---
license: apache-2.0
library_name: peft
tags:
- llama2
- llama2-7b
- code generation
- code-generation
- code
- instruct
- instruct-code
- code-alpaca
- alpaca-instruct
- alpaca
- llama7b
- gpt2
datasets:
- nampdn-ai/tiny-codes
base_model: meta-llama/Llama-2-7b-hf
---
## Training procedure
We finetuned [Llama 2 7B model](https://huggingface.co/meta-llama/Llama-2-7b-hf) from Meta on [nampdn-ai/tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes) for ~ 10,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).

This dataset contains **1.63 million rows** and is a collection of short and clear code snippets that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the relationship of entities. 

The finetuning session got completed in 193 minutes and costed us only ~ `$7.5` for the entire finetuning run!

#### Hyperparameters & Run details:
- Model Path: meta-llama/Llama-2-7b-hf
- Dataset: nampdn-ai/tiny-codes
- Learning rate: 0.0002
- Number of epochs: 1 (10k steps)
- Data split: Training: 90% / Validation: 10%
- Gradient accumulation steps: 1

### Framework versions

- PEFT 0.4.0

### Loss metrics:
![training loss](train-loss.png "Training loss")