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---
library_name: peft
tags:
- translation
- code
- instruct
- gemma
datasets:
- cfilt/iitb-english-hindi
base_model: google/gemma-2-2b-it
license: apache-2.0
---
### 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](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), 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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