| import gradio as gr | |
| import json | |
| import requests | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| model_name = 'Pyg' | |
| tokenizer = AutoTokenizer.from_pretrained("TheBloke/Pygmalion-7B-SuperHOT-8K-GPTQ") | |
| model = AutoModelForCausalLM.from_pretrained("TheBloke/Pygmalion-7B-SuperHOT-8K-GPTQ") | |
| pipe = pipeline("text-generation", model="TheBloke/Pygmalion-7B-SuperHOT-8K-GPTQ") | |
| def generate_text(input_text): | |
| input_ids = tokenizer.encode(input_text, return_tensors='pt') | |
| outputs = model.generate(input_ids, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id) | |
| text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return text | |
| iface = gr.Interface(fn=generate_text, | |
| inputs=gr.inputs.Textbox(lines=5, placeholder='Enter text here...'), | |
| outputs=gr.outputs.Textbox()) | |
| iface.launch() |