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| import gradio as gr | |
| from gpt4all import GPT4All | |
| from huggingface_hub import hf_hub_download | |
| import os | |
| current_directory = os.getcwd() | |
| model_directory = os.path.join(current_directory, "models") | |
| title = "TaoScience" | |
| description = """ | |
| <h1><center>LLM Finetuned on TaoScience<center></h1> | |
| <h3><center>TaoGPT is a fine-tuned LLM on Tao Science by Dr. Rulin Xu and Dr. Zhi Gang Sha. <br> Check out- <a href='https://github.com/agencyxr/taogpt7B'>Github Repo</a> For More Information. 💬<h3><center> | |
| """ | |
| NOMIC = """ | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>TaoGPT - DataMap</title> | |
| <style> | |
| iframe { | |
| width: 100%; | |
| height: 600px; /* You can adjust the height as needed */ | |
| border: 0; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <iframe | |
| src="https://atlas.nomic.ai/map/c1ce06f4-7ed0-4c02-88a4-dd3b47bdf878/f2941fb8-0f36-4a23-8cbe-40dbf76ca9e4?xs=-41.09135&xf=41.12038&ys=-22.50394&yf=23.67273" | |
| ></iframe> | |
| </body> | |
| </html> | |
| """ | |
| model_path = "models" | |
| model_name = "taogpt-v1-gguf.Q5_K_M.gguf" | |
| if os.path.exists(model_directory) and os.path.isdir(model_directory): | |
| print("Models folder already exits") | |
| else: | |
| hf_hub_download(repo_id="agency888/TaoGPT-v1-GGUF-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False) | |
| print("Start the model init process") | |
| model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu") | |
| print("Finish the model init process") | |
| model.config["promptTemplate"] = """{0} | |
| """ | |
| model.config["systemPrompt"] = "In the Context of TaoScience answer this questions: " | |
| model._is_chat_session_activated = False | |
| max_new_tokens = 2048 | |
| def generator(message, history, temperature, top_p, top_k): | |
| prompt = "" | |
| for user_message, assistant_message in history: | |
| prompt += model.config["promptTemplate"].format(user_message) | |
| prompt += model.config["promptTemplate"].format(message) | |
| outputs = [] | |
| for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True): | |
| outputs.append(token) | |
| yield "".join(outputs) | |
| def vote(data: gr.LikeData): | |
| if data.liked: | |
| return | |
| else: | |
| return | |
| chatbot = gr.Chatbot(bubble_full_width=False) | |
| additional_inputs=[ | |
| gr.Slider( | |
| label="temperature", | |
| value=0.2, | |
| minimum=0.0, | |
| maximum=2.0, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.", | |
| ), | |
| gr.Slider( | |
| label="top_p", | |
| value=1.0, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.01, | |
| interactive=True, | |
| info="0.1 means only the tokens comprising the top 10% probability mass are considered. Suggest set to 1 and use temperature. 1 means 100% and will disable it", | |
| ), | |
| gr.Slider( | |
| label="top_k", | |
| value=40, | |
| minimum=0, | |
| maximum=1000, | |
| step=1, | |
| interactive=True, | |
| info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.", | |
| ) | |
| ] | |
| with gr.Blocks() as demo: | |
| gr.HTML("<h1><center>TaoGPTv0<center></h1>") | |
| gr.HTML("<h3><center>TaoGPTv0 is a fine-tuned Mistal-7B model with a retrieval augmented generation pipeline on Tao Science by Dr. Rulin Xu and Dr. Zhi Gang Sha. Check out- <a href='https://github.com/agencyxr/taogpt7B'>Github Repo</a> For More Information. 💬<h3><center>") | |
| with gr.Column(): | |
| with gr.Accordion("Visualise Training Data"): | |
| gr.HTML("<h3>Look into the dataset we used to finetune our model</h3>") | |
| gr.HTML(NOMIC) | |
| with gr.Column(): | |
| gr.ChatInterface( | |
| fn = generator, | |
| title=title, | |
| description = description, | |
| chatbot=chatbot, | |
| additional_inputs=additional_inputs, | |
| examples=[ | |
| ["What is TaoScience ?"], | |
| ["TaoScience was written by ?"], | |
| ["Tell me more about TaoScience"]],) | |
| RAG_Checkbox = gr.Checkbox(label="Use Retrival Augmented Generation" , value=True , interactive=False) | |
| gr.Markdown("The model is prone to Hallucination and many not always be Factual") | |
| if __name__ == "__main__": | |
| demo.queue(max_size=50).launch(share=True) |