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---
library_name: peft
tags:
- code
- instruct
- code-llama
datasets:
- cognitivecomputations/dolphin-coder
base_model: codellama/CodeLlama-7b-hf
license: apache-2.0
---
### Finetuning Overview:
**Model Used:** codellama/CodeLlama-7b-hf
**Dataset:** cognitivecomputations/dolphin-coder
#### Dataset Insights:
[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.
#### Finetuning Details:
With the utilization of [MonsterAPI](https://monsterapi.ai)'s [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning:
- Was achieved with great cost-effectiveness.
- Completed in a total duration of 15hr 31mins for 1 epochs using an A6000 48GB GPU.
- Costed `$31.31` for the entire 1 epoch.
#### Hyperparameters & Additional Details:
- **Epochs:** 1
- **Total Finetuning Cost:** $31.31
- **Model Path:** codellama/CodeLlama-7b-hf
- **Learning Rate:** 0.0002
- **Data Split:** 100% train
- **Gradient Accumulation Steps:** 128
- **lora r:** 32
- **lora alpha:** 64
---
license: apache-2.0 |