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| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| df = pd.read_csv("data.csv") # Columns are 'Prompt' and 'Target' | |
| model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it") | |
| model.cuda().eval().requires_grad_(False) | |
| tokenizer = AutoTokenizer.from_pretrained(model.name_or_path) | |
| # Get the activations for the dataset | |
| inputs = tokenizer(df.Prompt.tolist(), return_tensors="pt", padding=True).to("cuda") | |
| hidden_states = model(**inputs, output_hidden_states=True).hidden_states | |
| # Arrange hidden state as [sample, layers x hidden_dim], using only the last input token | |
| # This assumes tokenizer.padding_side is 'left' | |
| activations = np.stack( | |
| [layer[:, -1, :].cpu() for layer in hidden_states], | |
| axis=1, | |
| ).reshape(len(df), -1) | |
| # Save for later use | |
| # np.save("activations.npy", activations) | |
| # Split into train and test sets | |
| act_trn, act_tst, df_trn, df_tst = train_test_split( | |
| activations, | |
| df, | |
| stratify=df.Target, | |
| random_state=42, | |
| ) | |
| # Standardize (z-score) the activations, based only on training set mean/std | |
| act_trn_z = (act_trn - act_trn.mean(axis=0)) / act_trn.std(axis=0) | |
| act_tst_z = (act_tst - act_trn.mean(axis=0)) / act_trn.std(axis=0) | |
| # Compute the two centroids from the training set | |
| pos_centroid = act_trn_z[df_trn.Target].mean(axis=0) | |
| neg_centroid = act_trn_z[~df_trn.Target].mean(axis=0) | |
| dist_to_pos = np.linalg.norm(pos_centroid - act_tst_z, axis=1) | |
| dist_to_neg = np.linalg.norm(neg_centroid - act_tst_z, axis=1) | |
| df_tst["Score"] = dist_to_neg - dist_to_pos | |
| df_tst["Pred"] = dist_to_pos < dist_to_neg | |
| # The below 4 lines are equivalent to the distance comparison above, but much faster. | |
| # midpoint = (pos_centroid + neg_centroid) / 2 | |
| # direction = pos_centroid - neg_centroid | |
| # df_tst["Score"] = (act_tst_z - midpoint) @ direction | |
| # df_tst["Pred"] = df_tst.Score > 0 | |
| accuracy = df_tst.Target.eq(df_tst.Pred).mean() | |
| print(f"Test set accuracy: {accuracy:.1%}") |
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