Commit
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402ce86
1
Parent(s):
80c7823
Update app.py
Browse files
app.py
CHANGED
@@ -1,4 +1,3 @@
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import gradio as gr
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import gc, copy, re
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import urllib.request
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@@ -15,7 +14,7 @@ model = RWKV(model=title, strategy='cpu bf16')
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pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
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def generate_prompt(instruction, input=None, history=None):
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#
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history_str = ""
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for pair in history:
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history_str += f"Instruction: {pair[0]}\n\nAssistant: {pair[1]}\n\n"
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@@ -51,7 +50,7 @@ def evaluate(
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top_p=0.5,
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presencePenalty = 0.5,
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countPenalty = 0.5,
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history=None #
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):
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args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
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alpha_frequency = countPenalty,
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@@ -61,7 +60,7 @@ def evaluate(
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instruction = re.sub(r'\n{2,}', '\n', instruction).strip().replace('\r\n','\n')
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input = re.sub(r'\n{2,}', '\n', input).strip().replace('\r\n','\n')
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ctx = generate_prompt(instruction, input, history) #
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print(ctx + "\n")
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all_tokens = []
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@@ -119,23 +118,64 @@ with gr.Blocks(title=title) as demo:
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gr.Markdown(f"100% RNN RWKV-LM **trained on 100+ natural languages**. Demo limited to ctxlen {ctx_limit}. For best results, <b>keep your prompt short and clear</b>.")
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with gr.Row():
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with gr.Column():
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instruction = gr.Textbox(lines=2, label="Instruction", value=
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input = gr.Textbox(lines=2, label="Input", placeholder="")
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token_count = gr.Slider(10, 512, label="Max Tokens", step=10, value=333)
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temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.3)
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presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0)
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count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.7)
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data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
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data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
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with gr.Tab("Chat mode"):
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demo.queue(max_size=10)
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demo.launch(share=False)
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import gradio as gr
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import gc, copy, re
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import urllib.request
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pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
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def generate_prompt(instruction, input=None, history=None):
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# parse the chat history into a string of user and assistant messages
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history_str = ""
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for pair in history:
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history_str += f"Instruction: {pair[0]}\n\nAssistant: {pair[1]}\n\n"
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top_p=0.5,
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presencePenalty = 0.5,
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countPenalty = 0.5,
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history=None # add the history parameter to the evaluate function
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):
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args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
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alpha_frequency = countPenalty,
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instruction = re.sub(r'\n{2,}', '\n', instruction).strip().replace('\r\n','\n')
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input = re.sub(r'\n{2,}', '\n', input).strip().replace('\r\n','\n')
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ctx = generate_prompt(instruction, input, history) # pass the history to the generate_prompt function
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print(ctx + "\n")
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all_tokens = []
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gr.Markdown(f"100% RNN RWKV-LM **trained on 100+ natural languages**. Demo limited to ctxlen {ctx_limit}. For best results, <b>keep your prompt short and clear</b>.")
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with gr.Row():
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with gr.Column():
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instruction = gr.Textbox(lines=2, label="Instruction", value='東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。')
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input = gr.Textbox(lines=2, label="Input", placeholder="")
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token_count = gr.Slider(10, 512, label="Max Tokens", step=10, value=333)
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temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.3)
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presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0)
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count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.7)
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with gr.Column():
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with gr.Row():
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submit = gr.Button("Submit", variant="primary")
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clear = gr.Button("Clear", variant="secondary")
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output = gr.Textbox(label="Output", lines=5)
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data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
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submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty, []], [output])
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clear.click(lambda: None, [], [output])
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data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
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with gr.Tab("Chat mode"):
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with gr.Row():
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chatbot = gr.Chatbot()
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with gr.Column():
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msg = gr.Textbox(scale=4, show_label=False, placeholder="Enter text and press enter", container=False)
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clear = gr.Button("Clear")
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with gr.Column():
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token_count = gr.Slider(10, 512, label="Max Tokens", step=10, value=333)
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temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.3)
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presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0)
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count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.7)
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def clear_chat():
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return "", []
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def user_msg(message, history):
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history = history or []
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return "", history + [[message, None]]
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def chat(history):
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# get the last user message and the additional parameters
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message = history[-1][0]
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instruction = msg.value
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token_count = token_count.value
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temperature = temperature.value
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top_p = top_p.value
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presence_penalty = presence_penalty.value
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count_penalty = count_penalty.value
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response = evaluate(instruction, None, token_count, temperature, top_p, presence_penalty, count_penalty, history)
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history[-1][1] = response
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return history
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msg.submit(user_msg, [msg, chatbot], [msg, chatbot], queue=False).then(
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chat, chatbot, chatbot, api_name="chat"
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)
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clear.click(clear_chat, None, [chatbot], queue=False)
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demo.queue(max_size=10)
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demo.launch(share=False)
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