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--- |
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library_name: peft |
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tags: |
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- translation |
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- code |
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- instruct |
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- gemma |
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datasets: |
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- cfilt/iitb-english-hindi |
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base_model: google/gemma-2-2b-it |
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license: apache-2.0 |
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--- |
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### Finetuning Overview: |
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**Model Used:** google/gemma-2-2b-it |
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**Dataset:** cfilt/iitb-english-hindi |
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#### Dataset Insights: |
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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. |
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#### Finetuning Details: |
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With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning: |
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- Was achieved with cost-effectiveness. |
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- Completed in a total duration of 1 hour and 33 minutes for 0.1 epochs. |
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- Costed `$1.91` for the entire process. |
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#### Hyperparameters & Additional Details: |
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- **Epochs:** 0.1 |
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- **Total Finetuning Cost:** $1.91 |
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- **Model Path:** google/gemma-2-2b-it |
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- **Learning Rate:** 0.001 |
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- **Data Split:** 100% Train |
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- **Gradient Accumulation Steps:** 16 |
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##### Prompt Template |
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``` |
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<bos><start_of_turn>user |
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{PROMPT}<end_of_turn> |
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<start_of_turn>model |
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{OUTPUT} <end_of_turn> |
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<eos> |
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``` |
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Training loss: |
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--- |
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license: apache-2.0 |
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