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| 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() |