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import gradio as gr |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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import time |
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import numpy as np |
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from torch.nn import functional as F |
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import os |
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from threading import Thread |
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token = os.environ["HF_TOKEN"] |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,token=token) |
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tok = AutoTokenizer.from_pretrained("google/gemma-2b-it",token=token) |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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model = model.to(device) |
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start_message = "" |
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def user(message, history): |
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return "", history + [[message, ""]] |
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def chat(message, history): |
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chat = [] |
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for item in history: |
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chat.append({"role": "user", "content": item[0]}) |
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if item[1] is not None: |
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chat.append({"role": "assistant", "content": item[1]}) |
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chat.append({"role": "user", "content": message}) |
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messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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model_inputs = tok([messages], return_tensors="pt").to(device) |
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streamer = TextIteratorStreamer( |
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tok, timeout=10., skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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model_inputs, |
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streamer=streamer, |
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max_new_tokens=1024, |
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do_sample=True, |
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top_p=0.95, |
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top_k=1000, |
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temperature=0.75, |
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num_beams=1, |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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partial_text = "" |
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for new_text in streamer: |
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partial_text += new_text |
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yield partial_text |
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demo = gr.ChatInterface(fn=chat, examples=[["Write me a poem about Machine Learning."]], title="gemma 2b-it") |
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demo.launch() |
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