dev

app.py

@@ -28,9 +28,8 @@ model_save_path = '/data/lora_finetuned_model'  # Specify your desired save path
 model.save_pretrained(model_save_path)
 '''
 
-def fine_tune_model(dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad):
-    try:
-        model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh8")
+def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad):
+    try:        
         torch.cuda.empty_cache()
         torch.nn.CrossEntropyLoss()
         #rouge_metric = evaluate.load("rouge", cache_dir='/data/cache')
@@ -301,10 +300,7 @@ def run_train(dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad):
                 torch.nn.init.xavier_uniform_(param.data)  # Xavier initialization
             elif 'encoder.block.0.layer.0.DenseReluDense.wo.weight' in name:  # Another example layer
                 torch.nn.init.kaiming_normal_(param.data)  # Kaiming initialization
-    
-    config = AutoConfig.from_pretrained("google/t5-efficient-tiny-nh8")
-    model = AutoModelForSeq2SeqLM.from_config(config)
-    initialize_weights(model)
+    model = AutoModelForSeq2SeqLM.from_pretrained("tarekziade/wikipedia-summaries-t5-efficient-tiny")
     lora_config = LoraConfig(
         r=4,  # Rank of the low-rank adaptation
         lora_alpha=8,  # Scaling factor
@@ -335,7 +331,7 @@ except Exception as e:
 # Create Gradio interface
 try:
     iface = gr.Interface(
-        fn=fine_tune_model,
+        fn=run_train,
         inputs=[
             gr.Textbox(label="Dataset Name (e.g., 'imdb')"),
             gr.Textbox(label="HF hub to push to after training"),

gradio-3.wpu

@@ -326,7 +326,7 @@ guimgr.overall-gui-state = {'windowing-policy': 'combined-window',
                                        'fRegexFlags': 42,
                                        'fReplaceText': 'add_knowledge_base_to_vector_store',
                                        'fReverse': False,
-                                       'fSearchText': 'lf.app.aupdate_stat',
+                                       'fSearchText': 'readme',
                                        'fStartPos': 0,
                                        'fStyle': 'wildcard',
                                        'fWholeWords': False,
@@ -425,311 +425,290 @@ guimgr.overall-gui-state = {'windowing-policy': 'combined-window',
                       'wide',
                       1,
                       {})],
-        'primary_view_state': {'editor_states': ({'bookmarks': ([[loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py'),
-        {'attrib-starts': [('infer_framework_load_model|0|',
-                            205)],
-         'code-line': '        raise RuntimeError(\n',
-         'first-line': 226,
+        'primary_view_state': {'editor_states': ({'bookmarks': ([[loc('selenium_click.py'),
+        {'attrib-starts': [('test|0|',
+                            2)],
+         'code-line': "    model_name = 'Baicai003/tiny-t5'\n",
+         'first-line': 0,
          'folded-linenos': [],
-         'sel-line': 239,
-         'sel-line-start': 9148,
-         'selection_end': 9148,
-         'selection_start': 9148,
+         'sel-line': 3,
+         'sel-line-start': 55,
+         'selection_end': 90,
+         'selection_start': 90,
          'zoom': 0},
-        1733890299.157337],
+        1733969719.994683],
+        [loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/tokenization_utils_base.py'),
+         {'attrib-starts': [('PreTrainedTokenizerBase|0|',
+                             1379),
+                            ('PreTrainedTokenizerBase|0|.from_pretrained|0|',
+                             1794)],
+          'code-line': '            raise EnvironmentError(\n',
+          'first-line': 1991,
+          'folded-linenos': [],
+          'sel-line': 2015,
+          'sel-line-start': 101653,
+          'selection_end': 101653,
+          'selection_start': 101653,
+          'zoom': 0},
+         1733969734.6770854],
         [loc('selenium_click.py'),
-         {'attrib-starts': [],
-          'code-line': 'def test(text):    \n',
+         {'attrib-starts': [('test|0|',
+                             2)],
+          'code-line': "    model_name = 'Baicai003/tiny-t5'\n",
           'first-line': 0,
           'folded-linenos': [],
-          'sel-line': 2,
-          'sel-line-start': 35,
-          'selection_end': 50,
-          'selection_start': 50,
+          'sel-line': 3,
+          'sel-line-start': 55,
+          'selection_end': 91,
+          'selection_start': 91,
           'zoom': 0},
-         1733890425.432772],
+         1733969741.5539677],
         [loc('app.py'),
-         {'attrib-starts': [('run_train|0|',
-                             286)],
-          'code-line': '    config = AutoConfig.from_pretrained("google/t5-efficient-tiny-nh8")\n',
-          'first-line': 273,
-          'folded-linenos': [],
-          'sel-line': 294,
-          'sel-line-start': 13525,
-          'selection_end': 13594,
-          'selection_start': 13566,
-          'zoom': 0},
-         1733890482.8022997],
-        [loc('selenium_click.py'),
-         {'attrib-starts': [],
-          'code-line': 'def test(text):    \n',
-          'first-line': 0,
+         {'attrib-starts': [('fine_tune_model|0|',
+                             30)],
+          'code-line': '        model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh8")\n',
+          'first-line': 241,
           'folded-linenos': [],
-          'sel-line': 2,
-          'sel-line-start': 35,
-          'selection_end': 50,
-          'selection_start': 50,
+          'sel-line': 32,
+          'sel-line-start': 1129,
+          'selection_end': 1213,
+          'selection_start': 1213,
           'zoom': 0},
-         1733890703.8402333],
+         1733969749.87093],
         [loc('app.py'),
          {'attrib-starts': [('run_train|0|',
-                             286)],
-          'code-line': '        lora_alpha=8,  # Scaling factor\n',
-          'first-line': 285,
+                             296)],
+          'code-line': '    config = AutoConfig.from_pretrained("google/t5-efficient-tiny-nh8")\n',
+          'first-line': 298,
           'folded-linenos': [],
-          'sel-line': 299,
-          'sel-line-start': 13759,
-          'selection_end': 13779,
-          'selection_start': 13779,
+          'sel-line': 304,
+          'sel-line-start': 13927,
+          'selection_end': 13996,
+          'selection_start': 13968,
           'zoom': 0},
-         1733890990.4531481],
+         1733969751.1447525],
         [loc('selenium_click.py'),
-         {'attrib-starts': [],
-          'code-line': 'def test(text):    \n',
+         {'attrib-starts': [('test|0|',
+                             2)],
+          'code-line': "    model_name = 'huseinzol05/abstractive-summarization-v2-tiny-t5-quantized'\n",
           'first-line': 0,
           'folded-linenos': [],
-          'sel-line': 2,
-          'sel-line-start': 35,
-          'selection_end': 50,
-          'selection_start': 50,
+          'sel-line': 3,
+          'sel-line-start': 55,
+          'selection_end': 131,
+          'selection_start': 131,
           'zoom': 0},
-         1733890992.9117355],
-        [loc('app.py'),
-         {'attrib-starts': [('run_train|0|',
-                             286)],
-          'code-line': '        lora_alpha=8,  # Scaling factor\n',
-          'first-line': 285,
+         1733970908.1719747],
+        [loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/tokenization_utils_base.py'),
+         {'attrib-starts': [('PreTrainedTokenizerBase|0|',
+                             1379),
+                            ('PreTrainedTokenizerBase|0|.from_pretrained|0|',
+                             1794)],
+          'code-line': '            raise EnvironmentError(\n',
+          'first-line': 2001,
           'folded-linenos': [],
-          'sel-line': 299,
-          'sel-line-start': 13759,
-          'selection_end': 13779,
-          'selection_start': 13779,
+          'sel-line': 2015,
+          'sel-line-start': 101653,
+          'selection_end': 101653,
+          'selection_start': 101653,
           'zoom': 0},
-         1733891097.8497958],
-        [loc('app.py'),
-         {'attrib-starts': [('fine_tune_model|0|',
-                             30)],
-          'code-line': "                return 'TOKENS DONE'\n",
-          'first-line': 153,
+         1733970913.9058852],
+        [loc('selenium_click.py'),
+         {'attrib-starts': [('test|0|',
+                             2)],
+          'code-line': "        tokenizer='google/t5-efficient-tiny',\n",
+          'first-line': 0,
           'folded-linenos': [],
-          'sel-line': 162,
-          'sel-line-start': 7058,
-          'selection_end': 7093,
-          'selection_start': 7093,
+          'sel-line': 7,
+          'sel-line-start': 211,
+          'selection_end': 254,
+          'selection_start': 254,
           'zoom': 0},
-         1733891492.4296558],
-        [loc('app.py'),
-         {'attrib-starts': [('fine_tune_model|0|',
-                             30)],
-          'code-line': '            #greater_is_better=True,\n',
-          'first-line': 66,
+         1733970939.6640677],
+        [loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py'),
+         {'attrib-starts': [('infer_framework_load_model|0|',
+                             205)],
+          'code-line': '            raise ValueError(\n',
+          'first-line': 279,
           'folded-linenos': [],
-          'sel-line': 82,
-          'sel-line-start': 3317,
-          'selection_end': 3330,
-          'selection_start': 3330,
+          'sel-line': 301,
+          'sel-line-start': 12058,
+          'selection_end': 12058,
+          'selection_start': 12058,
           'zoom': 0},
-         1733913275.840877],
+         1733970951.1496227],
         [loc('selenium_click.py'),
-         {'attrib-starts': [],
-          'code-line': 'def test(text):    \n',
+         {'attrib-starts': [('test|0|',
+                             2)],
+          'code-line': "    model_name = 'tarekziade/wikipedia-summaries-t5-efficient-tiny'\n",
           'first-line': 0,
           'folded-linenos': [],
-          'sel-line': 2,
-          'sel-line-start': 35,
-          'selection_end': 50,
-          'selection_start': 50,
+          'sel-line': 3,
+          'sel-line-start': 55,
+          'selection_end': 121,
+          'selection_start': 73,
           'zoom': 0},
-         1733913307.3307884],
-        [loc('app.py'),
-         {'attrib-starts': [('fine_tune_model|0|',
-                             30)],
-          'code-line': '            #greater_is_better=True,\n',
-          'first-line': 66,
+         1733971918.209765],
+        [loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py'),
+         {'attrib-starts': [('infer_framework_load_model|0|',
+                             205)],
+          'code-line': '            raise ValueError(\n',
+          'first-line': 279,
           'folded-linenos': [],
-          'sel-line': 82,
-          'sel-line-start': 3317,
-          'selection_end': 3330,
-          'selection_start': 3330,
+          'sel-line': 301,
+          'sel-line-start': 12058,
+          'selection_end': 12058,
+          'selection_start': 12058,
           'zoom': 0},
-         1733913312.928603],
+         1733971919.070313],
         [loc('app.py'),
          {'attrib-starts': [('run_train|0|',
-                             286)],
-          'code-line': '    #model = get_peft_model(model, lora_config)\n',
-          'first-line': 289,
+                             296)],
+          'code-line': '    config = AutoConfig.from_pretrained("google/t5-efficient-tiny-nh8")\n',
+          'first-line': 298,
           'folded-linenos': [],
-          'sel-line': 303,
-          'sel-line-start': 13896,
-          'selection_end': 13901,
-          'selection_start': 13901,
+          'sel-line': 304,
+          'sel-line-start': 13927,
+          'selection_end': 13996,
+          'selection_start': 13968,
           'zoom': 0},
-         1733913335.5815701],
-        [loc('selenium_click.py'),
+         1733971926.4038842],
+        [loc('app.py'),
          {'attrib-starts': [],
-          'code-line': "pp(test(''' We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased before considering any other forms of uniform scaling across other dimensions. This is largely due to how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, a tall base model might also generally more efficient compared to a large model. We generally find that, regardless of size, even if absolute performance might increase as we continue to stack layers, the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to consi",
-          'first-line': 0,
+          'code-line': 'def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad):\n',
+          'first-line': 21,
           'folded-linenos': [],
-          'sel-line': 18,
-          'sel-line-start': 456,
-          'selection_end': 1486,
-          'selection_start': 1486,
+          'sel-line': 30,
+          'sel-line-start': 1034,
+          'selection_end': 1061,
+          'selection_start': 1061,
           'zoom': 0},
-         1733913348.8564975],
-        [loc('selenium_click.py'),
-         {'attrib-starts': [('test|0|',
-                             2)],
-          'code-line': '    )        \n',
-          'first-line': 0,
+         1733971946.8028426],
+        [loc('app.py'),
+         {'attrib-starts': [('run_train|0|',
+                             295)],
+          'code-line': '    model = AutoModelForSeq2SeqLM.from_pretrained("tarekziade/wikipedia-summaries-t5-efficient-tiny")\n',
+          'first-line': 295,
           'folded-linenos': [],
-          'sel-line': 10,
-          'sel-line-start': 309,
-          'selection_end': 322,
-          'selection_start': 322,
+          'sel-line': 303,
+          'sel-line-start': 13856,
+          'selection_end': 13897,
+          'selection_start': 13897,
           'zoom': 0},
-         1733913374.2822852],
-        [loc('../../envs/gradio-3/lib/python3.12/site-packages/torch/nn/modules/module.py'),
-         {'attrib-starts': [('Module|0|',
-                             398),
-                            ('Module|0|._call_impl|0|',
-                             1739)],
-          'code-line': '            return forward_call(*args, **kwargs)\n',
-          'first-line': 1723,
+         1733971973.3178246],
+        [loc('app.py'),
+         {'attrib-starts': [('fine_tune_model|0|',
+                             30)],
+          'code-line': "        tokenizer = AutoTokenizer.from_pretrained('google/t5-efficient-tiny', use_fast=True, trust_remote_code=True)\n",
+          'first-line': 89,
           'folded-linenos': [],
-          'sel-line': 1746,
-          'sel-line-start': 69769,
-          'selection_end': 69769,
-          'selection_start': 69769,
+          'sel-line': 96,
+          'sel-line-start': 3967,
+          'selection_end': 4042,
+          'selection_start': 4042,
           'zoom': 0},
-         1733913382.1839955],
-        [loc('selenium_click.py'),
-         {'attrib-starts': [('test|0|',
-                             2)],
-          'code-line': "        cache_dir='./cache',\n",
-          'first-line': 0,
+         1733971982.8726587],
+        [loc('app.py'),
+         {'attrib-starts': [('fine_tune_model|0|',
+                             30)],
+          'code-line': "        tokenizer = AutoTokenizer.from_pretrained('google/t5-efficient-tiny', use_fast=True, trust_remote_code=True)\n",
+          'first-line': 130,
           'folded-linenos': [],
-          'sel-line': 9,
-          'sel-line-start': 268,
-          'selection_end': 296,
-          'selection_start': 296,
+          'sel-line': 96,
+          'sel-line-start': 3967,
+          'selection_end': 4042,
+          'selection_start': 4042,
           'zoom': 0},
-         1733913397.7313583],
-        [loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/generation/utils.py'),
-         {'attrib-starts': [('GenerationMixin|0|',
-                             331),
-                            ('GenerationMixin|0|._validate_model_kwargs|0|',
-                             1332)],
-          'code-line': '            raise ValueError(\n',
-          'first-line': 1364,
+         1733971990.8933518],
+        [loc('app.py'),
+         {'attrib-starts': [('fine_tune_model|0|',
+                             30)],
+          'code-line': "        tokenizer = AutoTokenizer.from_pretrained('google/t5-efficient-tiny-nh8', use_fast=True, trust_remote_code=True)\n",
+          'first-line': 256,
           'folded-linenos': [],
-          'sel-line': 1387,
-          'sel-line-start': 74204,
-          'selection_end': 74204,
-          'selection_start': 74204,
+          'sel-line': 97,
+          'sel-line-start': 4038,
+          'selection_end': 4042,
+          'selection_start': 4042,
           'zoom': 0},
-         1733913398.4591272],
-        [loc('../tidal_slides_com/src/ts_ai.py'),
-         {'attrib-starts': [],
-          'code-line': '#kevin fink\n',
-          'first-line': 0,
+         1733972563.3713217],
+        [loc('app.py'),
+         {'attrib-starts': [('run_train|0|',
+                             296)],
+          'code-line': '    #model = get_peft_model(model, lora_config)\n',
+          'first-line': 292,
           'folded-linenos': [],
-          'sel-line': 0,
-          'sel-line-start': 0,
-          'selection_end': 0,
-          'selection_start': 0,
+          'sel-line': 313,
+          'sel-line-start': 14297,
+          'selection_end': 14344,
+          'selection_start': 14344,
           'zoom': 0},
-         1733913400.5161366],
-        [loc('../tidal_slides_com/src/ts_main.py'),
-         {'attrib-starts': [],
-          'code-line': '#kevin fink\n',
-          'first-line': 0,
+         1733972569.8712249],
+        [loc('app.py'),
+         {'attrib-starts': [('fine_tune_model|0|',
+                             30)],
+          'code-line': '    try:        \n',
+          'first-line': 28,
           'folded-linenos': [],
-          'sel-line': 0,
-          'sel-line-start': 0,
-          'selection_end': 0,
-          'selection_start': 0,
+          'sel-line': 31,
+          'sel-line-start': 1120,
+          'selection_end': 1128,
+          'selection_start': 1128,
           'zoom': 0},
-         1733913403.929367],
-        [loc('../tidal_slides_com/src/ts_main.py'),
-         {'attrib-starts': [('init_summarizer|0|',
-                             81)],
-          'code-line': "        model_kwargs={'cache_dir': summary_model_fp}\n",
-          'first-line': 81,
+         1733972581.3592975],
+        [loc('app.py'),
+         {'attrib-starts': [('run_train|0|',
+                             295)],
+          'code-line': '    model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh8")\n',
+          'first-line': 289,
           'folded-linenos': [],
-          'sel-line': 87,
-          'sel-line-start': 2233,
-          'selection_end': 2285,
-          'selection_start': 2241,
+          'sel-line': 304,
+          'sel-line-start': 13921,
+          'selection_end': 14002,
+          'selection_start': 14002,
           'zoom': 0},
-         1733913404.9677553]],
+         1733972584.572358]],
         20),
         'current-loc': loc('selenium_click.py'),
         'editor-state-list': [(loc('app.py'),
                                {'attrib-starts': [('run_train|0|',
-        286)],
-                                'code-line': '    #model = get_peft_model(model, lora_config)\n',
+        295)],
+                                'code-line': '    model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh8")\n',
                                 'first-line': 289,
                                 'folded-linenos': [],
-                                'sel-line': 303,
-                                'sel-line-start': 13896,
-                                'selection_end': 13901,
-                                'selection_start': 13901,
+                                'sel-line': 304,
+                                'sel-line-start': 13921,
+                                'selection_end': 14002,
+                                'selection_start': 14002,
+                                'zoom': 0}),
+                              (loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py'),
+                               {'attrib-starts': [('infer_framework_load_model|0|',
+        205)],
+                                'code-line': '            raise ValueError(\n',
+                                'first-line': 279,
+                                'folded-linenos': [],
+                                'sel-line': 301,
+                                'sel-line-start': 12058,
+                                'selection_end': 12058,
+                                'selection_start': 12058,
                                 'zoom': 0}),
                               (loc('selenium_click.py'),
                                {'attrib-starts': [('test|0|',
         2)],
-                                'code-line': "        model_kwargs={'cache_dir': './cache'}\n",
-                                'first-line': 0,
-                                'folded-linenos': [],
-                                'sel-line': 9,
-                                'sel-line-start': 268,
-                                'selection_end': 311,
-                                'selection_start': 311,
-                                'zoom': 0}),
-                              (loc('../tidal_slides_com/src/ts_ai.py'),
-                               {'attrib-starts': [],
-                                'code-line': '#kevin fink\n',
+                                'code-line': '        model=model_name,\n',
                                 'first-line': 0,
                                 'folded-linenos': [],
-                                'sel-line': 0,
-                                'sel-line-start': 0,
-                                'selection_end': 0,
-                                'selection_start': 0,
-                                'zoom': 0}),
-                              (loc('../tidal_slides_com/src/ts_main.py'),
-                               {'attrib-starts': [('init_summarizer|0|',
-        81)],
-                                'code-line': "        model_kwargs={'cache_dir': summary_model_fp}\n",
-                                'first-line': 81,
-                                'folded-linenos': [],
-                                'sel-line': 87,
-                                'sel-line-start': 2233,
-                                'selection_end': 2285,
-                                'selection_start': 2241,
-                                'zoom': 0}),
-                              (loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/generation/utils.py'),
-                               {'attrib-starts': [('GenerationMixin|0|',
-        331),
-        ('GenerationMixin|0|._validate_model_kwargs|0|',
-         1332)],
-                                'code-line': '            raise ValueError(\n',
-                                'first-line': 1364,
-                                'folded-linenos': [],
-                                'sel-line': 1387,
-                                'sel-line-start': 74204,
-                                'selection_end': 74204,
-                                'selection_start': 74204,
+                                'sel-line': 6,
+                                'sel-line-start': 175,
+                                'selection_end': 200,
+                                'selection_start': 200,
                                 'zoom': 0})],
         'has-focus': True,
         'locked': False},
         [loc('app.py'),
-         loc('selenium_click.py'),
-         loc('../tidal_slides_com/src/ts_ai.py'),
-         loc('../tidal_slides_com/src/ts_main.py'),
-         loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/generation/utils.py')]),
+         loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py'),
+         loc('selenium_click.py')]),
                                'open_files': ['app.py',
-        '../tidal_slides_com/src/ts_ai.py',
-        '../tidal_slides_com/src/ts_main.py',
         'selenium_click.py']},
         'saved_notebook_display': None,
         'split_percents': {0: 0.4410585404971933,
@@ -739,7 +718,7 @@ guimgr.overall-gui-state = {'windowing-policy': 'combined-window',
         'tab_location': 'top',
         'traversal_pos': ((1,
                            1),
-                          1733913397.6458473),
+                          1733970939.6550887),
         'user_data': {}},
                  'saved_notebook_display': None,
                  'split_percents': {0: 0.49743589743589745},
@@ -747,17 +726,15 @@ guimgr.overall-gui-state = {'windowing-policy': 'combined-window',
                  'tab_location': 'left',
                  'traversal_pos': ((1,
                                     0),
-                                   1733913397.6374698),
+                                   1733971910.3712847),
                  'user_data': {}},
         'window-alloc': (0,
                          0,
                          1920,
-                         823)}]}
+                         838)}]}
 guimgr.recent-documents = [loc('selenium_click.py'),
-                           loc('../tidal_slides_com/src/ts_main.py'),
-                           loc('../tidal_slides_com/src/ts_ai.py'),
-                           loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/generation/utils.py'),
-                           loc('app.py')]
+                           loc('app.py'),
+                           loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py')]
 guimgr.visual-state = {loc('../../envs/gradio-3/lib/python3.12/site-packages/selenium/webdriver/remote/errorhandler.py'): {'attrib-starts': [('ErrorHandler|0|',
         140),
         ('ErrorHandler|0|.check_response|0|',
@@ -796,13 +773,35 @@ guimgr.visual-state = {loc('../../envs/gradio-3/lib/python3.12/site-packages/sel
         'zoom': 0},
                        loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/pipelines/base.py'): {'attrib-starts': [('infer_framework_load_model|0|',
         205)],
-        'code-line': '        raise RuntimeError(\n',
-        'first-line': 226,
+        'code-line': '            raise ValueError(\n',
+        'first-line': 277,
+        'folded-linenos': [],
+        'sel-line': 301,
+        'sel-line-start': 12058,
+        'selection_end': 12058,
+        'selection_start': 12058,
+        'zoom': 0},
+                       loc('../../envs/gradio-3/lib/python3.12/site-packages/transformers/tokenization_utils_base.py'): {'attrib-starts': [('PreTrainedTokenizerBase|0|',
+        1379),
+        ('PreTrainedTokenizerBase|0|.from_pretrained|0|',
+         1794)],
+        'code-line': '            raise EnvironmentError(\n',
+        'first-line': 2001,
+        'folded-linenos': [],
+        'sel-line': 2015,
+        'sel-line-start': 101653,
+        'selection_end': 101653,
+        'selection_start': 101653,
+        'zoom': 0},
+                       loc('../tidal_slides_com/src/ts_main.py'): {'attrib-starts': [('init_summarizer|0|',
+        81)],
+        'code-line': "        model_kwargs={'cache_dir': summary_model_fp}\n",
+        'first-line': 81,
         'folded-linenos': [],
-        'sel-line': 239,
-        'sel-line-start': 9148,
-        'selection_end': 9148,
-        'selection_start': 9148,
+        'sel-line': 87,
+        'sel-line-start': 2233,
+        'selection_end': 2285,
+        'selection_start': 2241,
         'zoom': 0},
                        loc('gradio-3.wpr'): {'attrib-starts': [],
         'code-line': '#!wing\n',
@@ -824,6 +823,7 @@ guimgr.visual-state = {loc('../../envs/gradio-3/lib/python3.12/site-packages/sel
         'zoom': 0}}
 proj.pyexec = {None: ('custom',
                       '/home/kevin/envs/gradio-3/bin/python3')}
+search.search-history = ['readme']
 testing.stored-results = (1,
                           [],
                           {})

selenium_click.py

@@ -1,11 +1,11 @@
 from transformers import pipeline
 
 def test(text):    
-    model_name = 'shorecode/t5-efficient-tiny-nh8-summarizer'
+    model_name = 'tarekziade/wikipedia-summaries-t5-efficient-tiny'
     summarizer = pipeline(
         "summarization",
         model=model_name,
-        tokenizer=model_name,
+        tokenizer='google/t5-efficient-tiny',
         clean_up_tokenization_spaces=True,
         model_kwargs={'cache_dir': './cache'}
     )