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--- |
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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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- mistral |
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datasets: |
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- cognitivecomputations/dolphin-coder |
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base_model: mistralai/Mistral-7B-v0.1 |
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license: apache-2.0 |
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--- |
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### Finetuning Overview: |
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**Model Used:** mistralai/Mistral-7B-v0.1 |
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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) 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 15hr 36mins for 1 epochs using an A6000 48GB GPU. |
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- Costed `$31.51` 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:** mistralai/Mistral-7B-v0.1 |
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- **Learning Rate:** 0.0002 |
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- **Data Split:** 100% train |
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- **Gradient Accumulation Steps:** 128 |
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- **lora r:** 32 |
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- **lora alpha:** 64 |
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--- |
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license: apache-2.0 |
