This is an experimental llama2 7B lora created using the VNTL-v2-1k dataset. There have been some minor changes in the dataset since version 0.2, and I have made the following adjustments to the training arguments:
- Model loaded in 8 bits.
- Sequence length limited to 1024 tokens to speed up experiments.
- Effective batch size changed to 30 (6 batch size + 5 grad acc).
- 2 epochs.
Eval Loss: 0.78
This lora was trained alongside a 4-bit lora (qlora), the goal being to see if training a lora would be better than training a qlora. In the end, I don't think that there was much of a difference. At most I saw a consistent 0.01 drop in loss, but the loss graph looked the same, which meant both fine-tunes converged the same way.
This is an prompt example:
<<START>>
Name: Uryuu Shingo (ηη ζ°εΎ) | Gender: Male | Aliases: Onii-chan (γε
γ‘γγ)
Name: Uryuu Sakuno (ηη ζ‘δΉ) | Gender: Female
<<JAPANESE>>
[ζ‘δΉ]: γβ¦β¦γγγγ
<<ENGLISH>> (fidelity = absolute)
[Sakuno]: γ... Sorry.γ
<<JAPANESE>>
[ζ°εΎ]: γγγγγγγθ¨γ£γ‘γγͺγγ γγ©γθΏ·εγ§γγγ£γγγζ‘δΉγ―ε―ζγγγγγγγγεΏι
γγ‘γγ£γ¦γγγ γδΏΊγ
<<ENGLISH>> (fidelity = high)
The generated translation for that prompt, with temperature 0, is:
[Shingo]: γNo, don't apologize. I'm just glad you're safe. You're so cute, Sakuno, I was worried sick.γ
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