Fix evaluate comment saving

modules/evaluate.py

@@ -24,6 +24,8 @@ past_evaluations = load_past_evaluations()
 
 
 def save_past_evaluations(df):
+    global past_evaluations
+    past_evaluations = df
     df.to_csv(Path('logs/evaluations.csv'), index=False)
 
 

modules/shared.py

@@ -123,6 +123,7 @@ parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized m
 parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
 parser.add_argument('--groupsize', type=int, default=-1, help='Group size.')
 parser.add_argument('--pre_layer', type=int, default=0, help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models.')
+parser.add_argument('--file-path', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
 parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.')
 parser.add_argument('--no-quant_attn', action='store_true', help='(triton) Disable quant attention. If you encounter incoherent results try disabling this.')
 parser.add_argument('--no-warmup_autotune', action='store_true', help='(triton) Disable warmup autotune.')

modules/training.py

@@ -112,7 +112,7 @@ def create_train_interface():
                 evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The raw text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.')
                 with gr.Row():
                     stride_length = gr.Slider(label='Stride', minimum=1, maximum=2048, value=512, step=1, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.')
-                    max_length = gr.Slider(label='max_length', minimum=1, maximum=8096, value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
+                    max_length = gr.Slider(label='max_length', minimum=0, maximum=8096, value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
 
                 with gr.Row():
                     start_current_evaluation = gr.Button("Evaluate loaded model")