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Fix evaluate comment saving

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oobabooga 2023-04-21 12:34:08 -03:00
parent 5e023ae64d
commit d46b9b7c50
3 changed files with 4 additions and 1 deletions
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2
modules/evaluate.py
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@ -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)

1
modules/shared.py
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@ -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.')

2
modules/training.py
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@ -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")