README.md

---
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

![Train Loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/aNujXePogMlJZmoi1Bq56.png)

---
license: apache-2.0