diff --git a/README-en.md b/README-en.md
index fc447c4..e3fffbd 100644
--- a/README-en.md
+++ b/README-en.md
@@ -73,7 +73,16 @@ We release all model parameters for research and limited commercial use. In futu
   |[MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)|[MiniCPM-2B-dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)|[MiniCPM-2B-dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16)|[MiniCPM-2B-dpo-bf16](https://replicate.com/tuantuanzhang/minicpm)
   |[MiniCPM-2B-dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)|[MiniCPM-2B-dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)|[MiniCPM-2B-dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16)
   |[MiniCPM-2B-dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)|[MiniCPM-2B-dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)|[MiniCPM-2B-dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32)
+  |[MiniCPM-2B-sft-fp32-llama-format](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32-llama-format)|
+  |[MiniCPM-2B-sft-bf16-llama-format](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16-llama-format)|
+  |[MiniCPM-2B-dpo-fp16-gguf](https://huggingface.co/runfuture/MiniCPM-2B-dpo-fp16-gguf) |
+  |[MiniCPM-2B-dpo-q4km-gguf](https://huggingface.co/runfuture/MiniCPM-2B-dpo-q4km-gguf) |
 
+  Note: 
+  1. The model training was conducted in bf16 format, so inference using bf16 will yield the best results. Other formats might experience a slight performance decline due to precision issues.
+  2. The models with a '-llama-format' suffix are those where we have transformed the MiniCPM structure into the Llama structure (primarily integrating the parameterization scheme of mup into the model's own parameters). This enables users of the Llama model to try out MiniCPM at no extra cost. [See details](#llamaformat)
+  3. Thanks to [the contributor](https://github.com/runfuture) for adapting MiniCPM to [llama.cpp](https://github.com/ggerganov/llama.cpp) and [ollama](https://github.com/ollama/ollama).
+     
 * Multimodel Model
 
     | HuggingFace | ModelScope | WiseModel |
@@ -187,22 +196,19 @@ python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/promp
 #### llama.cpp and Ollama Inference
 We have supported inference with [llama.cpp](https://github.com/ggerganov/llama.cpp/) and [ollama](https://github.com/ollama/ollama).
 
-##### Ollama
-First, download ggml-model-q4_0.gguf from [huggingface](openbmb/minicpm-dpo-bf16-ggml-model-q4_0).
 
-ModelFile:
+##### llama.cpp
+1. [install llama.cpp](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build)
+2. download model in gguf format。[link-fp16](https://huggingface.co/runfuture/MiniCPM-2B-dpo-fp16-gguf) [link-q4km](https://huggingface.co/runfuture/MiniCPM-2B-dpo-q4km-gguf)
+3. In command line:
 ```
-FROM ggml-model-q4_0.gguf
-PARAMETER temperature 0.5
-PARAMETER num_ctx 4096
-TEMPLATE """<用户>{{ .Prompt }}<AI>"""
+./main -m ../../model_ckpts/download_from_hf/MiniCPM-2B-dpo-fp16-gguf.gguf --prompt "<用户>写藏头诗，藏头是龙年大吉<AI>" --temp 0.3 --top-p 0.8 --repeat-penalty 1.05
 ```
-cmd:
-```
-ollama create minicpm -f ModelFile
-ollama run minicpm
-```
-(Note: We have noticed that this quantized model has noticable performance decrease and are trying to fix it)
+More parameters adjustment [see this](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+##### ollama
+Solving [this issue](https://github.com/ollama/ollama/issues/2383)
+
 
 <p id="Community"></p>
 
diff --git a/README.md b/README.md
index 75cbfba..7ef052b 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@
  
 </p>
 
-MiniCPM 是面壁智能与清华大学自然语言处理实验室共同开源的系列端侧大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。
+MiniCPM 是面壁智能与清华大学自然语言处理实验室共同开源的系列端侧大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量, 总计2.7B参数量。
 - 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。
 - 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。
 - 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。
@@ -74,9 +74,16 @@ MiniCPM 是面壁智能与清华大学自然语言处理实验室共同开源的
   |[MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)|[MiniCPM-2B-dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)|[MiniCPM-2B-dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16)|[MiniCPM-2B-dpo-bf16](https://replicate.com/tuantuanzhang/minicpm)
   |[MiniCPM-2B-dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)|[MiniCPM-2B-dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)|[MiniCPM-2B-dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16)|
   |[MiniCPM-2B-dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)|[MiniCPM-2B-dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)|[MiniCPM-2B-dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32)|
-  |[MiniCPM-2B-sft-fp32-llama-format](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)|
+  |[MiniCPM-2B-sft-fp32-llama-format](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32-llama-format)|
+  |[MiniCPM-2B-sft-bf16-llama-format](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16-llama-format)|
+  |[MiniCPM-2B-dpo-fp16-gguf](https://huggingface.co/runfuture/MiniCPM-2B-dpo-fp16-gguf) |
+  |[MiniCPM-2B-dpo-q4km-gguf](https://huggingface.co/runfuture/MiniCPM-2B-dpo-q4km-gguf) |
+
+  注: 
+  1. 模型训练为bf16训练，因此用bf16进行推理将取得最好的效果，其他的格式会由于精度问题造成一点的性能下降。
+  2. -llama-format后缀的模型是我们将MiniCPM结构的模型转化成了Llama结构的（主要将mup的参数化方案融合进了模型本身的参数）。使得Llama模型的使用者可以零成本尝试MiniCPM。[详见](#llamaformat)
+  3. 感谢[贡献者](https://github.com/runfuture)对minicpm进行了[llama.cpp](https://github.com/ggerganov/llama.cpp)和[ollama](https://github.com/ollama/ollama)的适配
 
-  注: -llama-format后缀的模型是我们将MiniCPM结构的模型转化成了Llama结构的（主要将mup的参数化方案融合进了模型本身的参数）。使得Llama模型的使用者可以零成本尝试MiniCPM。详见
 
 * 多模态模型
   | HuggingFace | ModelScope | WiseModel |
@@ -196,23 +203,18 @@ python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/promp
 #### llama.cpp与Ollama推理
 我们支持了[llama.cpp](https://github.com/ggerganov/llama.cpp/) 推理与[ollama](https://github.com/ollama/ollama)推理.
 
+##### llama.cpp
+1. [安装llama.cpp](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build)
+2. 下载gguf形式的模型。[下载链接-fp16格式](https://huggingface.co/runfuture/MiniCPM-2B-dpo-fp16-gguf) [下载链接-q4km格式](https://huggingface.co/runfuture/MiniCPM-2B-dpo-q4km-gguf)
+3. 在命令行运行示例代码:
+```
+./main -m ../../model_ckpts/download_from_hf/MiniCPM-2B-dpo-fp16-gguf.gguf --prompt "<用户>写藏头诗，藏头是龙年大吉<AI>" --temp 0.3 --top-p 0.8 --repeat-penalty 1.05
+```
+更多参数调整[详见](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
 ##### Ollama
-先下载ggml-model-q4_0.gguf in [huggingface](openbmb/minicpm-dpo-bf16-ggml-model-q4_0)
+正在解决[这个问题](https://github.com/ollama/ollama/issues/2383)
 
-ModelFile:
-```
-FROM ggml-model-q4_0.gguf
-PARAMETER temperature 0.5
-PARAMETER num_ctx 4096
-TEMPLATE """<用户>{{ .Prompt }}<AI>"""
-```
-
-cmd:
-```
-ollama create minicpm -f ModelFile
-ollama run minicpm
-```
-（注：我们注意到这个量化后的模型性能有较大损失，正在尝试解决）
 
 
 <p id="community"></p>
@@ -270,10 +272,10 @@ ollama run minicpm
 |-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
 |Llama2-7B|35.40|36.21|31.765|32.42|31.11|44.32|12.2|27.17|13.57|1.8|33.23|75.25|42.75|75.62*|
 |Qwen-7B|49.46|47.19|59.655|58.96|60.35|57.65|17.07|42.15|41.24|5.34|37.75|83.42|64.76|75.32*|
-|Deepseek-7B|39.96|39.15|43.635|42.82|44.45|47.82|20.12|41.45|15.85|1.53|33.38|74.58*|42.15*|75.45*|
+|Deepseek-7B|39.96|39.15|43.64|42.82|44.45|47.82|20.12|41.45|15.85|1.53|33.38|74.58*|42.15*|75.45*|
 |Mistral-7B|48.97|49.96|44.54|46.12|42.96|62.69|27.44|45.2|33.13|5.0|41.06|83.92|70.73|80.43*|
 |Llama2-13B|41.48|42.44|37.19|37.32|37.06|54.71|17.07|32.55|21.15|2.25|37.92|78.87*|58.19|79.23*|
-|MPT-30B|38.17|39.82|30.715|29.34|32.09|46.56|21.95|35.36|10.31|1.56|38.22|78.66*|46.08*|79.72*|
+|MPT-30B|38.17|39.82|30.72|29.34|32.09|46.56|21.95|35.36|10.31|1.56|38.22|78.66*|46.08*|79.72*|
 |Falcon-40B|43.62|44.21|40.93|40.29|41.57|53.53|24.39|36.53|22.44|1.92|36.24|81.94*|57.68|83.26*|
 |MiniCPM-2B|52.33|52.6|51.1|51.13|51.07|53.46|50.00|47.31|53.83|10.24|36.87|85.44|68.00|68.25|
 
@@ -281,11 +283,11 @@ ollama run minicpm
 |模型|平均分|英文均分|中文均分|C-Eval|CMMLU|MMLU|HumanEval|MBPP|GSM8K|MATH|BBH|ARC-E|ARC-C|HellaSwag|
 |-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
 |TinyLlama-1.1B|25.36|25.55|24.525|25.02|24.03|24.3|6.71|19.91|2.27|0.74|28.78|60.77*|28.15*|58.33*|Qwen-1.8B|34.72|31.87|47.565|49.81|45.32|43.37|7.93|17.8|19.26|2.42|29.07|63.97*|43.69|59.28*|
-|Qwen-1.8B|34.72|31.87|47.565|49.81|45.32|43.37|7.93|17.8|19.26|2.42|29.07|63.97*|43.69|59.28*|
+|Qwen-1.8B|34.72|31.87|47.57|49.81|45.32|43.37|7.93|17.80|19.26|2.42|29.07|63.97*|43.69|59.28*|
 |Gemini Nano-3B|-|-|-|-|-|-|-|27.2(report)|22.8(report)|-|42.4(report)|-|-|-|
-|StableLM-Zephyr-3B|43.46|46.31|30.615|30.34|30.89|45.9|35.37|31.85|52.54|12.49|37.68|73.78|55.38|71.87*|
-|Phi-2-2B|48.84|54.41|23.775|23.37|24.18|52.66|47.56|55.04|57.16|3.5|43.39|86.11|71.25|73.07*|
-|MiniCPM-2B|52.33|52.6|51.1|51.13|51.07|53.46|50.00|47.31|53.83|10.24|36.87|85.44|68.00|68.25|
+|StableLM-Zephyr-3B|43.46|46.31|30.62|30.34|30.89|45.9|35.37|31.85|52.54|12.49|37.68|73.78|55.38|71.87*|
+|Phi-2-2B|48.84|54.41|23.78|23.37|24.18|52.66|47.56|55.04|57.16|3.5|43.39|86.11|71.25|73.07*|
+|MiniCPM-2B|52.33|52.6|51.10|51.13|51.07|53.46|50.00|47.31|53.83|10.24|36.87|85.44|68.00|68.25|
 
 **Chat模型比较：**
 |模型|平均分|英文均分|中文均分|C-Eval|CMMLU|MMLU|HumanEval|MBPP|GSM8K|MATH|BBH|ARC-E|ARC-C|HellaSwag|
@@ -298,7 +300,7 @@ ollama run minicpm
 |Baichuan2-7B-Chat|44.68|42.74|53.39|53.28|53.5|53|21.34|32.32|25.25|6.32|37.46|79.63|60.15|69.23*|
 |Deepseek-7B-chat|49.34|49.56|48.335|46.95|49.72|51.67|40.85|48.48|48.52|4.26|35.7|76.85|63.05|76.68*|
 |Llama2-7B-Chat|38.16|39.17|33.59|34.54|32.64|47.64|14.02|27.4|21.15|2.08|35.54|74.28|54.78|75.65*|
-|MiniCPM-2B|52.33|52.6|51.1|51.13|51.07|53.46|50.00|47.31|53.83|10.24|36.87|85.44|68.00|68.25|
+|MiniCPM-2B|52.33|52.6|51.10|51.13|51.07|53.46|50.00|47.31|53.83|10.24|36.87|85.44|68.00|68.25|
 
 **DPO后模型比较：**