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app.py

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+# Imports
+import gradio as gr
+import transformers
+import torch
+from transformers import pipeline, AutoTokenizer
+
+from huggingface_hub import login
+
+login('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')
+
+# Model name in Hugging Face docs
+model = 'klyang/MentaLLaMA-chat-7B'
+
+#"meta-llama/Llama-2-7b-chat-hf"
+
+
+tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)
+
+llama_pipeline = pipeline(
+    "text-generation",  # LLM task
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+
+SYSTEM_PROMPT = """<s>[INST] <<SYS>>
+You are a helpful bot. Your answers are clear and concise.
+<</SYS>>
+
+"""
+
+# Formatting function for message and history
+def format_message(message: str, history: list, memory_limit: int = 3) -> str:
+    """
+    Formats the message and history for the Llama model.
+
+    Parameters:
+        message (str): Current message to send.
+        history (list): Past conversation history.
+        memory_limit (int): Limit on how many past interactions to consider.
+
+    Returns:
+        str: Formatted message string
+    """
+    # always keep len(history) <= memory_limit
+    if len(history) > memory_limit:
+        history = history[-memory_limit:]
+
+    if len(history) == 0:
+        return SYSTEM_PROMPT + f"{message} [/INST]"
+
+    formatted_message = SYSTEM_PROMPT + f"{history[0][0]} [/INST] {history[0][1]} </s>"
+
+    # Handle conversation history
+    for user_msg, model_answer in history[1:]:
+        formatted_message += f"<s>[INST] {user_msg} [/INST] {model_answer} </s>"
+
+    # Handle the current message
+    formatted_message += f"<s>[INST] {message} [/INST]"
+
+    return formatted_message
+
+    
+
+# Generate a response from the Llama model
+def get_llama_response(message: str, history: list) -> str:
+    """
+    Generates a conversational response from the Llama model.
+
+    Parameters:
+        message (str): User's input message.
+        history (list): Past conversation history.
+
+    Returns:
+        str: Generated response from the Llama model.
+    """
+    query = format_message(message, history)
+    response = ""
+
+    sequences = llama_pipeline(
+        query,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        max_length=1024,
+    )
+
+    generated_text = sequences[0]['generated_text']
+    response = generated_text[len(query):]  # Remove the prompt from the output
+
+    print("Chatbot:", response.strip())
+    return response.strip()
+
+
+gr.ChatInterface(get_llama_response).launch(debug=True)
+
+
+
+
+
+
+
+
+
+
+

requirements.txt

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+accelerate
+gradio
+torch
+transformers
+sentencepiece
+