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 53 hours and costed us ~ $125 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:

monsterapi/llama2-code-generation

Training procedure

Hyperparameters & Run details:

Framework versions

Loss metrics:

monsterapi/
llama2-code-generation