Model Documentation: Gpt-2.2

Overview

Gpt-2.2 is a fine-tuned version of the GPT-2 base model, optimized for language modeling tasks using the Hugging Face transformers library.

Training Details

• Dataset: Wikitext-2-raw-v1.
• Training Volume: 10% of the training split.
• Epochs: 3.
• Framework: PyTorch with Hugging Face Trainer API.

How It Works & Capabilities

This model works through causal language modeling, predicting the next token in a sequence based on previous context.

• Text Generation: It is fully capable of generating coherent, human-like text.
• Token Limit: In this setup, the model is configured to generate up to 50 tokens per request, though the underlying architecture supports sequences up to 1024 tokens.

Hardware Recommendations

• GPU (Recommended): Use a GPU (like the NVIDIA T4 in Colab) for near-instant text generation. This is ideal for real-time applications.
• CPU: The model will run on a CPU, but expect significantly slower response times. It is suitable for testing if a GPU is unavailable.

How Others Can Use This Model

To download and use Gpt-2.2, other users simply need the transformers library installed. They can load it directly from the Hugging Face Hub using your repository ID:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Replace with your actual repo ID if different
repo_id = "BikoRiko/Gpt-2.2"

# Download and load the model
model = GPT2LMHeadModel.from_pretrained(repo_id)
tokenizer = GPT2Tokenizer.from_pretrained(repo_id)

# Ready to generate text!