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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() | |