README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Shi Hao Ng, IB DP Student, Marlborough College Malaysia
- **Model type:** Transformer. Decoder only
- **Language(s) (NLP):** Python, HTML, etc. 
- **License:** mit
- **Finetuned from model [optional]:** No

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/Ice-Citron/GPTesla

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
- You input half finished python code, and it will generate python code.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
- Some level of fine tuning is likely needed or preferred. However I won't be working on this.

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
- This can easily be used for IDEs. Not ideal though as it's rarely correct in its answer. Likely mainly attributed to how it's a pretty small model after all.
- Even then, I'm already struggling to train it with my 4x Nvidia A100 PCIe 80GB, taking 15 hours!

## How to Get Started with the Model

Use the code below to get started with the model.
- just follow the instructions on huggingface "use this model". Should work. If not try and contact me.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- 111 million parameter. FP16, 444 Megabytes.
- Pretty fast and lightweight model when using T4 GPU.

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
- https://huggingface.co/datasets/shng2025/gptesla-valid

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->
- https://huggingface.co/datasets/shng2025/gptesla-train


#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
- Perhaps not accurate because I'm expecting 1 to 1 representation for code. As in reality there's many way of coding to reach the same logic. And a precise way of coding is not required.

### Results

- 1.1 loss/train in the end. Model converged after 150,000 steps.
- weights and biases file: https://wandb.ai/marlborough-college-malaysia/gptesla-small/runs/m9sqzqo3?nw=nwusershng2025

#### Summary



## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** 4x Nvidia A100 PCIe + 96x AMD CPU
- **Hours used:** 15 hours
- **Cloud Provider:** Azure
- **Compute Region:** Unclear
- **Carbon Emitted:** [More Information Needed]

### Model Architecture and Objective

- Based on codeparrot. And using GPT2's architecture but it's weights are random initialised. 

### Compute Infrastructure

- NVMe Link
- 4x Nvidia A100 PCIe
- 96x AMD CPU from Azure
- 900 GB RAM

#### Hardware

- NVMe Link
- 4x Nvidia A100 PCIe
- 96x AMD CPU from Azure
- 900 GB RAM

#### Software

- Python 3.10.14
- Latest version of Pytorch, transformer, wandb libraries, etc. installed. Refer to github repo for versions
- Accelerate

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
- codeparrot used