Delete cs772_proj

cs772_proj/bert_base/checkpoint-3848/config.json

@@ -1,37 +0,0 @@
-{
-  "_name_or_path": "bert-base-uncased",
-  "architectures": [
-    "BertForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "hate",
-    "1": "normal",
-    "2": "offense"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "hate": 0,
-    "normal": 1,
-    "offense": 2
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "problem_type": "single_label_classification",
-  "torch_dtype": "float32",
-  "transformers_version": "4.39.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}

cs772_proj/bert_base/checkpoint-3848/model.safetensors

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cs772_proj/bert_base/checkpoint-3848/optimizer.pt

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cs772_proj/bert_base/checkpoint-3848/rng_state.pth

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cs772_proj/bert_base/checkpoint-3848/scheduler.pt

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cs772_proj/bert_base/checkpoint-3848/special_tokens_map.json

@@ -1,7 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}

cs772_proj/bert_base/checkpoint-3848/tokenizer_config.json

@@ -1,55 +0,0 @@
-{
-  "added_tokens_decoder": {
-    "0": {
-      "content": "[PAD]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "100": {
-      "content": "[UNK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "101": {
-      "content": "[CLS]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "102": {
-      "content": "[SEP]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "103": {
-      "content": "[MASK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "model_max_length": 512,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]"
-}

cs772_proj/bert_base/checkpoint-3848/trainer_state.json

@@ -1,85 +0,0 @@
-{
-  "best_metric": 0.7247874736785889,
-  "best_model_checkpoint": "bert_base/checkpoint-1924",
-  "epoch": 4.0,
-  "eval_steps": 500,
-  "global_step": 3848,
-  "is_hyper_param_search": false,
-  "is_local_process_zero": true,
-  "is_world_process_zero": true,
-  "log_history": [
-    {
-      "epoch": 1.0,
-      "grad_norm": 13.14333724975586,
-      "learning_rate": 8.000000000000001e-06,
-      "loss": 0.8204,
-      "step": 962
-    },
-    {
-      "epoch": 1.0,
-      "eval_f1": 0.6669116037121897,
-      "eval_loss": 0.7362112402915955,
-      "eval_runtime": 2.6989,
-      "eval_samples_per_second": 712.147,
-      "eval_steps_per_second": 44.833,
-      "step": 962
-    },
-    {
-      "epoch": 2.0,
-      "grad_norm": 15.062124252319336,
-      "learning_rate": 6e-06,
-      "loss": 0.664,
-      "step": 1924
-    },
-    {
-      "epoch": 2.0,
-      "eval_f1": 0.6714560068474462,
-      "eval_loss": 0.7247874736785889,
-      "eval_runtime": 1.9229,
-      "eval_samples_per_second": 999.531,
-      "eval_steps_per_second": 62.926,
-      "step": 1924
-    },
-    {
-      "epoch": 3.0,
-      "grad_norm": 30.878219604492188,
-      "learning_rate": 4.000000000000001e-06,
-      "loss": 0.5662,
-      "step": 2886
-    },
-    {
-      "epoch": 3.0,
-      "eval_f1": 0.6630607481681304,
-      "eval_loss": 0.7806704044342041,
-      "eval_runtime": 2.4376,
-      "eval_samples_per_second": 788.47,
-      "eval_steps_per_second": 49.638,
-      "step": 2886
-    },
-    {
-      "epoch": 4.0,
-      "grad_norm": 21.18539810180664,
-      "learning_rate": 2.0000000000000003e-06,
-      "loss": 0.4919,
-      "step": 3848
-    },
-    {
-      "epoch": 4.0,
-      "eval_f1": 0.6878731135692044,
-      "eval_loss": 0.7753087282180786,
-      "eval_runtime": 2.3502,
-      "eval_samples_per_second": 817.816,
-      "eval_steps_per_second": 51.486,
-      "step": 3848
-    }
-  ],
-  "logging_steps": 500,
-  "max_steps": 4810,
-  "num_input_tokens_seen": 0,
-  "num_train_epochs": 5,
-  "save_steps": 500,
-  "total_flos": 1892113337449692.0,
-  "train_batch_size": 16,
-  "trial_name": null,
-  "trial_params": null
-}

cs772_proj/bert_base/checkpoint-3848/training_args.bin

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cs772_proj/cs772_bert/.gitattributes

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cs772_proj/cs772_bert/README.md

@@ -1,13 +0,0 @@
----
-title: Cs772 Bert
-emoji: 🌖
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 4.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

cs772_proj/demo_gradio_distilbert.py

@@ -1,372 +0,0 @@
-import gradio as gr
-
-import transformers 
-import torch
-#import neptune
-#from knockknock import slack_sender
-from transformers import *
-#import glob 
-from transformers import BertTokenizer
-from transformers import BertForSequenceClassification, AdamW, BertConfig
-import random
-import pandas as pd
-from transformers import BertTokenizer
-#from Models.utils import masked_cross_entropy,fix_the_random,format_time,save_normal_model,save_bert_model
-from sklearn.metrics import accuracy_score,f1_score
-from tqdm import tqdm
-'''from TensorDataset.datsetSplitter import createDatasetSplit
-from TensorDataset.dataLoader import combine_features
-from Preprocess.dataCollect import collect_data,set_name'''
-from sklearn.metrics import accuracy_score,f1_score,roc_auc_score,recall_score,precision_score
-import matplotlib.pyplot as plt
-import time
-import os
-from transformers import BertTokenizer
-#import GPUtil
-from sklearn.utils import class_weight
-#import json
-#from Models.bertModels import *
-#from Models.otherModels import *
-import sys
-#import time
-#from waiting import wait
-from sklearn.preprocessing import LabelEncoder
-import numpy as np
-#import threading
-#import argparse
-#import ast
-
-#from manual_training_inference import select_model
-#from Models.utils import save_normal_model,save_bert_model,load_model
-#from Models.utils import return_params
-from transformers import DistilBertTokenizer
-
-
-#from TensorDataset.dataLoader import custom_att_masks
-#from keras.preprocessing.sequence import pad_sequences
-
-#import seaborn as sns 
-import matplotlib.pyplot as plt 
-import numpy as np
-import PIL.Image as Image
-from torch import nn
-
-from pyvene import embed_to_distrib, top_vals, format_token
-from pyvene import (
-    IntervenableModel,
-    VanillaIntervention, Intervention,
-    RepresentationConfig,
-    IntervenableConfig,
-    ConstantSourceIntervention,
-    LocalistRepresentationIntervention
-)
-from pyvene import create_gpt2
-#%config InlineBackend.figure_formats = ['svg']
-from plotnine import (
-    ggplot,
-    geom_tile,
-    aes,
-    facet_wrap,
-    theme,
-    element_text,
-    geom_bar,
-    geom_hline,
-    scale_y_log10,
-    xlab, ylab, ylim,
-    scale_y_discrete, scale_y_continuous, ggsave
-)
-from plotnine.scales import scale_y_reverse, scale_fill_cmap
-from tqdm import tqdm
-global device
-device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-def create_bert(cache_dir=None):
-    """Creates a GPT2 model, config, and tokenizer from the given name and revision"""
-    from transformers import BertConfig
-
-    config = BertConfig.from_pretrained("./bert_base/checkpoint-3848/config.json")
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    gpt = AutoModelForSequenceClassification.from_pretrained("./bert_base/checkpoint-3848", config=config, cache_dir=cache_dir)
-    print("loaded model")
-    return config, tokenizer, gpt
-def interpret(text,label):
-            titles={
-            "block_output": "single restored layer in BERT",
-            "mlp_activation": "center of interval of 5 patched mlp layer",
-            "attention_output": "center of interval of 5 patched attn layer"
-        }
-
-            colors={
-            "block_output": "Purples",
-            "mlp_activation": "Greens",
-            "attention_output": "Reds"
-        } 
-         
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
-            #config, tokenizer, gpt =  pv.create_llama(name="sharpbai/alpaca-7b-merged")
-            config, tokenizer, gpt = create_bert()
-            #config, tokenizer, gpt = create_gpt2(name="gpt2-xl")
-
-            gpt.to(device)
-
-            base = text
-            inputs = [
-                tokenizer(base, return_tensors="pt").to(device),
-            ]
-            #print(base)
-            base_token = tokenizer.convert_ids_to_tokens(inputs[0]['input_ids'][0])
-            res = gpt(**inputs[0])
-            probabilities = nn.functional.softmax(res[0], dim=-1)
-            if label=="hate":
-                 l = 0
-            elif label=="normal":
-                 l=1
-            else:l=2
-            #print(probabilities)
-            #print(res[0][0][0].item())
-            #print(res)
-            #distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)
-            #top_vals(tokenizer, distrib[0][-1], n=20)
-            base = tokenizer(text, return_tensors="pt").to(device)
-            config = corrupted_config(type(gpt))
-            intervenable = IntervenableModel(config, gpt)
-            _, counterfactual_outputs = intervenable(
-                base, unit_locations={"base": ([[[0,1,2,3]]])}
-            )
-            #probabilities = nn.functional.softmax(counterfactual_outputs[0], dim=-1)
-            #print(probabilities)
-            for stream in ["block_output", "mlp_activation", "attention_output"]:
-                data = []
-                for layer_i in tqdm(range(gpt.config.num_hidden_layers)):
-                    for pos_i in range(len(base_token)):
-                        config = restore_corrupted_with_interval_config(
-                            layer_i, stream, 
-                            window=1 if stream == "block_output" else 5
-                        )
-                        
-                        n_restores = len(config.representations) - 1
-                        intervenable = IntervenableModel(config, gpt)
-                        _, counterfactual_outputs = intervenable(
-                            base,
-                            [None] + [base]*n_restores,
-                            {
-                                "sources->base": (
-                                    [None] + [[[pos_i]]]*n_restores,
-                                    [[[0,1,2,3]]] + [[[pos_i]]]*n_restores,
-                                )
-                            },
-                        )
-                        #distrib = embed_to_distrib(
-                            #gpt, counterfactual_outputs.last_hidden_state, logits=False
-                        #)
-                        #prob = distrib[0][-1][token].detach().cpu().item()
-                        logits = counterfactual_outputs[0]
-                        probabilities = nn.functional.softmax(logits, dim=-1)
-                        prob_offense = probabilities[0][l].item()
-                        data.append({"layer": layer_i, "pos": pos_i, "prob": prob_offense})
-                df = pd.DataFrame(data)
-                df.to_csv(f"./tutorial_data/pyvene_rome_{stream}.csv")
-            for stream in ["block_output", "mlp_activation", "attention_output"]:
-                    df = pd.read_csv(f"./tutorial_data/pyvene_rome_{stream}.csv")
-                    df["layer"] = df["layer"].astype(int)
-                    df["pos"] = df["pos"].astype(int)
-                    prob_type = "p"+"("+label+")"
-                    df[prob_type] = df["prob"].astype(float)
-                    #custom_labels = ["imagine*","the*", "riots*", "if", "people", "actually", "got" ,"food" ,"boxes" ,"instead", "of" ,"ebt", "cards", "every", "ghetto", "in", "america", "would" ,"look", "like", "ferguson"]
-                    custom_labels = base_token #["what*", "sort*", "of*", "white*","man" ,"or", "woman", "would", "vote", "for", "this", "nigger"]
-                    #custom_labels = ["no*", "liberal*","congratulated*", "hindu*", "refugees", "post", "cab", "because", "they", "hate", "hindus"]
-                    breaks = list(range(len(custom_labels)))#[0, 1, 2, 3, 4, 5, 6,7,8,9,10,11]
-
-
-                    plot = (
-                        ggplot(df, aes(x="layer", y="pos"))    
-
-                        + geom_tile(aes(fill=prob_type))
-                        + scale_fill_cmap(colors[stream]) + xlab(titles[stream])
-                        + scale_y_reverse(
-                            limits = (-0.5, len(custom_labels)), 
-                            breaks=breaks, labels=custom_labels) 
-                        + theme(figure_size=(6,9)) + ylab("") 
-                        + theme(axis_text_y  = element_text(angle = 90, hjust = 1))
-                    )
-                    ggsave(
-                        plot, filename=f"./tutorial_data/pyvene_rome_{stream}.png", dpi=200
-                    )
-                    if stream == "mlp_activation":
-                        mlp_img_path = f"./tutorial_data/pyvene_rome_{stream}.png"
-                    elif stream=="block_output":
-                        bo_path = f"./tutorial_data/pyvene_rome_{stream}.png"
-                    else:attention_path = f"./tutorial_data/pyvene_rome_{stream}.png"
-            return mlp_img_path,bo_path,attention_path
-
-def restore_corrupted_with_interval_config(
-    layer, stream="mlp_activation", window=5, num_layers=12):
-    start = max(0, layer - window // 2)
-    end = min(num_layers, layer - (-window // 2))
-    config = IntervenableConfig(
-        representations=[
-            RepresentationConfig(
-                0,       # layer
-                "block_input",  # intervention type
-            ),
-        ] + [
-            RepresentationConfig(
-                i,       # layer
-                stream,  # intervention type
-        ) for i in range(start, end)],
-        intervention_types=\
-            [NoiseIntervention]+[VanillaIntervention]*(end-start)
-    )
-    return config
-
-class NoiseIntervention(ConstantSourceIntervention, LocalistRepresentationIntervention):
-    def __init__(self, embed_dim, **kwargs):
-        super().__init__()
-        self.interchange_dim = embed_dim
-        rs = np.random.RandomState(1)
-        prng = lambda *shape: rs.randn(*shape)
-        self.noise = torch.from_numpy(
-            prng(1, 4, embed_dim)).to(device)
-        self.noise_level = 0.7462981581687927 #0.3462981581687927
-
-    def forward(self, base, source=None, subspaces=None):
-        base[..., : self.interchange_dim] += self.noise * self.noise_level
-        return base
-
-    def __str__(self):
-        return f"NoiseIntervention(embed_dim={self.embed_dim})"
-
-
-def corrupted_config(model_type):
-    config = IntervenableConfig(
-        model_type=model_type,
-        representations=[
-            RepresentationConfig(
-                0,              # layer
-                "block_input",  # intervention type
-            ),
-        ],
-        intervention_types=NoiseIntervention,
-    )
-    return config
-def create_bert(cache_dir=None):
-    """Creates a GPT2 model, config, and tokenizer from the given name and revision"""
-    from transformers import BertConfig
-
-    config = BertConfig.from_pretrained("./bert_base/checkpoint-3848/config.json")
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    gpt = AutoModelForSequenceClassification.from_pretrained("./bert_base/checkpoint-3848", config=config, cache_dir=cache_dir)
-    print("loaded model")
-    return config, tokenizer, gpt
-
-# params = return_params('best_model_json/distilbert.json', 0.001 )
-#params = return_params('best_model_json/distilbert.json', 1 )
-
-
-'''embeddings=None
-if(params['bert_tokens']):
-    train,val,test=createDatasetSplit(params)       #update
-else:
-    train,val,test,vocab_own=createDatasetSplit(params)
-    params['embed_size']=vocab_own.embeddings.shape[1]
-    params['vocab_size']=vocab_own.embeddings.shape[0]
-    embeddings=vocab_own.embeddings
-if(params['auto_weights']):
-    y_test = [ele[2] for ele in test] 
-#         print(y_test)
-    encoder = LabelEncoder()
-    encoder.classes_ = np.load(params['class_names'],allow_pickle=True)
-    params['weights']=class_weight.compute_class_weight('balanced',np.unique(y_test),y_test).astype('float32') 
-    #params['weights']=np.array([len(y_test)/y_test.count(encoder.classes_[0]),len(y_test)/y_test.count(encoder.classes_[1]),len(y_test)/y_test.count(encoder.classes_[2])]).astype('float32') 
-
-model=select_model(params,embeddings)
-model = model.eval()
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-
-
-classes_ = np.load('Data/classes.npy')
-'''
-def main_function(text,label):
-    '''tokens = tokenizer.encode_plus(text)
-    input_ids = pad_sequences(torch.tensor(tokens['input_ids']).unsqueeze(0),maxlen=int(params['max_length']),\
-                               dtype="long", 
-                          value=0, truncating="post", padding="post")
-    # att_vals = pad_sequences(att_vals,maxlen=int(params['max_length']), dtype="float", 
-    #                       value=0.0, truncating="post", padding="post")
-    att_masks=custom_att_masks(input_ids)
-
-    outs = model(torch.tensor(input_ids), 
-            attention_mask=torch.tensor(att_masks, dtype=bool), 
-            labels=None,
-            device='cuda')
-    
-    text_tokens = tokenizer.convert_ids_to_tokens(input_ids.squeeze())
-
-    text_tokens_ = text_tokens[:len(tokens['input_ids'])]
-
-    print ('xyz')
-    print (outs[1][5].shape)
-    avg_attn = torch.mean(outs[1][5], dim=1)
-    avg_attn_np = avg_attn[0,0,:len(tokens['input_ids'])].detach().squeeze().numpy()
-    
-    logits = outs[0]
-    print (logits)
-    print (np.sum(avg_attn_np))
-    print (avg_attn_np)
-
-    pred = torch.argmax(logits)
-    pred_label = classes_[pred]
-    '''
-    ml_img_path,bo_img_path,atten_img_path = interpret(text,label)
-    ml_im = Image.open(ml_img_path)
-    bo_im = Image.open(bo_img_path)
-    atten_im = Image.open(atten_img_path)
-
-    yield ml_im, bo_im, atten_im
-
-    '''
-    sns.set_theme(rc={'figure.figsize':(30,1)})
-  
-    # creating subplot 
-    fig, ax = plt.subplots() 
-    
-    # drawing heatmap on current axes 
-    ax = sns.heatmap(np.expand_dims(avg_attn_np,0), annot= np.expand_dims(np.array(text_tokens_),0), \
-                                            fmt="", annot_kws={'size': 10}, cmap="magma") 
-
-    fig = ax.get_figure()
-    fig.savefig("out.png" ,bbox_inches='tight')
-
-    im = Image.open("out.png")
-
-    yield im
-
-    '''
-
-    #return list(zip(text_tokens_ , avg_attn_np)), pred_label
-    # return list(zip(text_tokens_[1:-1] , avg_attn_np[1:-1])) 
-     
-
-demo = gr.Interface(main_function,
-                    inputs="textbox",
-                    outputs="image",
-                    theme = 'compact')
-
-with gr.Blocks() as demo:
-    with gr.Tab("Text Input"):
-        text_input = gr.Textbox()
-        label_input = gr.Textbox()
-        text_button = gr.Button("Show")
-
-    with gr.Tab("Interpretability"):
-         with gr.Row():
-              image_output1 = gr.Image()
-              image_output2 = gr.Image()
-              image_output3 = gr.Image()
-
-    text_button.click(main_function, inputs=[text_input,label_input], outputs=[image_output1,image_output2,image_output3])
-
-
-
-
-if __name__ == "__main__":
-    demo.launch(share=True)

cs772_proj/requirements.txt

@@ -1,123 +0,0 @@
-accelerate==0.29.2
-aiohttp==3.9.4
-aiosignal==1.3.1
-asttokens 
-async-timeout==4.0.3
-attrs==23.2.0
-backcall==0.2.0
-beautifulsoup4==4.12.3
-bleach==6.1.0
-certifi==2024.2.2
-charset-normalizer==3.3.2
-comm 
-contourpy==1.2.1
-cycler==0.12.1
-datasets==2.18.0
-debugpy
-decorator 
-defusedxml==0.7.1
-dill==0.3.8
-docopt==0.6.2
-exceptiongroup 
-executing 
-fastjsonschema==2.19.1
-filelock==3.13.4
-fonttools==4.51.0
-frozenlist==1.4.1
-fsspec==2023.6.0
-huggingface-hub==0.20.3
-idna==3.7
-importlib_metadata 
-ipykernel 
-ipython==8.12.3
-ipywidgets==8.1.2
-jedi
-Jinja2==3.1.3
-jsonschema==4.21.1
-jsonschema-specifications==2023.12.1
-jupyter_client
-jupyter_core 
-jupyterlab_pygments==0.3.0
-jupyterlab_widgets==3.0.10
-kiwisolver==1.4.5
-MarkupSafe==2.1.5
-matplotlib==3.8.4
-matplotlib-inline 
-mistune==3.0.2
-mizani==0.11.1
-mpmath==1.3.0
-multidict==6.0.5
-multiprocess==0.70.16
-nbclient==0.10.0
-nbconvert==7.16.3
-nbformat==5.10.4
-nest_asyncio 
-networkx==3.3
-numpy==1.26.4
-nvidia-cublas-cu12==12.1.3.1
-nvidia-cuda-cupti-cu12==12.1.105
-nvidia-cuda-nvrtc-cu12==12.1.105
-nvidia-cuda-runtime-cu12==12.1.105
-nvidia-cudnn-cu12==8.9.2.26
-nvidia-cufft-cu12==11.0.2.54
-nvidia-curand-cu12==10.3.2.106
-nvidia-cusolver-cu12==11.4.5.107
-nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.19.3
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.1.105
-packaging 
-pandas==2.2.2
-pandocfilters==1.5.1
-parso 
-patsy==0.5.6
-pexpect 
-pickleshare 
-pillow==10.3.0
-pipreqs==0.5.0
-platformdirs
-plotnine==0.13.4
-prompt-toolkit 
-protobuf==5.26.1
-psutil 
-ptyprocess 
-pure-eval 
-pyarrow==15.0.2
-pyarrow-hotfix==0.6
-Pygments 
-pyparsing==3.1.2
-python-dateutil==2.8.2
-pytz==2023.4
-pyvene==0.1.1
-PyYAML==6.0.1
-pyzmq 
-referencing==0.34.0
-regex==2023.12.25
-requests==2.31.0
-rpds-py==0.18.0
-safetensors==0.4.3
-scipy==1.13.0
-sentencepiece==0.2.0
-six 
-soupsieve==2.5
-stack-data 
-statsmodels==0.14.1
-sympy==1.12
-tinycss2==1.2.1
-tokenizers==0.15.2
-torch==2.2.2
-tornado 
-tqdm==4.66.2
-traitlets 
-transformers==4.39.3
-triton==2.2.0
-typing_extensions 
-tzdata==2024.1
-urllib3==2.0.7
-wcwidth 
-webencodings==0.5.1
-widgetsnbextension==4.0.10
-xxhash==3.4.1
-yarg==0.1.9
-yarl==1.9.4
-zipp 

cs772_proj/tutorial_data/pyvene_rome_attention_output.csv

@@ -1,73 +0,0 @@
-,layer,pos,prob
-0,0,0,0.008943566121160984
-1,0,1,0.010685674846172333
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-70,11,4,0.012925814837217331
-71,11,5,0.011390508152544498

cs772_proj/tutorial_data/pyvene_rome_block_output.csv

@@ -1,73 +0,0 @@
-,layer,pos,prob
-0,0,0,0.009189224801957607
-1,0,1,0.011389641091227531
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cs772_proj/tutorial_data/pyvene_rome_mlp_activation.csv

@@ -1,73 +0,0 @@
-,layer,pos,prob
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