Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "01-ai/Yi-34B-200K"
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def run(message, chat_history, system_prompt, max_new_tokens=1024, temperature=0.3, top_p=0.9, top_k=50):
+    prompt = get_prompt(message, chat_history, system_prompt)
+
+    # Encode the prompt to tensor
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+
+    # Generate a response using the model with adjusted parameters
+    response_ids = model.generate(
+        input_ids,
+        max_length=max_new_tokens + input_ids.shape[1],
+        temperature=temperature,  # Controls randomness. Lower values make text more deterministic.
+        top_p=top_p,              # Nucleus sampling: higher values allow more diversity.
+        top_k=top_k,              # Top-k sampling: limits the number of top tokens considered.
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # Decode the response
+    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return response
+
+def get_prompt(message, chat_history, system_prompt):
+    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]
+
+    do_strip = False
+    for user_input, response in chat_history:
+        user_input = user_input.strip() if do_strip else user_input
+        do_strip = True
+        texts.append(f"{user_input} [/INST] {response.strip()} </s><s>[INST] ")
+    message = message.strip() if do_strip else message
+    texts.append(f"{message} [/INST]")
+    return ''.join(texts)
+
+DEFAULT_SYSTEM_PROMPT = """
+    You are Yi. You are an AI assistant, you are moderately-polite and give only true information.
+    You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. 
+    If you think there might not be a correct answer, you say so. Since you are autoregressive, 
+    each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, 
+    assumptions, and step-by-step thinking BEFORE you try to answer a question.
+"""
+MAX_MAX_NEW_TOKENS = 200000
+DEFAULT_MAX_NEW_TOKENS = 100000
+MAX_INPUT_TOKEN_LENGTH = 100000
+
+DESCRIPTION = "# [Yi-6B](https://huggingface.co/01-ai/Yi-6B)"
+
+def clear_and_save_textbox(message): return '', message
+
+def display_input(message, history=[]):
+    history.append((message, ''))
+    return history
+
+def delete_prev_fn(history=[]):
+    try:
+        message, _ = history.pop()
+    except IndexError:
+        message = ''
+    return history, message or ''
+
+def generate(message, history_with_input, system_prompt, max_new_tokens, temperature, top_p, top_k):
+    if max_new_tokens > MAX_MAX_NEW_TOKENS:
+        raise ValueError
+
+    history = history_with_input[:-1]
+    generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
+    try:
+        first_response = next(generator)
+        yield history + [(message, first_response)]
+    except StopIteration:
+        yield history + [(message, '')]
+    for response in generator:
+        yield history + [(message, response)]
+
+def process_example(message):
+    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
+    for x in generator:
+        pass
+    return '', x
+
+def check_input_token_length(message, chat_history, system_prompt):
+    input_token_length = len(message) + len(chat_history)
+    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
+        raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.")
+
+with gr.Blocks(theme='ParityError/Anime') as demo:
+    gr.Markdown(DESCRIPTION)
+
+
+    
+    with gr.Group():
+        chatbot = gr.Chatbot(label='Yi-6B')
+        with gr.Row():
+            textbox = gr.Textbox(
+                container=False,
+                show_label=False,
+                placeholder='Hi, Yi',
+                scale=10
+            )
+            submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0)
+
+    with gr.Row():
+        retry_button = gr.Button('Retry', variant='secondary')
+        undo_button = gr.Button('Undo', variant='secondary')
+        clear_button = gr.Button('Clear', variant='secondary')
+
+    saved_input = gr.State()
+
+    with gr.Accordion(label='Advanced options', open=False):
+        system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=False)
+        max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+        temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=0.1)
+        top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=10)
+
+    textbox.submit(
+        fn=clear_and_save_textbox,
+        inputs=textbox,
+        outputs=[textbox, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=check_input_token_length,
+        inputs=[saved_input, chatbot, system_prompt],
+        api_name=False,
+        queue=False,
+    ).success(
+        fn=generate,
+        inputs=[
+            saved_input,
+            chatbot,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+        ],
+        outputs=chatbot,
+        api_name=False,
+    )
+
+    button_event_preprocess = submit_button.click(
+        fn=clear_and_save_textbox,
+        inputs=textbox,
+        outputs=[textbox, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=check_input_token_length,
+        inputs=[saved_input, chatbot, system_prompt],
+        api_name=False,
+        queue=False,
+    ).success(
+        fn=generate,
+        inputs=[
+            saved_input,
+            chatbot,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+        ],
+        outputs=chatbot,
+        api_name=False,
+    )
+
+    retry_button.click(
+        fn=delete_prev_fn,
+        inputs=chatbot,
+        outputs=[chatbot, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=generate,
+        inputs=[
+            saved_input,
+            chatbot,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+        ],
+        outputs=chatbot,
+        api_name=False,
+    )
+
+    undo_button.click(
+        fn=delete_prev_fn,
+        inputs=chatbot,
+        outputs=[chatbot, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=lambda x: x,
+        inputs=[saved_input],
+        outputs=textbox,
+        api_name=False,
+        queue=False,
+    )
+
+    clear_button.click(
+        fn=lambda: ([], ''),
+        outputs=[chatbot, saved_input],
+        queue=False,
+        api_name=False,
+    )
+
+demo.queue(max_size=32).launch(show_api=False)