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| import os | |
| from threading import Thread | |
| from typing import Iterator | |
| import gradio as gr | |
| import spaces | |
| import torch | |
| from transformers import ( | |
| AutoModelForCausalLM, | |
| AutoTokenizer, | |
| TextIteratorStreamer, | |
| LlamaTokenizer, | |
| ) | |
| MAX_MAX_NEW_TOKENS = 1024 | |
| DEFAULT_MAX_NEW_TOKENS = 50 | |
| MAX_INPUT_TOKEN_LENGTH = 512 | |
| DESCRIPTION = """\ | |
| # OpenELM-270M-Instruct -- Running on CPU | |
| This Space demonstrates [apple/OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) by Apple. Please, check the original model card for details. | |
| For additional detail on the model, including a link to the arXiv paper, refer to the [Hugging Face Paper page for OpenELM](https://huggingface.co/papers/2404.14619) . | |
| For details on pre-training, instruction tuning, and parameter-efficient finetuning for the model refer to the [OpenELM page in the CoreNet GitHub repository](https://github.com/apple/corenet/tree/main/projects/openelm) . | |
| """ | |
| LICENSE = """ | |
| <p/> | |
| --- | |
| As a derivative work of [apple/OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) by Apple, | |
| this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-270M-Instruct/blob/main/LICENSE) | |
| Based on the [Norod78/OpenELM_3B_Demo](https://huggingface.co/spaces/Norod78/OpenELM_3B_Demo) space. | |
| """ | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "apple/OpenELM-270M-Instruct", | |
| revision="eb111ff", | |
| trust_remote_code=True, | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "meta-llama/Llama-2-7b-hf", | |
| revision="01c7f73", | |
| trust_remote_code=True, | |
| tokenizer_class=LlamaTokenizer, | |
| ) | |
| if tokenizer.pad_token == None: | |
| tokenizer.pad_token = tokenizer.eos_token | |
| tokenizer.pad_token_id = tokenizer.eos_token_id | |
| model.config.pad_token_id = tokenizer.eos_token_id | |
| def generate( | |
| message: str, | |
| chat_history: list[tuple[str, str]], | |
| max_new_tokens: int = 1024, | |
| temperature: float = 0.1, | |
| top_p: float = 0.5, | |
| top_k: int = 3, | |
| repetition_penalty: float = 1.4, | |
| ) -> Iterator[str]: | |
| historical_text = "" | |
| #Prepend the entire chat history to the message with new lines between each message | |
| for user, assistant in chat_history: | |
| historical_text += f"\n{user}\n{assistant}" | |
| if len(historical_text) > 0: | |
| message = historical_text + f"\n{message}" | |
| input_ids = tokenizer([message], return_tensors="pt").input_ids | |
| if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: | |
| input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] | |
| gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
| input_ids = input_ids.to(model.device) | |
| streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| {"input_ids": input_ids}, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=top_p, | |
| top_k=top_k, | |
| temperature=temperature, | |
| num_beams=1, | |
| pad_token_id = tokenizer.eos_token_id, | |
| repetition_penalty=repetition_penalty, | |
| no_repeat_ngram_size=5, | |
| early_stopping=False, | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| outputs = [] | |
| for text in streamer: | |
| outputs.append(text) | |
| yield "".join(outputs) | |
| chat_interface = gr.ChatInterface( | |
| fn=generate, | |
| additional_inputs=[ | |
| gr.Slider( | |
| label="Max new tokens", | |
| minimum=1, | |
| maximum=MAX_MAX_NEW_TOKENS, | |
| step=1, | |
| value=DEFAULT_MAX_NEW_TOKENS, | |
| ), | |
| gr.Slider( | |
| label="Temperature", | |
| minimum=0.0, | |
| maximum=4.0, | |
| step=0.1, | |
| value=0.1, | |
| ), | |
| gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| minimum=0.05, | |
| maximum=1.0, | |
| step=0.05, | |
| value=0.5, | |
| ), | |
| gr.Slider( | |
| label="Top-k", | |
| minimum=1, | |
| maximum=1000, | |
| step=1, | |
| value=3, | |
| ), | |
| gr.Slider( | |
| label="Repetition penalty", | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| value=1.4, | |
| ), | |
| ], | |
| stop_btn="Stop", | |
| cache_examples=False, | |
| examples=[ | |
| ["You are three years old. Count from one to ten."], | |
| ["Explain quantum physics in 5 words or less:"], | |
| ["Question: What do you call a bear with no teeth?\nAnswer:"], | |
| ], | |
| ) | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| chat_interface.render() | |
| gr.Markdown(LICENSE) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |