Training procedure
We finetuned Llama 2 7B model from Meta on nampdn-ai/tiny-codes for ~ 10,000 steps using MonsterAPI no-code LLM finetuner.
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:
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Model tree for monsterapi/llama2-7b-tiny-codes-code-generation
Base model
meta-llama/Llama-2-7b-hf