import os from threading import Thread from typing import Iterator import gradio as gr import spaces import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer MAX_MAX_NEW_TOKENS = 1024 DEFAULT_MAX_NEW_TOKENS = 256 MAX_INPUT_TOKEN_LENGTH = 512 DESCRIPTION = """\ # OpenELM-3B This Space demonstrates [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) by Apple. Please, check the original model card for details. You can see the other models of the OpenELM family [here](https://huggingface.co/apple/OpenELM) The following Colab notebooks are available: * [OpenELM-3B (GPU)](https://gist.github.com/Norod/4f11bb36bea5c548d18f10f9d7ec09b0) * [OpenELM-270M (CPU)](https://gist.github.com/Norod/5a311a8e0a774b5c35919913545b7af4) You might also be interested in checking out Apple's [CoreNet Github page](https://github.com/apple/corenet?tab=readme-ov-file). If you duplicate this space, make sure you have access to [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) because this model uses it as a tokenizer. # Note: Use this model for only for completing sentences. ## While the user interface is of a chatbot for convenience, this is a base model and is not fine-tuned for chatbot tasks or instruction following tasks. As such, the model is not provided a chat history and will complete your text based on the last given prompt only. """ LICENSE = """

--- As a derivative work of [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) by Apple, this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-3B/blob/main/LICENSE). """ if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🥶 This demo does not work on CPU.

" if torch.cuda.is_available(): model_id = "apple/OpenELM-3B" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True) tokenizer_id = "meta-llama/Llama-2-7b-hf" tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) if tokenizer.pad_token == None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id @spaces.GPU def generate( message: str, chat_history: list[tuple[str, str]], max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.4, ) -> Iterator[str]: 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, repetition_penalty=repetition_penalty, ) 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=0.1, maximum=4.0, step=0.1, value=0.6, ), gr.Slider( label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, ), gr.Slider( label="Top-k", minimum=1, maximum=1000, step=1, value=50, ), gr.Slider( label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.4, ), ], stop_btn=None, examples=[ ["A recipe for a chocolate cake:"], ["Can you explain briefly to me what is the Python programming language?"], ["Explain the plot of Cinderella in a sentence."], ["Question: What is the capital of France?\nAnswer:"], ["Question: I am very tired, what should I do?\nAnswer:"], ], ) with gr.Blocks(css="style.css") as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") chat_interface.render() gr.Markdown(LICENSE) if __name__ == "__main__": demo.queue(max_size=20).launch()