| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained('Aityz/Aityz-3B') | |
| model = AutoModelForCausalLM.from_pretrained('Aityz/Aityz-3B') | |
| def generate(instruction, input = None, maxtokens: int = 20): | |
| if input is not None: | |
| ln = f'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} \n### Response:' | |
| if input is None: | |
| ln = f'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:' | |
| inputs = tokenizer(ln, return_tensors="pt") | |
| output = model.generate(inputs=inputs['input_ids'], max_new_tokens=maxtokens) | |
| return tokenizer.decode(output[0].tolist()) | |
| inter = gr.Interface(fn=generate, inputs=["textbox", "textbox", gr.Slider(1, 1000, value=100)], outputs="textbox") | |
| inter.launch(share=False) |