PEFT
code
instruct
mistral
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@@ -3,49 +3,41 @@ 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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  #### 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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  #### 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 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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  #### 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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-
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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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-
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- ![training loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/9bgb518kFwtDsFtrHzmTu.png)
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  license: apache-2.0
 
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  tags:
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  - code
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  - instruct
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+ - mistral
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  datasets:
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+ - cognitivecomputations/dolphin-coder
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+ base_model: tiiuae/falcon-7b
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  license: apache-2.0
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  ---
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  ### Finetuning Overview:
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+ **Model Used:** tiiuae/falcon-7b
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+ **Dataset:** cognitivecomputations/dolphin-coder
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  #### Dataset Insights:
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+ [Dolphin-Coder](https://huggingface.co/datasets/cognitivecomputations/dolphin-coder) Dolphin-Coder dataset – a high-quality collection of 100,000+ coding questions and responses. It's perfect for supervised fine-tuning (SFT), and teaching language models to improve on coding-based tasks.
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  #### Finetuning Details:
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+ With the utilization of [MonsterAPI](https://monsterapi.ai)'s [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning:
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  - Was achieved with great cost-effectiveness.
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+ - Completed in a total duration of 14hr 10mins for 1 epochs using an A6000 48GB GPU.
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+ - Costed `$28.48` for the entire 1 epoch.
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  #### Hyperparameters & Additional Details:
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  - **Epochs:** 1
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+ - **Cost Per Epoch:** $31.51
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+ - **Model Path:** tiiuae/falcon-7b
 
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  - **Learning Rate:** 0.0002
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  - **Data Split:** 100% train
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+ - **Gradient Accumulation Steps:** 64
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+ - **lora r:** 64
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+ - **lora alpha:** 16
 
 
 
 
 
 
 
 
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+ ---
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  license: apache-2.0