| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| import torch | |
| model_id = "mistralai/Mistral-7B-Instruct-v0.1" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| device_map="auto", | |
| torch_dtype=torch.float16, | |
| load_in_4bit=True | |
| ) | |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| def chat(prompt, history=[]): | |
| full_prompt = prompt | |
| output = pipe(full_prompt, max_new_tokens=200, do_sample=True, temperature=0.7) | |
| return output[0]["generated_text"] | |
| gr.ChatInterface( | |
| fn=chat, | |
| title="🧠 Mistral 7B Instruct Chatbot", | |
| description="This chatbot is powered by the open-source Mistral 7B LLM. Ask anything!", | |
| theme="soft" | |
| ).launch() | |