Instructions to use David0dods/codeforces-gpt-neo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use David0dods/codeforces-gpt-neo-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
model = PeftModel.from_pretrained(base_model, "David0dods/codeforces-gpt-neo-lora")

Transformers

How to use David0dods/codeforces-gpt-neo-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="David0dods/codeforces-gpt-neo-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("David0dods/codeforces-gpt-neo-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use David0dods/codeforces-gpt-neo-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "David0dods/codeforces-gpt-neo-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "David0dods/codeforces-gpt-neo-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/David0dods/codeforces-gpt-neo-lora

SGLang

How to use David0dods/codeforces-gpt-neo-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "David0dods/codeforces-gpt-neo-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "David0dods/codeforces-gpt-neo-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "David0dods/codeforces-gpt-neo-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "David0dods/codeforces-gpt-neo-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use David0dods/codeforces-gpt-neo-lora with Docker Model Runner:
```
docker model run hf.co/David0dods/codeforces-gpt-neo-lora
```

Codeforces GPT-Neo Python Assistant

Model Description

This model is a specialized version of EleutherAI/gpt-neo-125M, fine-tuned to assist in solving competitive programming challenges. It has been trained to generate structured Python solutions based on complex problem descriptions. This project was developed as a key task for the Generative AI track within the Digital Egypt Pioneers Initiative (DEPI) scholarship.

Intended Uses & Limitations

Intended Use: Assistance with Python coding logic and competitive programming patterns.
Limitations: Due to its lightweight architecture (125M parameters), the model may struggle with highly complex algorithmic logic and is best used as a coding assistant rather than a primary solver.

Training and Evaluation Data

The model was fine-tuned on a specialized subset of the MatrixStudio/Codeforces-Python-Submissions dataset. The training data followed an Instruction-Problem-Solution format to ensure the model learns to follow specific programming prompts effectively.

Training Procedure

Technique: LoRA (Low-Rank Adaptation)

To ensure efficient training, the model utilized PEFT (LoRA). This allowed for effective domain adaptation while significantly reducing the number of trainable parameters.

Training Hyperparameters

Learning Rate: 0.0002
Total Train Batch Size: 16 (4 batch size per device with 4 accumulation steps)
Optimizer: AdamW with Mixed Precision (Native AMP)
Epochs: 3
LoRA Rank (r): 8

Training Results

The model showed consistent improvement over 3 epochs, with the final Validation Loss reaching 2.0083. The steady decrease in both Training and Validation loss indicates successful convergence without overfitting.

Training Loss	Epoch	Step	Validation Loss
2.0445	1.0	563	2.0761
2.0477	2.0	1126	2.0216
1.9682	3.0	1689	2.0083

Framework Versions

PEFT: 0.18.1
Transformers: 5.2.0
Pytorch: 2.10.0+cu128
Datasets: 4.5.0

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