Create README.md

README.md

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+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
+
+```sh
+pip install transformers torch
+```
+
+### Loading the Model in Python
+
+```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.