Spaces:
Runtime error
Runtime error
| import torch | |
| from peft import PeftModel, PeftConfig | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| peft_model_id = "JuliaUpton/Math_AI" | |
| config = PeftConfig.from_pretrained(peft_model_id) | |
| model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False) | |
| tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) | |
| # Load the Lora model | |
| merged_model = PeftModel.from_pretrained(model, peft_model_id) | |
| def input_from_text(instruction): | |
| return f"<s>[INST]Below is a math inquiry, please answer it as a math expert showing your thought process.\n\n### Inquiry:\n{instruction}\n\n### Response:[/INST]" | |
| def make_inference(instruction): | |
| inputs = mixtral_tokenizer(input_from_text(instruction), return_tensors="pt") | |
| outputs = merged_model.generate( | |
| **inputs, | |
| max_new_tokens=150, | |
| generation_kwargs={"repetition_penalty" : 1.7} | |
| ) | |
| # print(mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| result = mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[1] | |
| return result | |
| if __name__ == "__main__": | |
| # make a gradio interface | |
| import gradio as gr | |
| gr.Interface( | |
| make_inference, | |
| [ | |
| gr.Textbox(lines=5, label="Instruction"), | |
| ], | |
| gr.Textbox(label="Answer"), | |
| title="Math-AI", | |
| description="Math-AI is a generative model that answers math questions", | |
| ).launch() | |