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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("./.", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("./.", trust_remote_code=True)
with torch.no_grad():
input_text = "Hi_"
inputs = tokenizer(text=input_text, return_tensors="pt")
del inputs["token_type_ids"]
print(inputs)
gen = model.generate(**inputs, max_new_tokens=1, do_sample=False)
decoded = tokenizer.batch_decode(gen, skip_special_tokens=True)
print(decoded)
"""
from hunyuan.configuration_hunyuan import HunYuanConfig
from hunyuan.modeling_hunyuan import HunYuanMoEV1ForCausalLM
import torch
config = HunYuanConfig.from_pretrained("./Hunyuan-A13B-Instruct", trust_remote_code=True)
config.moe_intermediate_size = [3072, 3072]
config.num_experts = 4
config.num_shared_expert = [1, 1]
config.moe_topk = [2, 2]
config.num_hidden_layers = 4
model = HunYuanMoEV1ForCausalLM(config)
print(model)
torch.manual_seed(0)
state_dict = model.state_dict()
for key in state_dict:
state_dict[key].uniform_(-0.2, 0.2)
model.load_state_dict(state_dict)
model.save_pretrained("./hunyuan-tiny")
"""
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