gpt2-lmheadmodel-next-line-prediction-model

from transformers import AutoTokenizer, AutoModelForCausalLM import torch

Set device

device = "cuda" if torch.cuda.is_available() else "cpu"

Model and tokenizer

model_name = "AventIQ-AI/gpt2-lmheadmodel-next-line-prediction-model" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

import html

Define test text

sample_text = "Artificial intelligence is transforming"

Tokenize input

inputs = tokenizer(sample_text, return_tensors="pt").to(device)

Generate prediction

with torch.no_grad(): output_tokens = model.generate( **inputs, max_length=50, num_beams=5, repetition_penalty=1.5, temperature=0.7, top_k=50, top_p=0.9, do_sample=True, no_repeat_ngram_size=2, num_return_sequences=1, early_stopping=True, length_penalty=1.0, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True )

Decode and clean response

generated_response = tokenizer.decode(output_tokens.sequences[0], skip_special_tokens=True) cleaned_response = html.unescape(generated_response).replace("#39;", "'").replace("quot;", '"')

print("\nGenerated Response:\n", cleaned_response)

GPT-2 for Next-line Prediction

This repository hosts a fine-tuned GPT-2 model optimized for next-line prediction tasks. The model has been fine-tuned on the OpenWebText dataset and quantized in FP16 format to enhance efficiency without compromising performance.

Model Details

• Model Architecture: GPT-2 (Causal Language Model)
• Task: Next-line Prediction
• Dataset: OpenWebText (subset: stas/openwebtext-10k)
• Quantization: FP16 for reduced model size and faster inference
• Fine-tuning Framework: Hugging Face Transformers

Training Details

• Number of Epochs: 3
• Batch Size: 4
• Evaluation Strategy: Epoch
• Learning Rate: 5e-5

Evaluation Metrics (Perplexity Score)

Perplexity Score: 14.355693817138672

Limitations

• The model is optimized for English-language next-word prediction tasks.
• While quantization improves speed, minor accuracy degradation may occur.
• Performance on out-of-distribution text (e.g., highly technical or domain-specific data) may be limited.

Usage Instructions

Installation

pip install transformers torch

Loading the Model in Python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/gpt2-lmheadmodel-next-line-prediction-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.