README.md

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# 1. Detect device (GPU or CPU)
DEVICE = 0 if torch.cuda.is_available() else -1

# 2. Load tokenizer & model from Hugging Face Hub
MODEL_NAME = "kshitijthakkar/loggenix_general"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)

# 3. Set up a text-generation pipeline (optional but convenient)
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device=DEVICE,       # GPU if available, else CPU
    framework="pt"
)

# 4. Simple generate function
def generate_text(prompt: str, max_length: int = 100, num_return_sequences: int = 1):
    output = generator(
        prompt,
        max_length=max_length,
        num_return_sequences=num_return_sequences,
        do_sample=True,        # enables sampling
        top_k=50,              # top-k sampling
        top_p=0.95,            # nucleus sampling
        temperature=0.7        # adjust creativity
    )
    return [item['generated_text'] for item in output]

# 5. Example usage
if __name__ == "__main__":
    prompt = input("Enter your prompt: ")
    outputs = generate_text(prompt, max_length=512)
    for i, text in enumerate(outputs, 3):
        print(f"----- Generated Sequence {i} -----")
        print(text)
        print()