| import torch | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("upstage/SOLAR-10.7B-Instruct-v1.0") | |
| model = AutoModelForCausalLM.from_pretrained("upstage/SOLAR-10.7B-Instruct-v1.0") | |
| def generate_response(prompt): | |
| conversation = [{'role': 'user', 'content': prompt}] | |
| prompt = tokenizer.apply_chat_template(conversation, tokenizer=False, add_generation_prompt=True) | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| outputs = model.generate(**inputs, use_cache=True, max_length=4096) | |
| outputs_text = tokenizer.decode(outputs[0]) | |
| return outputs_text | |
| iface = gr.Interface(fn=generate_response, inputs="text", outputs="text") | |
| iface.launch() | |