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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| #from unsloth import FastLanguageModel | |
| from peft import AutoPeftModelForCausalLM | |
| from transformers import AutoTokenizer | |
| """ | |
| 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") | |
| #client = InferenceClient("halme/id2223_lora_model") | |
| 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 """ | |
| """ model, tokenizer = FastLanguageModel.from_pretrained( | |
| model_name = "halme/id2223_lora_model", # YOUR MODEL YOU USED FOR TRAINING | |
| max_seq_length = max_tokens, | |
| dtype = None, | |
| load_in_4bit = True, | |
| ) """ | |
| model = AutoPeftModelForCausalLM.from_pretrained( | |
| "halme/id2223_lora_model", # YOUR MODEL YOU USED FOR TRAINING | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("halme/id2223_lora_model") | |
| #FastLanguageModel.for_inference(model) # Enable native 2x faster inference | |
| """messages = [ | |
| {"role": "user", "content": "Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,"}, | |
| ] """ | |
| inputs = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize = True, | |
| add_generation_prompt = True, # Must add for generation | |
| return_tensors = "pt", | |
| ) | |
| from transformers import TextStreamer | |
| text_streamer = TextStreamer(tokenizer, skip_prompt = True) | |
| yield model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, | |
| use_cache = True, temperature = 1.5, min_p = 0.1) | |
| """ | |
| 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() | |