| import os | |
| import math | |
| import transformers | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| import sentencepiece | |
| from tokenization_xgen import XgenTokenizer | |
| title = "Welcome to 🙋🏻♂️Tonic's😈Xgen-8K Chat!" | |
| description = "Interestingly there simply wasnt a public demo for Xgen, So I made one. You can use [Salesforce/xgen-7b-8k-inst](https://huggingface.co/Salesforce/xgen-7b-8k-inst) 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/Xgen?duplicate=true) . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Let's build together!." | |
| 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 = "Salesforce/xgen-7b-8k-base" | |
| tokenizer = XgenTokenizer.from_pretrained("./") | |
| model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") | |
| model = model.to(dtype=torch.bfloat16) | |
| model = model.to(device) | |
| class XgenChatBot: | |
| def __init__(self, model, tokenizer, system_message="You are Xgen, an AI language model created by Tonic-AI. 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 set_system_message(self, new_system_message): | |
| self.system_message = new_system_message | |
| def format_prompt(self, user_message): | |
| prompt = f"<|im_start|>assistant\n{self.system_message}<|im_end|>\n<|im_start|>\nuser\n{user_message}<|im_end|>\nassistant\n" | |
| return prompt | |
| def predict(self, user_message, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9): | |
| prompt = self.format_prompt(user_message) | |
| 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, | |
| do_sample=True | |
| ) | |
| response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| return response | |
| def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty): | |
| Orca_bot.set_system_message(system_message) | |
| response = Orca_bot.predict(user_message, temperature, max_new_tokens, top_p, repetition_penalty) | |
| return response | |
| Orca_bot = OrcaChatBot(model, tokenizer) | |
| 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=550, minimum=360, maximum=600, 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.queue(max_size=5).launch() |