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import torch | |
import transformers | |
import gradio as gr | |
from transformers import AutoModelForCausalLM | |
from transformers import AutoTokenizer | |
model_name = "thfname/test-gemma2-2b" | |
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
def gt (tx): | |
input_ids = tokenizer(tx, return_tensors="pt") | |
outputs = model.generate(**input_ids, max_new_tokens=32) | |
return tokenizer.decode(outputs[0]) | |
demo = gr.Interface(fn=gt, inputs="text", outputs="text") | |
demo.launch() |