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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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HUGGING_FACE_USER_NAME = "indikamk" |
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model_name = "BLOOMZ_finetuned_Misconceptions" |
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peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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def make_inference(sentence): |
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batch = tokenizer(f"### INSTRUCTION\nBelow is a student response to a writen question about an electrical circuit. Please identify whether there is a sequential misconception. A sequential misconception in terms of electric circuits is one in which it is believed that elements that are further “downstream” from a source (such as R2 and R3 in the example circuit of Figure 1) “receive” current after elements closer to the source (R1 in the example circuit). With such a misconception, it is likely that a student will think that changes in R2 have no effect on the potential difference and current associated with R1 or Vs..\n\n### Sentence:\n{sentence}\n### Response:\n", return_tensors='pt') |
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with torch.cuda.amp.autocast(): |
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output_tokens = model.generate(**batch, max_new_tokens=200) |
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return tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
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if __name__ == "__main__": |
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import gradio as gr |
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gr.Interface( |
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make_inference, |
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[ |
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gr.inputs.Textbox(lines=2, label="Sentence"), |
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], |
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gr.outputs.Textbox(label="Response"), |
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title="MisconAI", |
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description="MisconAI is a tool the allows you to input a student response to a writen question about an electrical circuit. It will identify whether there is a sequential misconcepion", ).launch() |
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