| #app.py Modif04 | |
| #https://www.freddyboulton.com/blog/llama-cpp-python | |
| import gradio as gr | |
| from llama_cpp import Llama | |
| llm = Llama( | |
| model_path="/home/user/app/h2o-danube3-500m-chat-Q4_K_M.gguf", | |
| verbose=True | |
| ) | |
| def predict(message, history): | |
| # messages = [{"role": "system", "content": "You are a helpful assistant."}] | |
| # messages = [{"role": "assistant", "content": "You are a helpful assistant."}] | |
| # messages = [{"role": "assistant", "content": "Bonjour, comment puis-je vous aider?"}] | |
| messages = [] | |
| for user_message, bot_message in history: | |
| if user_message: | |
| messages.append({"role": "user", "content": user_message}) | |
| if bot_message: | |
| messages.append({"role": "assistant", "content": bot_message}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for chunk in llm.create_chat_completion( | |
| stream=True, | |
| messages=messages, | |
| ): | |
| part = chunk["choices"][0]["delta"].get("content", None) | |
| if part: | |
| response += part | |
| yield response | |
| demo = gr.ChatInterface(predict) | |
| demo.launch() | |
| ##app.py Modif03 | |
| #import gradio as gr | |
| #from huggingface_hub import create_inference_endpoint, InferenceClient | |
| #from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # | |
| ##model_name = "MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf" | |
| ##model = AutoModelForCausalLM.from_pretrained(model_name) | |
| ##tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # | |
| ##client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| ##client = InferenceClient("MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf") | |
| ##client = InferenceClient("/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf") | |
| # | |
| ## Créez une instance Inference locale | |
| #endpoint = create_inference_endpoint( | |
| # "Local-Endpoint-MisterAI-H2O", | |
| # repository="MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf", | |
| ## model_path="/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf", | |
| # framework="pytorch", | |
| # task="text-generation", | |
| # accelerator="cpu", | |
| # vendor="local", | |
| # region="local", | |
| # type="unprotected", | |
| # instance_size="small", | |
| # instance_type="local", | |
| # URL="http://0.0.0.0:6789" | |
| #) | |
| # | |
| #print(f"Endpoint créé à l'URL : {endpoint.url}") | |
| # | |
| #client = endpoint.client | |
| # | |
| # | |
| # | |
| #def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| #): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # | |
| # messages.append({"role": "user", "content": message}) | |
| # | |
| # response = "" | |
| # | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # | |
| # response += token | |
| # yield response | |
| # | |
| #demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| #) | |
| # | |
| # | |
| #if __name__ == "__main__": | |
| # demo.launch() | |
| # | |
| # | |
| # | |
| # | |
| ##app.py Modif01 | |
| #import gradio as gr | |
| #from huggingface_hub import Inference, InferenceClient | |
| #from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # | |
| ##model_name = "MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf" | |
| ##model = AutoModelForCausalLM.from_pretrained(model_name) | |
| ##tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # | |
| ##client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| ##client = InferenceClient("MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf") | |
| ##client = InferenceClient("/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf") | |
| # | |
| ## Créez une instance Inference locale | |
| #inference = Inference( | |
| # model_path="/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf", | |
| # device="cpu", # Utilisez le CPU pour l'inference | |
| # token=None, # Pas de token nécessaire pour cette instance | |
| #) | |
| # | |
| #client = inference | |
| # | |
| # | |
| # | |
| #def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| #): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # | |
| # messages.append({"role": "user", "content": message}) | |
| # | |
| # response = "" | |
| # | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # | |
| # response += token | |
| # yield response | |
| # | |
| #demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| #) | |
| # | |
| # | |
| #if __name__ == "__main__": | |
| # demo.launch() | |
| # | |
| # | |
| # | |
| # | |
| # | |
| ##app.py ORIGINAL | |
| #import gradio as gr | |
| #from huggingface_hub import InferenceClient | |
| # | |
| #""" | |
| #For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| #""" | |
| #client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # | |
| # | |
| #def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| #): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # | |
| # messages.append({"role": "user", "content": message}) | |
| # | |
| # response = "" | |
| # | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # | |
| # response += token | |
| # yield response | |
| # | |
| #""" | |
| #For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| #""" | |
| #demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| #) | |
| # | |
| # | |
| #if __name__ == "__main__": | |
| # demo.launch() | |