| import gradio as gr | |
| from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained( | |
| 'vitaliy-sharandin/wiseai', | |
| load_in_8bit=True, | |
| device_map = {"": 0} | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained('vitaliy-sharandin/wiseai') | |
| pipe = pipeline('text-generation', model=model,tokenizer=tokenizer) | |
| def generate_text(instruction, input): | |
| if not instruction.strip(): | |
| return str('The instruction field is required.') | |
| if instruction.strip() and input.strip(): | |
| input_prompt = (f"Below is an instruction that describes a task. " | |
| "Write a response that appropriately completes the request.\n\n" | |
| "### Instruction:\n" | |
| f"{instruction}\n\n" | |
| "### Input:\n" | |
| f"{input}\n\n" | |
| f"### Response: \n") | |
| else : | |
| input_prompt = (f"Below is an instruction that describes a task. " | |
| "Write a response that appropriately completes the request.\n\n" | |
| "### Instruction:\n" | |
| f"{instruction}\n\n" | |
| f"### Response: \n") | |
| result = pipe(input_prompt, max_length=200, top_p=0.9, temperature=0.9, num_return_sequences=1, return_full_text=False)[0]['generated_text'] | |
| return result[:str(result).find("###")] | |
| iface = gr.Interface(fn=generate_text, inputs=[gr.Textbox(label="Instruction"), | |
| gr.Textbox(label="Additional Input")], | |
| outputs=gr.Textbox(label="Response")) | |
| iface.launch() |