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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 | |
| MAX_MAX_NEW_TOKENS = 1024 | |
| DEFAULT_MAX_NEW_TOKENS = 256 | |
| MAX_INPUT_TOKEN_LENGTH = 512 | |
| DESCRIPTION = """\ | |
| # OpenELM-3B-Instruct | |
| This Space demonstrates [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) 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-Instruct (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 and instruction following. | |
| """ | |
| LICENSE = """ | |
| <p/> | |
| --- | |
| As a derivative work of [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) by Apple, | |
| this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-3B-Instruct/blob/main/LICENSE). | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| if torch.cuda.is_available(): | |
| model_id = "apple/OpenELM-3B-Instruct" | |
| 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 | |
| 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 = 0.6, | |
| top_p: float = 0.9, | |
| top_k: int = 50, | |
| 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=0.1, | |
| 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() | |