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| # 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("meta-llama/Meta-Llama-3-8B-Instruct") | |
| # ## None type | |
| # def respond( | |
| # message: str, | |
| # history: list[tuple[str, str]], # This will not be used | |
| # system_message: str, | |
| # max_tokens: int, | |
| # temperature: float, | |
| # top_p: float, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # # Append only the latest user message | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # try: | |
| # # Generate response from the model | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # if message.choices[0].delta.content is not None: | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # except Exception as e: | |
| # yield f"An error occurred: {e}" | |
| # ], | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| ##Running smothly CHATBOT | |
| # 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("meta-llama/Meta-Llama-3-8B-Instruct") | |
| # def respond( | |
| # message: str, | |
| # history: list[tuple[str, str]], # This will not be used | |
| # system_message: str, | |
| # max_tokens: int, | |
| # temperature: float, | |
| # top_p: float, | |
| # ): | |
| # # Build the messages list | |
| # messages = [{"role": "system", "content": system_message}] | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # try: | |
| # # Generate response from the model | |
| # for msg in client.chat_completion( | |
| # messages=messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # if msg.choices[0].delta.content is not None: | |
| # token = msg.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # except Exception as e: | |
| # yield f"An error occurred: {e}" | |
| # """ | |
| # 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() | |
| # Use a pipeline as a high-level helper | |
| from transformers import pipeline | |
| messages = [ | |
| {"role": "user", "content": "Who are you?"}, | |
| ] | |
| pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3.1-8B-Instruct") | |
| pipe(messages) | |
| # # Load model directly | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") | |
| # model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") | |