Update app.py
Browse files
app.py
CHANGED
@@ -16,29 +16,18 @@ top_p=0.92
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repetition_penalty=1.7
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max_length=2048
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base_model_id = "tiiuae/falcon-7b-instruct"
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model_directory = "Tonic/GaiaMiniMed"
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# Instantiate the Tokenizer
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tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left")
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = 'left'
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# Load the GaiaMiniMed model with the specified configuration
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# Load the Peft model with a specific configuration
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# Specify the configuration class for the model
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model_config = AutoConfig.from_pretrained(base_model_id)
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# Load the PEFT model with the specified configuration
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peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config)
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peft_model = PeftModel.from_pretrained(peft_model, model_directory)
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# Class to encapsulate the Falcon chatbot
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class FalconChatBot:
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def __init__(self, system_prompt
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self.system_prompt = system_prompt
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def process_history(self, history):
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@@ -56,7 +45,7 @@ class FalconChatBot:
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user_message = message.get("user", "")
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assistant_message = message.get("assistant", "")
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# Check if the user_message is not a special command
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if not user_message.startswith("
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filtered_history.append({"user": user_message, "assistant": assistant_message})
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return filtered_history
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@@ -83,31 +72,26 @@ class FalconChatBot:
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falcon_bot = FalconChatBot()
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# Define the Gradio interface
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title = "👋🏻
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description = "
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# "top_p": 0.90,
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# "repetition_penalty": 1.9,
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# }
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# ]
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# ]
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additional_inputs=[
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gr.Textbox("", label="
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gr.Slider(
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label="Temperature",
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value=0.9,
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@@ -152,7 +136,7 @@ iface = gr.Interface(
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description=description,
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# examples=examples,
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inputs=[
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gr.inputs.Textbox(label="
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] + additional_inputs,
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outputs="text",
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theme="ParityError/Anime"
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repetition_penalty=1.7
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max_length=2048
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model_name = "OpenLLM-France/Claire-7B-0.1"
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
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model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
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device_map="auto",
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torch_dtype=torch.bfloat16,
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load_in_4bit=True # For efficient inference, if supported by the GPU card
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)
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# Class to encapsulate the Falcon chatbot
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class FalconChatBot:
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def __init__(self, system_prompt):
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self.system_prompt = system_prompt
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def process_history(self, history):
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user_message = message.get("user", "")
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assistant_message = message.get("assistant", "")
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# Check if the user_message is not a special command
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if not user_message.startswith("Protagoniste:"):
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filtered_history.append({"user": user_message, "assistant": assistant_message})
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return filtered_history
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falcon_bot = FalconChatBot()
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# Define the Gradio interface
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title = "👋🏻Bienvenue à Tonic's 🌜🌚Claire Chat !"
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description = "Vous pouvez utiliser [🌜🌚ClaireGPT](https://huggingface.co/OpenLLM-France/Claire-7B-0.1) Ou dupliquer pour l'uiliser localement ou sur huggingface! [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
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history = [
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{"user": "Le dialogue suivant est une conversation entre Emmanuel Macron et Elon Musk:", "assistant": "Emmanuel Macron: Bonjour Monsieur Musk. Je vous remercie de me recevoir aujourd'hui."},]
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examples = [
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[
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{
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"user_message": "[Elon Musk:] - Bonjour Emmanuel. Enchanté de vous revoir.",
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"assistant_message": "[Emmanuel Macron:] - Je vois que vous avez effectué un voyage dans la région de la Gascogne.",
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"history": [],
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"temperature": 0.4,
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"max_new_tokens": 700,
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"top_p": 0.90,
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"repetition_penalty": 1.9,
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}
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]
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]
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additional_inputs=[
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gr.Textbox("", label="Introduisez Un Autre Personnage Ici ou Mettez En Scene"),
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gr.Slider(
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label="Temperature",
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value=0.9,
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description=description,
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# examples=examples,
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inputs=[
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gr.inputs.Textbox(label="Utilisez se format pour initier une conversation [Personage:]", type="text", lines=5),
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] + additional_inputs,
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outputs="text",
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theme="ParityError/Anime"
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