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Update README.md
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README.md
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tags:
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- code
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- instruct
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datasets:
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base_model:
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license: apache-2.0
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---
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### Finetuning Overview:
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**Model Used:**
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**Dataset:**
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#### Dataset Insights:
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[
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#### Finetuning Details:
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With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://
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- Was achieved with great cost-effectiveness.
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- Completed in a total duration of
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- Costed `$
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#### Hyperparameters & Additional Details:
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- **Epochs:** 1
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- **Cost Per Epoch:** $
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- **
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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:**
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- **lora r:**
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- **lora alpha:**
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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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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
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