---
library_name: transformers
tags:
- code
license: mit
base_model:
- distilbert/distilgpt2
datasets:
- teven/code_contests
language:
- en
---

# Model Card for Model ID

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



## Model Details

### Model Description

This model is a LoRA fine-tuned version of distilgpt2, optimized for generating programming solutions in a style similar to competitive programming platforms such as LeetCode and Codeforces. It was trained on a custom dataset of ~5000 coding questions and answers and designed to be deployed with low-resource hardware (4GB VRAM GPU RTX 3050). The model is part of a larger project that incorporates Retrieval-Augmented Generation (RAG) to personalize outputs according to a user's historical coding patterns.

- **Developed by:** https://github.com/Srinidhi-Yoganand
- **Funded by :** Self-funded 
- **Shared by :** sriniidhi
- **Model type:** Causal Language Model (Decoder-only)
- **Language(s) (NLP):** English (programming-focused)
- **License:** MIT 
- **Finetuned from model :** distilgpt2

### Model Sources 

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

- **Repository:** [Link]
- **Demo:** [Link]

## Uses

### Direct Use

This model can be used for:

  -  Auto-completing coding problems with competitive programming-style answers

  -  Assisting in learning algorithms by showing step-by-step code solutions

  -  Experimenting with personalized coding assistants

### Downstream Use 

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

It can be plugged into systems using RAG to personalize answers by analyzing a user’s prior code submissions, or integrated into IDE plugins or chat-based tutoring systems.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

  - Generating natural language responses outside of programming tasks

  - Mission-critical code generation (e.g., medical, legal, or financial systems)

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

- May hallucinate code or logic for uncommon problems.

- Not robust to complex multi-language code interactions or frameworks.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

- Use in combination with RAG for best personalization results.

- Validate generated code before execution.

- Avoid relying solely on this model for production-critical code generation.

## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sriniidhi/gpt2-coding")
tokenizer = AutoTokenizer.from_pretrained("sriniidhi/gpt2-coding")

prompt = "def two_sum(nums, target):"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## 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. -->

The dataset consists of 5000+ competitive programming Q&A-style examples extracted and formatted from LeetCode, Codeforces, and similar platforms. Each entry includes a problem prompt and a sample Python, Java, Cpp solution.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
LoRA fine-tuning using peft and transformers on top of distilgpt2.

#### Preprocessing [optional]

- Tokenized using GPT2TokenizerFast

- Prompt-style formatting with problem + solution pairs

- All code lowercased for consistency


#### Training Hyperparameters

- **Training regime:**
- Epochs: 3

- Batch size: 2

- Learning rate: 5e-5

- LoRA rank: 8

- Precision: fp16

- Max length: 512

- Optimizer: AdamW
  
#### Speeds, Sizes, Times 

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- Fine-tuned on Google Colab and locally on RTX 3050 4GB

- Training duration: ~30 hours

- LoRA-adapted weights: ~75MB

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

Evaluation done on 10000 held-out samples not used during training.

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

Manual evaluation of logical correctness and style similarity

### Results

- Approx. 80% logical match on simple algorithm questions

- Maintains coding style reasonably for most basic prompts

- Some struggles with complex nested logic

## Model Examination 

- Focused on learning indentation, loop constructs, and simple algorithm templates

- No external code memory or global context unless paired with RAG

## 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:** NVIDIA RTX 3050 (4GB VRAM)
- **Hours used:** ~30 hrs 
- **Cloud Provider:** Google Colab (partial)
- **Compute Region:** India
- **Carbon Emitted:** ~2.15 kg CO₂ eq (estimated)

## Technical Specifications 

### Model Architecture and Objective

Decoder-only Transformer (distilgpt2, 6-layer GPT-2)

### Compute Infrastructure

- Colab + Local

- PyTorch, transformers, peft, bitsandbytes

#### Hardware

- Ryzen 7
- RTX 3050

## Citation 

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

**BibTeX:**
```
@misc{gpt2-coding,
  author       = {Srinidhi},
  title        = {LoRA Fine-tuned distilgpt2 for Code Generation},
  year         = {2025},
  url          = {https://huggingface.co/sriniidhi/gpt2-coding}
}
```

## Model Card Contact

GitHub: https://github.com/Srinidhi-Yoganand

Hugging Face: https://huggingface.co/sriniidhi