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| import gradio as gr | |
| import os | |
| import spaces | |
| from transformers import AutoTokenizer, TextIteratorStreamer | |
| from threading import Thread | |
| from llama_cpp import Llama | |
| # Set an environment variable | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| DESCRIPTION = ''' | |
| <div> | |
| <h1 style="text-align: center;">CyberNative-AI/Colibri_8b_v0.1</h1> | |
| <p>This Space demonstrates the CyberSecurity-tuned model <a href="https://huggingface.co/CyberNative-AI/Colibri_8b_v0.1"><b>Colibri_8b_v0.1</b></a>. | |
| </div> | |
| ''' | |
| LICENSE = """ | |
| <p/> | |
| --- | |
| Colibri v0.1 is built on top of Dolphin Llama 3 | |
| """ | |
| PLACEHOLDER = """ | |
| <div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;"> | |
| <img src="https://huggingface.co/CyberNative-AI/Colibri_8b_v0.1/resolve/main/cybernative_ai_colibri_logo.jpeg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; "> | |
| <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Colibri_v0.1</h1> | |
| <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p> | |
| </div> | |
| """ | |
| css = """ | |
| h1 { | |
| text-align: center; | |
| display: block; | |
| } | |
| #duplicate-button { | |
| margin: auto; | |
| color: white; | |
| background: #1565c0; | |
| border-radius: 100vh; | |
| } | |
| """ | |
| def chat_llama3_8b(message: str, | |
| history: list, | |
| temperature: float, | |
| max_new_tokens: int | |
| ) -> str: | |
| """ | |
| Generate a streaming response using the llama3-8b model. | |
| Args: | |
| message (str): The input message. | |
| history (list): The conversation history used by ChatInterface. | |
| temperature (float): The temperature for generating the response. | |
| max_new_tokens (int): The maximum number of new tokens to generate. | |
| Returns: | |
| str: The generated response. | |
| """ | |
| conversation = [] | |
| conversation.append({"role": "system", "content": "You are Colibri, an advanced cybersecurity AI assistant developed by CyberNative AI."}) | |
| for user, assistant in history: | |
| conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) | |
| conversation.append({"role": "user", "content": message}) | |
| llm = Llama.from_pretrained( | |
| repo_id="CyberNative-AI/Colibri_8b_v0.1_q5_gguf", | |
| filename="*Q5_K_M.gguf", | |
| chat_format="chatml", | |
| verbose=False, | |
| max_tokens=max_new_tokens, | |
| stop=["<|im_end|>"] | |
| ) | |
| response=llm.create_chat_completion(messages=conversation, temperature=temperature) | |
| # Access the first (and likely only) choice in the response | |
| choice = response['choices'][0] | |
| # Extract the text content from the message within the choice | |
| text_response = choice['message']['content'] | |
| yield text_response | |
| # Gradio block | |
| chatbot=gr.Chatbot(height=700, placeholder=PLACEHOLDER, label='Gradio ChatInterface') | |
| with gr.Blocks(fill_height=True, css=css) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.ChatInterface( | |
| fn=chat_llama3_8b, | |
| chatbot=chatbot, | |
| fill_height=True, | |
| additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), | |
| additional_inputs=[ | |
| gr.Slider(minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=0.6, | |
| label="Temperature", | |
| render=False), | |
| gr.Slider(minimum=128, | |
| maximum=4096, | |
| step=1, | |
| value=512, | |
| label="Max new tokens", | |
| render=False ), | |
| ], | |
| examples=[ | |
| ['What are the two main methods used in the research to collect DKIM information?'], | |
| ['What is the primary purpose of OS fingerprinting using tools like Nmap, and why might it not always be 100% accurate?'], | |
| ['What is 9,000 * 9,000?'], | |
| ['What technique can be used to enumerate SMB shares within a Windows environment from a Windows client?'], | |
| ['What is the primary benefit of interleaving in cybersecurity education and training?'] | |
| ], | |
| cache_examples=False, | |
| ) | |
| gr.Markdown(LICENSE) | |
| if __name__ == "__main__": | |
| demo.launch() |