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| # -*- coding: utf-8 -*- | |
| """orca_mini_3b_T4_GPU.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/#fileId=https%3A//huggingface.co/psmathur/orca_mini_3b/blob/main/orca_mini_3b_T4_GPU.ipynb | |
| """ | |
| import torch | |
| from transformers import LlamaForCausalLM, LlamaTokenizer | |
| # Hugging Face model_path | |
| model_path = 'psmathur/orca_mini_3b' | |
| tokenizer = LlamaTokenizer.from_pretrained(model_path) | |
| model = LlamaForCausalLM.from_pretrained( | |
| model_path, torch_dtype=torch.float16, device_map='auto', | |
| ) | |
| #generate text function | |
| def predict(system, instruction, input=None): | |
| if input: | |
| prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" | |
| else: | |
| prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n" | |
| tokens = tokenizer.encode(prompt) | |
| tokens = torch.LongTensor(tokens).unsqueeze(0) | |
| tokens = tokens.to('cuda') | |
| instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50} | |
| length = len(tokens[0]) | |
| with torch.no_grad(): | |
| rest = model.generate( | |
| input_ids=tokens, | |
| max_length=length+instance['generate_len'], | |
| use_cache=True, | |
| do_sample=True, | |
| top_p=instance['top_p'], | |
| temperature=instance['temperature'], | |
| top_k=instance['top_k'] | |
| ) | |
| output = rest[0][length:] | |
| string = tokenizer.decode(output, skip_special_tokens=True) | |
| return f'[!] Response: {string}' | |
| import gradio as gr | |
| # Define input components | |
| prompt_input = gr.inputs.Textbox(label="System") | |
| instruction_input = gr.inputs.Textbox(label="Instruction") | |
| context_input = gr.inputs.Textbox(label="Context") | |
| # Define output component | |
| output_text = gr.outputs.Textbox(label="Output") | |
| # Create the interface | |
| gr.Interface(fn=predict, | |
| inputs=[prompt_input, instruction_input, context_input], | |
| outputs=output_text,enable_queue=True).launch(debug=True) |