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 $1.91 for 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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