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| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from llama_cpp import Llama | |
| # 📥 Model Download | |
| model_path = hf_hub_download( | |
| repo_id="RockSky1/Infinity_1.0", | |
| filename="Infinity_1.0.gguf" | |
| ) | |
| print("Loading model...") | |
| llm = Llama( | |
| model_path=model_path, | |
| n_ctx=512, | |
| n_threads=2, | |
| n_batch=128 | |
| ) | |
| print("Model loaded ✅") | |
| def chat_function(message, history): | |
| try: | |
| output = llm.create_chat_completion( | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": "You are Infinity AI 🔥 created by Shivam Kumar (RockSky1) from Bihar, India." | |
| }, | |
| *[ | |
| {"role": "user", "content": h[0]} if i % 2 == 0 | |
| else {"role": "assistant", "content": h[1]} | |
| for i, h in enumerate(history) | |
| ], | |
| {"role": "user", "content": message} | |
| ], | |
| max_tokens=128, | |
| temperature=0.7 | |
| ) | |
| reply = output["choices"][0]["message"]["content"] | |
| except Exception as e: | |
| reply = f"Error: {str(e)}" | |
| return reply | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# ♾️ Infinity-LLM v1.0 🚀") | |
| gr.ChatInterface( | |
| fn=chat_function, | |
| examples=[ | |
| "Who is Shivam Kumar?", | |
| "Tell me about Infinity-LLM.", | |
| "Write a simple Python script." | |
| ] | |
| ) | |
| demo.launch() |