diff --git a/README-en.md b/README-en.md
index 9b44d7d..d3477e6 100644
--- a/README-en.md
+++ b/README-en.md
@@ -57,8 +57,15 @@ Experience models with larger scale at [Luca](https://luca.cn/).
 
 <p id="3"></p>
 
-# Benchmark
+# Benchmark 
 
+  | HuggingFace | ModelScope | WiseModel |
+  |-------------|------------|-----------|
+  |[sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16)|[sft-bf16](https://modelscope.cn/models/OpenBMB/miniCPM-bf16)|[sft-bf16](https://wisemodel.cn/models/OpenBMB/miniCPM-bf16)
+  |[sft-fp32](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)|[sft-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-sft-fp32)|[sft-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32)
+  |[dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)|[dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)|[dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16)
+  |[dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)|[dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)|[dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16)
+  |[dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32)
 
 ## Multi-modal
 
@@ -149,6 +156,24 @@ Launch gradio-based demo using the following command:
 python demo/gradio_based_demo.py
 ```
 
+#### Inference with vLLM (Recommended!)
+
+* Install vLLM supporting MiniCPM
+  - vLLM 0.2.2 is adapted to MiniCPM in `inference/vllm`. More vLLM versions will be supported in the future
+```shell
+pip install inference/vllm
+```
+
+* Transfer Huggingface Transformers repo to vLLM-MiniCPM repo, where `<hf_repo_path>`, `<vllmcpm_repo_path>` are local paths.
+```shell
+python inference/convert_hf_to_vllmcpm.py --load <hf_repo_path> --save <vllmcpm_repo_path>
+```
+
+* Examples
+```shell
+cd inference/vllm/examples/infer_cpm
+python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/prompt_final.txt
+
 ## 
 
 ## LICENSE
diff --git a/README.md b/README.md
index 992395a..a43893d 100644
--- a/README.md
+++ b/README.md
@@ -48,10 +48,16 @@ XXXXXX
 <p id="2"></p>
 
 # 模型下载
+ 
+  | HuggingFace | ModelScope | WiseModel |
+  |-------------|------------|-----------|
+  |[sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16)|[sft-bf16](https://modelscope.cn/models/OpenBMB/miniCPM-bf16)|[sft-bf16](https://wisemodel.cn/models/OpenBMB/miniCPM-bf16)
+  |[sft-fp32](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)|[sft-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-sft-fp32)|[sft-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32)
+  |[dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)|[dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)|[dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16)
+  |[dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)|[dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)|[dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16)
+  |[dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32)
+
 
-  [HuggingFace仓库]()
-  [ModelScope仓库]()
-  [XX仓库]()
 
 
 <p id="3"></p>
@@ -184,7 +190,24 @@ todo
 ```shell
 python demo/gradio_based_demo.py
 ```
+#### vLLM推理部署
 
+* 安装支持MiniCPM的vLLM
+  - 我们当前支持版本为0.2.2的vLLM，未来将会支持更多版本
+```shell
+pip install inference/vllm
+```
+
+* 将Huggingface Transformers仓库转为vLLM-MiniCPM支持的格式，其中`<hf_repo_path>`, `<vllmcpm_repo_path>`均为本地路径
+```shell
+python inference/convert_hf_to_vllmcpm.py --load <hf_repo_path> --save <vllmcpm_repo_path>
+```
+
+* 测试样例
+```shell
+cd inference/vllm/examples/infer_cpm
+python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/prompt_final.txt
+```
 
 <p id="6"></p>
 
diff --git a/inference/convert_hf_to_vllmcpm.py b/inference/convert_hf_to_vllmcpm.py
new file mode 100644
index 0000000..76f541a
--- /dev/null
+++ b/inference/convert_hf_to_vllmcpm.py
@@ -0,0 +1,91 @@
+import argparse
+import json
+import os
+import shutil
+from tqdm import tqdm
+from collections import OrderedDict
+import torch
+
+def convert_model(config, ckpt):
+    # config
+    config_bmt = OrderedDict(
+        {
+            "_dtype": "bf16",
+            "activate_fn": "silu",
+            "architectures": [
+                "CPMDragonflyForCausalLM"
+            ],
+            "model_type": "cpm_dragonfly",
+            "base": 10000,
+            "dim_ff": config['intermediate_size'],
+            "dim_head": config['hidden_size'] // config['num_attention_heads'],
+            "dim_model": config['hidden_size'],
+            "dim_model_base": 256,
+            "dropout_p": 0.0,
+            "eps": config['rms_norm_eps'],
+            "init_std": config['initializer_range'],
+            "num_heads": config['num_attention_heads'],
+            "num_kv_heads": config['num_key_value_heads'],
+            "num_layers": config['num_hidden_layers'],
+            "orig_max_length": 4096,
+            "pose_prob": 0.0,
+            "pose_scaling_factor": 1.0,
+            "qk_norm": False,
+            "rope_scaling_factor": 1,
+            "rope_scaling_type": "",
+            "scale": True,
+            "scale_depth": config['scale_depth'],
+            "scale_emb": config['scale_emb'],
+            "tie_lm_head": True,
+            "tp": 0,
+            "transformers_version": "4.35.0",
+            "vocab_size": config['vocab_size']
+        }
+    )
+
+
+    model_bmt = OrderedDict()
+    model_bmt["input_embedding.weight"] = ckpt['model.embed_tokens.weight'].contiguous()
+    model_bmt["encoder.output_layernorm.weight"] = ckpt['model.norm.weight'].contiguous()
+    for lnum in tqdm(range(config_bmt['num_layers'])):
+        hf_pfx = f"model.layers.{lnum}"
+        bmt_pfx = f"encoder.layers.{lnum}"
+        model_bmt[f"{bmt_pfx}.self_att.layernorm_before_attention.weight"] = ckpt[f"{hf_pfx}.input_layernorm.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.self_att.self_attention.project_q.weight"] = ckpt[f"{hf_pfx}.self_attn.q_proj.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.self_att.self_attention.project_k.weight"] = ckpt[f"{hf_pfx}.self_attn.k_proj.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.self_att.self_attention.project_v.weight"] = ckpt[f"{hf_pfx}.self_attn.v_proj.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.self_att.self_attention.attention_out.weight"] = ckpt[f"{hf_pfx}.self_attn.o_proj.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.ffn.layernorm_before_ffn.weight"] = ckpt[f"{hf_pfx}.post_attention_layernorm.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.ffn.ffn.w_in.w_0.weight"] = ckpt[f"{hf_pfx}.mlp.gate_proj.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.ffn.ffn.w_in.w_1.weight"] = ckpt[f"{hf_pfx}.mlp.up_proj.weight"].contiguous()
+        model_bmt[f"{bmt_pfx}.ffn.ffn.w_out.weight"] = ckpt[f"{hf_pfx}.mlp.down_proj.weight"].contiguous()
+
+
+    return config_bmt, model_bmt
+
+def load_model_ckpt(args):
+    with open(os.path.join(args.load, "config.json"), 'r') as fin:
+        config = json.load(fin)
+    ckpt = torch.load(os.path.join(args.load, "pytorch_model.bin"))
+
+    os.makedirs(f"{args.save}", exist_ok=True)
+
+    # model and config
+    hf_config, hf_ckpt = convert_model(config, ckpt)
+    with open(os.path.join(args.save, "config.json"), 'w') as fout:
+        json.dump(hf_config, fout, indent=4)
+    torch.save(hf_ckpt, f"{args.save}/pytorch_model.pt")
+
+    # tokenizer
+    shutil.copyfile(f"{args.load}/tokenizer.json", f"{args.save}/tokenizer.json")
+    shutil.copyfile(f"{args.load}/tokenizer.model", f"{args.save}/tokenizer.model")
+    shutil.copyfile(f"{args.load}/special_tokens_map.json", f"{args.save}/special_tokens_map.json")
+    shutil.copyfile(f"{args.load}/tokenizer_config.json", f"{args.save}/tokenizer_config.json")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--load", type=str, default="")
+    parser.add_argument("--save", type=str, default="")
+    args = parser.parse_args()
+
+    load_model_ckpt(args)
\ No newline at end of file