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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer,AutoConfig
def load_tokenizer(model_name: str, is_hf: bool=False):
if not is_hf:
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.model_max_length = 2048
else:
if "mamba" in model_name or "mpt" in model_name:
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
else:
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
return tokenizer
from fla.models import DeltaNetConfig, DeltaNetForCausalLM, DeltaNetModel
print(DeltaNetConfig.model_type)
AutoConfig.register("delta_net",DeltaNetConfig)
AutoModelForCausalLM.register(DeltaNetConfig,DeltaNetForCausalLM)
from opencompass.models.fla2.models import mask_deltanetConfig,mask_deltanetForCausalLM
print(mask_deltanetConfig.model_type)
AutoConfig.register("mask_deltanet",mask_deltanetConfig)
AutoModelForCausalLM.register(mask_deltanetConfig,mask_deltanetForCausalLM)
# model_path = "/mnt/jfzn/msj/delta_net-1.3B-100B"
model_path = "/mnt/jfzn/msj/train_exp/mask_deltanet_1B_rank4"
# 注意：DeltaNet 必须开 trust_remote_code!!!
# tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16, # 推荐 fp16，加速且稳定
trust_remote_code=True, # <<=== 你的代码缺了它！
device_map="cuda",
)
tokenizer = load_tokenizer(model_path, is_hf=True)
prompt = "What is the official language of China?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=100,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |