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---
license: llama2
library_name: peft
tags:
- typescript
- instruction-tuning
- code-generation
- lora
- peft
base_model: codellama/CodeLlama-13b-hf
model-index:
- name: lora-out
results: []
datasets:
- mhhmm/typescript-instruct-20k
language:
- en
metrics:
- code_eval
pipeline_tag: text-generation
---
## Architecture
## About
This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/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
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7555 | 0.01 | 1 | 0.7062 |
| 0.7036 | 0.05 | 7 | 0.6673 |
| 0.5422 | 0.1 | 14 | 0.5152 |
| 0.5351 | 0.15 | 21 | 0.4866 |
| 0.495 | 0.2 | 28 | 0.4688 |
| 0.5651 | 0.25 | 35 | 0.4587 |
| 0.5146 | 0.3 | 42 | 0.4486 |
| 0.4955 | 0.35 | 49 | 0.4469 |
| 0.5117 | 0.4 | 56 | 0.4432 |
| 0.5245 | 0.45 | 63 | 0.4410 |
| 0.5003 | 0.5 | 70 | 0.4371 |
| 0.4502 | 0.55 | 77 | 0.4340 |
| 0.527 | 0.6 | 84 | 0.4315 |
| 0.48 | 0.65 | 91 | 0.4305 |
| 0.448 | 0.7 | 98 | 0.4289 |
| 0.5427 | 0.75 | 105 | 0.4289 |
| 0.4715 | 0.8 | 112 | 0.4279 |
| 0.5584 | 0.85 | 119 | 0.4276 |
| 0.4936 | 0.9 | 126 | 0.4267 |
| 0.4788 | 0.95 | 133 | 0.4268 |
| 0.476 | 1.0 | 140 | 0.4268 |
### 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
| Modal | Pass@k | Estimate | Num problems |
|-----------------------------------------|--------|----------|---------------|
| Code LLama - Instruct 13B | 1 | 39.0% | 159 |
| Our 13B | 1 | 42.4% | 159 |
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/*
```
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