import os import math import transformers from transformers import AutoModelForCausalLM, AutoTokenizer import torch import gradio as gr import sentencepiece title = "Welcome to Tonic's 🐋🐳Orca-2-13B (in 8bit)!" description = "You can use [🐋🐳microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) via API using Gradio by scrolling down and clicking Use 'Via API' or privately by [cloning this space on huggingface](https://huggingface.co/spaces/Tonic1/TonicsOrca2?duplicate=true) . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Big thanks to the HuggingFace Organisation for the Community Grant." # os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:50' device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model_name = "microsoft/Orca-2-13b" # offload_folder = './model_weights' # if not os.path.exists(offload_folder): # os.makedirs(offload_folder) tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) model = transformers.AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) class OrcaChatBot: def __init__(self, model, tokenizer, system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."): self.model = model self.tokenizer = tokenizer self.system_message = system_message def predict(self, user_message, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9): prompt = f"<|im_start|>system\n{self.system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False) input_ids = inputs["input_ids"].to(self.model.device) output_ids = self.model.generate( input_ids, max_length=input_ids.shape[1] + max_new_tokens, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, pad_token_id=self.tokenizer.eos_token_id, do_sample=True ) response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True) return response Orca_bot = OrcaChatBot(model, tokenizer) def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty): full_message = f"{system_message}\n{user_message}" if system_message else user_message return Orca_bot.predict(full_message, temperature, max_new_tokens, top_p, repetition_penalty) iface = gr.Interface( fn=gradio_predict, title=title, description=description, inputs=[ gr.Textbox(label="Your Message", type="text", lines=3), gr.Textbox(label="Introduce a Character Here or Set a Scene (system prompt)", type="text", lines=2), gr.Slider(label="Max new tokens", value=125, minimum=25, maximum=256, step=1), gr.Slider(label="Temperature", value=0.1, minimum=0.05, maximum=1.0, step=0.05), gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, step=0.05), gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0, step=0.05) ], outputs="text", theme="ParityError/Anime" ) iface.launch()