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import gradio as gr | |
import os | |
import spaces | |
from transformers import GemmaTokenizer, AutoModelForCausalLM | |
# Set an environment variable | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
# Load the tokenizer and model | |
tokenizer = GemmaTokenizer.from_pretrained("google/codegemma-7b-it") | |
model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it", device_map="auto") #.to("cuda:0") | |
def codegemma(message: str, history: list, temperature: float, max_new_tokens: int) -> str: | |
""" | |
Generate a response using the CodeGemma model. | |
Args: | |
message (str): The input message. | |
history (list): The conversation history used by ChatInterface. | |
temperature (float): The temperature for generating the response. | |
max_new_tokens (int): The maximum number of new tokens to generate. | |
Returns: | |
str: The generated response. | |
""" | |
input_ids = tokenizer(message, return_tensors="pt") | |
outputs = model.generate( | |
**input_ids, | |
temperature=temperature, | |
max_new_tokens=max_new_tokens, | |
) | |
response = tokenizer.decode(outputs[0]) | |
return response | |
placeholder = """ | |
<img src="https://huggingface.co/spaces/ysharma/CodeGemma/resolve/main/gemma_lockup_vertical_full-color_rgb.png" style="width:40%"> | |
<b>CodeGemma-7B-IT</b> | |
""" | |
# Gradio block | |
with gr.Blocks(fill_height=True) as demo: | |
gr.Markdown("# GEMMA-7b-IT") | |
#with gr.Tab('CodeGemma Chatbot'): | |
gr.ChatInterface(codegemma, | |
examples=[["Write a Python function to calculate the nth fibonacci number."]], | |
fill_height=True, | |
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), | |
additional_inputs=[ | |
gr.Slider(0, 1, 0.95, label="Temperature", render=False), | |
gr.Slider(128, 4096, 512, label="Max new tokens", render=False ), | |
], | |
) | |
if __name__ == "__main__": | |
demo.launch(debug=False) |