Finetuning Overview:
Model Used: google/gemma-2-2b-it
Dataset: cfilt/iitb-english-hindi
Dataset Insights:
The IIT Bombay English-Hindi corpus contains a parallel corpus for English-Hindi as well as a monolingual Hindi corpus collected from various sources. This corpus has been utilized in the Workshop on Asian Language Translation Shared Task since 2016 for Hindi-to-English and English-to-Hindi language pairs and as a pivot language pair for Hindi-to-Japanese and Japanese-to-Hindi translations.
Finetuning Details:
With the utilization of MonsterAPI's LLM finetuner, this finetuning:
- Was achieved with cost-effectiveness.
- Completed in a total duration of 1 hour and 33 minutes for 0.1 epochs.
- Costed
$1.91for the entire process.
Hyperparameters & Additional Details:
- Epochs: 0.1
- Total Finetuning Cost: $1.91
- Model Path: google/gemma-2-2b-it
- Learning Rate: 0.001
- Data Split: 100% Train
- Gradient Accumulation Steps: 16
Prompt Template
<bos><start_of_turn>user
{PROMPT}<end_of_turn>
<start_of_turn>model
{OUTPUT} <end_of_turn>
<eos>
Training loss:
license: apache-2.0
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