PEFT
code
instruct
gpt2
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  ---
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  library_name: peft
 
 
 
 
 
 
 
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  license: apache-2.0
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  ---
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- ## Training procedure
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- ### Framework versions
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- - PEFT 0.5.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  library_name: peft
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+ tags:
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+ - code
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+ - instruct
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+ - gpt2
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+ datasets:
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+ - HuggingFaceH4/no_robots
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+ base_model: gpt2
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  license: apache-2.0
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  ---
 
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+ ### Finetuning Overview:
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+ **Model Used:** gpt2
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+ **Dataset:** HuggingFaceH4/no_robots
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+
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+ #### Dataset Insights:
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+ [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better.
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+
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+ #### Finetuning Details:
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+
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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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+
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+ - Was achieved with great cost-effectiveness.
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+ - Completed in a total duration of 3mins 40s for 1 epoch using an A6000 48GB GPU.
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+ - Costed `$0.101` for the entire epoch.
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+
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+ #### Hyperparameters & Additional Details:
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+ - **Epochs:** 1
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+ - **Cost Per Epoch:** $0.101
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+ - **Total Finetuning Cost:** $0.101
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+ - **Model Path:** gpt2
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+ - **Learning Rate:** 0.0002
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+ - **Data Split:** 100% train
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+ - **Gradient Accumulation Steps:** 4
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+ - **lora r:** 32
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+ - **lora alpha:** 64
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+ #### Prompt Structure
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+ ```
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+ <|system|> <|endoftext|> <|user|> [USER PROMPT]<|endoftext|> <|assistant|> [ASSISTANT ANSWER] <|endoftext|>
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+ ```
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+ #### Training loss :
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+ ![training loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/9bgb518kFwtDsFtrHzmTu.png)
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+ license: apache-2.0