diff --git a/modelTest.py b/modelTest.py
new file mode 100644
index 0000000..35517ee
--- /dev/null
+++ b/modelTest.py
@@ -0,0 +1,84 @@
+import inspect
+import json
+import math
+import os
+import re
+import sys
+import time
+from collections import defaultdict
+from itertools import chain
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Union
+
+import bmtrain as bmt
+import numpy as np
+import torch
+from bmtrain import nccl
+from bmtrain.global_var import config as bmt_config
+
+sys.path.append("../../")
+from fm9g.arguments import get_args
+from fm9g.dragonfly.modeling_dragonfly import Dragonfly
+from fm9g.dragonfly.modeling_dragonfly import DragonflyConfig
+from fm9g.dragonfly.training_tasks.pretrain_indexed import CudaPrefetcher
+from fm9g.dragonfly.training_tasks.pretrain_indexed import MixedIndexedDataset
+from fm9g.dragonfly.training_tasks.pretrain_indexed import UnpadBatchedMixedDataset
+from fm9g.utils import exporter
+from fm9g.utils import logger
+from fm9g.utils.exporter import save_every_step_stats
+from fm9g.utils.training_stats import num_non_embedding_parameters
+from fm9g.utils.training_stats import num_parameters
+
+from apps.fm9g_2b.pretrain_dragonfly import initialize
+import argparse
+def get_tokenizer(args):
+    from transformers import LlamaTokenizerFast
+    tokenizer = LlamaTokenizerFast(vocab_file=args)
+    return tokenizer
+
+def get_model(args):
+    config = DragonflyConfig.from_json_file(args.model_config)
+    config.tp = 1 if args.tp_size != 1 else 0  # TODO
+    config.pose_prob = args.pose_prob
+    config.pose_scaling_factor = args.pose_scaling_factor
+    config.rope_scaling_type = args.rope_scaling_type
+    config.rope_scaling_factor = args.rope_scaling_factor
+    config.orig_max_length = args.orig_max_length
+
+    bmt.print_rank("model config: {}".format(config))
+    bmt.print_rank("bmt config: {}".format(bmt.config))
+
+    model = Dragonfly(config)
+    if args.load is not None:
+        bmt.print_rank("args.load is not None, start to load checkpoints" + args.load)
+        exporter.load_model_ckpt(args, model)
+    else:
+        bmt.print_rank("args.load is None, start to initialize parameters")
+        bmt.init_parameters(model)
+    return model
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    group = parser.add_argument_group("model", "model configuration")
+    group.add_argument("--model-config", type=str,default="apps/fm9g_2b/model_configs/2.4b.json", help="model configuration file")
+    group.add_argument("--eps", type=float, default=1e-5, help="eps in layernorm")
+    group.add_argument("--load", type=str, default="/root/autodl-tmp/fm9g_2b_hf_models",help="Path to a directory containing a model checkpoint.")
+    group.add_argument("--tp-size", default=1, type=int)
+
+    group = parser.add_argument_group("long_context_extend", "long context extend configurations")
+    group.add_argument("--pose_prob", default=0.0, type=float, help="Sample-level PoSE probability")
+    group.add_argument("--pose_scaling_factor",default=1.0, type=float,help="PoSE scaling factor, simulate input length = max_length * pose_scaling_factor")
+    group.add_argument("--rope_scaling_type",default="", type=str, choices=["Linear", "NTK-aware", "Dynamic NTK", "NTK-by-parts", "YaRN", ""],help="Context scaling type")
+    group.add_argument("--rope_scaling_factor", default=1, type=int, help="Context scaling factor")
+    group.add_argument("--orig_max_length", default=8192, type=int, help="Original context length before context extending")
+    args = parser.parse_args()
+    bmt.init_distributed(seed=42, tp_size=1)
+    bmt.synchronize()
+    '''tokenizer'''
+    tokenizer_path = "apps/fm9g_2b/tokenizer/tokenizer.model"
+    tokenizer = get_tokenizer(tokenizer_path)
+    '''model'''
+    model = get_model(args)
+    '''inference'''