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from transformers import AutoTokenizer, AutoModel
import gradio as gr
# tokenizer = AutoTokenizer.from_pretrained(".\\models\\chatglm-6b-int4", trust_remote_code=True, revision="")
# model = AutoModel.from_pretrained(".\\models\\chatglm-6b-int4", trust_remote_code=True, revision="").half().cuda().cpu()
# from transformers import AutoTokenizer, AutoModel
# tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
# model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
# model = model.eval()
# kernel_file = "./models/chatglm-6b-int4/quantization_kernels.so"
tokenizer = AutoTokenizer.from_pretrained("./models/chatglm-6b-int4", trust_remote_code=True, revision="")
# model = AutoModel.from_pretrained("./models/chatglm-6b-int4", trust_remote_code=True, revision="").half().cuda()
model = AutoModel.from_pretrained("./models/chatglm-6b-int4", trust_remote_code=True, revision="").float()
# model = model.quantize(bits=model_args.quantization_bit, kernel_file=kernel_file)
model = model.eval()
def chat(msg):
history = []
response, history = model.chat(tokenizer, msg, history=history)
print("response:", response)
return response
iface = gr.Interface(fn=chat, inputs="text", outputs="text")
iface.launch()
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