Update app.py
Browse files
app.py
CHANGED
@@ -2,15 +2,22 @@ import gradio as gr
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from gpt4all import GPT4All
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from huggingface_hub import hf_hub_download
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title = "
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description = """
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"""
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model_path = "
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# model_name = "model-unsloth.Q4_K_M.gguf"
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# hf_hub_download(repo_id="google/DiarizationLM-13b-Fisher-v1", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
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model_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
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hf_hub_download(repo_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
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@@ -18,37 +25,35 @@ print("Start the model init process")
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model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
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print("Finish the model init process")
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model.config["promptTemplate"] = "{0}
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model.config["systemPrompt"] = ""
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model._is_chat_session_activated = False
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max_new_tokens = 2048
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print("Finish the model config process")
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def generater(message, history, temperature, top_p, top_k):
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prompt =
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outputs = []
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for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True):
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outputs.append(token)
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yield "".join(outputs)
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def vote(data: gr.LikeData):
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if data.liked:
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return
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else:
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return
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chatbot = gr.Chatbot()
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print("Created chatbot")
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print("Add additional_inputs")
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additional_inputs=[
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gr.Slider(
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label="temperature",
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value=0.
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minimum=0.0,
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maximum=2.0,
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step=0.05,
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@@ -66,7 +71,7 @@ additional_inputs=[
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),
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gr.Slider(
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label="top_k",
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value=
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minimum=0,
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maximum=1000,
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step=1,
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@@ -74,7 +79,9 @@ additional_inputs=[
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info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.",
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)
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]
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iface = gr.ChatInterface(
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fn = generater,
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@@ -83,17 +90,17 @@ iface = gr.ChatInterface(
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chatbot=chatbot,
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additional_inputs=additional_inputs,
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examples=[
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["
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)
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with gr.Blocks() as demo:
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chatbot.like(vote, None, None)
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iface.render()
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print("Rendered iface")
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if __name__ == "__main__":
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demo.queue(max_size=3).launch()
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from gpt4all import GPT4All
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from huggingface_hub import hf_hub_download
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title = "Mistral-7B-Instruct-GGUF Run On CPU-Basic Free Hardware"
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description = """
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🔎 [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) [GGUF format model](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) , 4-bit quantization balanced quality gguf version, running on CPU. English Only (Also support other languages but the quality's not good). Using [GitHub - llama.cpp](https://github.com/ggerganov/llama.cpp) [GitHub - gpt4all](https://github.com/nomic-ai/gpt4all).
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🔨 Running on CPU-Basic free hardware. Suggest duplicating this space to run without a queue.
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Mistral does not support system prompt symbol (such as ```<<SYS>>```) now, input your system prompt in the first message if you need. Learn more: [Guardrailing Mistral 7B](https://docs.mistral.ai/usage/guardrailing).
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"""
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"""
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[Model From TheBloke/Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
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[Mistral-instruct-v0.1 System prompt](https://docs.mistral.ai/usage/guardrailing)
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"""
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model_path = "models"
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model_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
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hf_hub_download(repo_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
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model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
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print("Finish the model init process")
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model.config["promptTemplate"] = "[INST] {0} [/INST]"
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model.config["systemPrompt"] = ""
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model._is_chat_session_activated = False
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max_new_tokens = 2048
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def generater(message, history, temperature, top_p, top_k):
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prompt = "<s>"
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for user_message, assistant_message in history:
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prompt += model.config["promptTemplate"].format(user_message)
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prompt += assistant_message + "</s>"
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prompt += model.config["promptTemplate"].format(message)
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outputs = []
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for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True):
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outputs.append(token)
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yield "".join(outputs)
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def vote(data: gr.LikeData):
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if data.liked:
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return
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else:
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return
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chatbot = gr.Chatbot(avatar_images=('resourse/user-icon.png', 'resourse/chatbot-icon.png'),bubble_full_width = False)
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additional_inputs=[
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gr.Slider(
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label="temperature",
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value=0.5,
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minimum=0.0,
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maximum=2.0,
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step=0.05,
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),
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gr.Slider(
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label="top_k",
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value=40,
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minimum=0,
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maximum=1000,
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step=1,
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info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.",
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)
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]
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character = "Sherlock Holmes"
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series = "Arthur Conan Doyle's novel"
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iface = gr.ChatInterface(
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fn = generater,
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chatbot=chatbot,
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additional_inputs=additional_inputs,
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examples=[
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["Hello there! How are you doing?"],
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["How many hours does it take a man to eat a Helicopter?"],
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["You are a helpful and honest assistant. Always answer as helpfully as possible. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."],
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["I want you to act as a spoken English teacher and improver. I will speak to you in English and you will reply to me in English to practice my spoken English. I want you to strictly correct my grammar mistakes, typos, and factual errors. I want you to ask me a question in your reply. Now let's start practicing, you could ask me a question first. Remember, I want you to strictly correct my grammar mistakes, typos, and factual errors."],
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[f"I want you to act like {character} from {series}. I want you to respond and answer like {character} using the tone, manner and vocabulary {character} would use. Do not write any explanations. Only answer like {character}. You must know all of the knowledge of {character}."]
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]
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)
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with gr.Blocks(css="resourse/style/custom.css") as demo:
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chatbot.like(vote, None, None)
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iface.render()
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if __name__ == "__main__":
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demo.queue(max_size=3).launch()
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