---
library_name: peft
tags:
- code
- instruct
- gpt2
datasets:
- cognitivecomputations/dolphin-coder
base_model: gpt2
license: apache-2.0
---

### Finetuning Overview:

**Model Used:** gpt2

**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 58mins 48s for 1 epochs using an A6000 48GB GPU.
- Costed `$1.96` for the entire 1 epoch.

#### Hyperparameters & Additional Details:

- **Epochs:** 1
- **Total Finetuning Cost:** $1.96
- **Model Path:** gpt2
- **Learning Rate:** 0.0002
- **Data Split:** 100% train 
- **Gradient Accumulation Steps:** 128
- **lora r:** 32
- **lora alpha:** 64

![Train Loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/3XcaHhWcu0hTqq_zf0WSH.png)

---
license: apache-2.0