mhhmm/typescript-instruct-20k-v2

Architecture

About

This model is a fine-tuned version of codellama/CodeLlama-13b-hf. It achieves the following results on the evaluation set:

• —Loss: 0.4268

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0002
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 2
• —totaltrainbatch_size: 16
• —totalevalbatch_size: 16
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 10
• —num_epochs: 1

Training results

Framework versions

• —Transformers 4.36.0.dev0
• —Pytorch 2.0.1+cu118
• —Datasets 2.15.0
• —Tokenizers 0.15.0
• —PEFT 0.6.0

Evaluation

I'm using MultiPL-E benchmark, the same as Code Llmama using in their paper

How to reproduce my evaluation? Just run like the offical document of MultiPL-E: https://nuprl.github.io/MultiPL-E/tutorial.html, change the modal name by my model here: mhhmm/typescript-instruct-20k-v2

This is the code that I ran with Google Colab (using A100 40GB, yes, it requires that much GPU RAM)

If you even have a stronger GPU, increase the --batch-size, or --completion-limit

!pip install --upgrade pip
!pip install aiohttp numpy tqdm pytest datasets torch transformers sentencepiece
!git clone https://github.com/nuprl/MultiPL-E
%cd MultiPL-E
!mkdir typescript
!python3 automodel.py --name mhhmm/typescript-instruct-20k-v2 --root-dataset humaneval --lang ts --temperature 0.2 --batch-size 10 --completion-limit 20 --output-dir-prefix typescript
%cd evaluation/src
!python3 main.py --dir ../../typescript --output-dir ../../typescript --recursive
!python3 pass_k.py ./typescript/*