This model looks that can chat better.
  • rwkv-9Q-step607-N8-3.pth: Using rwkv-9Q-16k-step6-0-4.pth I add 100G tokens of N8-3.
  • rwkv-9Q-4k-stp662-N8-3.pth: Using rwkv-9Q-step607-N8-3.pth I added 10G tokes more of N8-3.

V6 models:

6B rocm-rwkv pth record: 12 layers embd=6144 ctx=4096.

  • rwkv-6B-N3-final.pth: 6B rocm-rwkv model trained with N3 with a final loss=3.56 after 100G Tokens
  • rwkv-6B-N0-final.pth: starting from the previous pth rocm-rwkv trained with N0 with a final loss=3.11 after 100G Tokens