| 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 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: | |
|  |