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import argparse
import gradio as gr
from llama2_wrapper import LLAMA2_WRAPPER
FIM_PREFIX = "<PRE> "
FIM_MIDDLE = " <MID>"
FIM_SUFFIX = " <SUF>"
FIM_INDICATOR = "<FILL_ME>"
EOS_STRING = "</s>"
EOT_STRING = "<EOT>"
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
default="./models/codellama-7b-instruct.ggmlv3.Q4_0.bin",
help="model path",
)
parser.add_argument(
"--backend_type",
type=str,
default="llama.cpp",
help="Backend options: llama.cpp, gptq, transformers",
)
parser.add_argument(
"--max_tokens",
type=int,
default=4000,
help="Maximum context size.",
)
parser.add_argument(
"--load_in_8bit",
type=bool,
default=False,
help="Whether to use bitsandbytes 8 bit.",
)
parser.add_argument(
"--share",
type=bool,
default=False,
help="Whether to share public for gradio.",
)
args = parser.parse_args()
llama2_wrapper = LLAMA2_WRAPPER(
model_path=args.model_path,
backend_type=args.backend_type,
max_tokens=args.max_tokens,
load_in_8bit=args.load_in_8bit,
)
def generate(
prompt,
temperature=0.9,
max_new_tokens=256,
top_p=0.95,
repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
fim_mode = False
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
stream=True,
)
if FIM_INDICATOR in prompt:
fim_mode = True
try:
prefix, suffix = prompt.split(FIM_INDICATOR)
except:
raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"
stream = llama2_wrapper.__call__(prompt, **generate_kwargs)
if fim_mode:
output = prefix
else:
output = prompt
# for response in stream:
# output += response
# yield output
# return output
previous_token = ""
for response in stream:
if any([end_token in response for end_token in [EOS_STRING, EOT_STRING]]):
if fim_mode:
output += suffix
yield output
return output
print("output", output)
else:
return output
else:
output += response
previous_token = response
yield output
return output
examples = [
'def remove_non_ascii(s: str) -> str:\n """ <FILL_ME>\nprint(remove_non_ascii(\'afkdj$$(\'))',
"X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
"// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
"Poor English: She no went to the market. Corrected English:",
"def alternating(list1, list2):\n results = []\n for i in range(min(len(list1), len(list2))):\n results.append(list1[i])\n results.append(list2[i])\n if len(list1) > len(list2):\n <FILL_ME>\n else:\n results.extend(list2[i+1:])\n return results",
]
def process_example(args):
for x in generate(args):
pass
return x
description = """
<div style="text-align: center;">
<h1>Code Llama Playground</h1>
</div>
<div style="text-align: center;">
<p>This is a demo to complete code with Code Llama. For instruction purposes, please use llama2-webui app.py with CodeLlama-Instruct models. </p>
</div>
"""
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown(description)
with gr.Row():
with gr.Column():
instruction = gr.Textbox(
placeholder="Enter your code here",
lines=5,
label="Input",
elem_id="q-input",
)
submit = gr.Button("Generate", variant="primary")
output = gr.Code(elem_id="q-output", lines=30, label="Output")
with gr.Row():
with gr.Column():
with gr.Accordion("Advanced settings", open=False):
with gr.Row():
column_1, column_2 = gr.Column(), gr.Column()
with column_1:
temperature = gr.Slider(
label="Temperature",
value=0.1,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
)
max_new_tokens = gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=8192,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
)
with column_2:
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
)
repetition_penalty = gr.Slider(
label="Repetition penalty",
value=1.05,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
gr.Examples(
examples=examples,
inputs=[instruction],
cache_examples=False,
fn=process_example,
outputs=[output],
)
submit.click(
generate,
inputs=[
instruction,
temperature,
max_new_tokens,
top_p,
repetition_penalty,
],
outputs=[output],
)
demo.queue(concurrency_count=16).launch(share=args.share)
if __name__ == "__main__":
main()
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