release v0.1.0

Former-commit-id: 63c8d3a17cb18f0d8a8e37bfa147daf5bdd28ea9

src/web_demo.py

@@ -1,95 +0,0 @@
-# coding=utf-8
-# Implements user interface in browser for fine-tuned models.
-# Usage: python web_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-
-import gradio as gr
-from threading import Thread
-from transformers import TextIteratorStreamer
-from transformers.utils.versions import require_version
-
-from llmtuner import Template, get_infer_args, load_model_and_tokenizer, get_logits_processor
-
-
-require_version("gradio>=3.30.0", "To fix: pip install gradio>=3.30.0")
-
-
-model_args, data_args, finetuning_args, generating_args = get_infer_args()
-model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-
-prompt_template = Template(data_args.prompt_template)
-source_prefix = data_args.source_prefix if data_args.source_prefix else ""
-
-
-def predict(query, chatbot, max_new_tokens, top_p, temperature, history):
-    chatbot.append((query, ""))
-
-    input_ids = tokenizer([prompt_template.get_prompt(query, history, source_prefix)], return_tensors="pt")["input_ids"]
-    input_ids = input_ids.to(model.device)
-
-    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
-
-    gen_kwargs = generating_args.to_dict()
-    gen_kwargs.update({
-        "input_ids": input_ids,
-        "top_p": top_p,
-        "temperature": temperature,
-        "max_new_tokens": max_new_tokens,
-        "logits_processor": get_logits_processor(),
-        "streamer": streamer
-    })
-
-    thread = Thread(target=model.generate, kwargs=gen_kwargs)
-    thread.start()
-
-    response = ""
-    for new_text in streamer:
-        response += new_text
-        new_history = history + [(query, response)]
-        chatbot[-1] = (query, response)
-        yield chatbot, new_history
-
-
-def reset_user_input():
-    return gr.update(value="")
-
-
-def reset_state():
-    return [], []
-
-
-with gr.Blocks() as demo:
-
-    gr.HTML("""
-    <h1 align="center">
-        <a href="https://github.com/hiyouga/LLaMA-Efficient-Tuning" target="_blank">
-            LLaMA Efficient Tuning
-        </a>
-    </h1>
-    """)
-
-    chatbot = gr.Chatbot()
-
-    with gr.Row():
-        with gr.Column(scale=4):
-            with gr.Column(scale=12):
-                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(container=False)
-            with gr.Column(min_width=32, scale=1):
-                submitBtn = gr.Button("Submit", variant="primary")
-
-        with gr.Column(scale=1):
-            emptyBtn = gr.Button("Clear History")
-            max_new_tokens = gr.Slider(10, 2048, value=generating_args.max_new_tokens, step=1.0,
-                                       label="Maximum new tokens", interactive=True)
-            top_p = gr.Slider(0.01, 1, value=generating_args.top_p, step=0.01,
-                              label="Top P", interactive=True)
-            temperature = gr.Slider(0.01, 1.5, value=generating_args.temperature, step=0.01,
-                                    label="Temperature", interactive=True)
-
-    history = gr.State([])
-
-    submitBtn.click(predict, [user_input, chatbot, max_new_tokens, top_p, temperature, history], [chatbot, history], show_progress=True)
-    submitBtn.click(reset_user_input, [], [user_input])
-
-    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
-
-demo.queue().launch(server_name="0.0.0.0", share=True, inbrowser=True)