update the link for technical report

README.md

@@ -1,21 +1,18 @@
 ---
+license: apache-2.0
 language:
 - zh
 - en
-library_name: transformers
-license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
 ---
-
-MiniCPM4-8B is a highly efficient large language model (LLM) designed explicitly for end-side devices. It achieves this efficiency through systematic innovation in model architecture, training data, training algorithms, and inference systems. The details can be found in [MiniCPM4: Ultra-Efficient LLMs on End Devices](https://huggingface.co/papers/2506.07900).
-
 <div align="center">
 <img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> 
 </div>
 
 <p align="center">
 <a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
-<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a> 
+<a href="https://arxiv.org/abs/2506.07900" target="_blank">Technical Report</a> 
 </p>
 <p align="center">
 👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
@@ -83,6 +80,13 @@ MiniCPM4 natively supports context lengths of up to 32,768 tokens. To reproduce
 }
 ```
 
+After modification, you can run the following command to reproduce the long-context acceleration effect (the script will automatically download the model weights from HuggingFace)
+```bash
+python3 tests/test_generate.py
+```
+
+For more details about CPM.cu, please refer to [the repo CPM.cu](https://github.com/OpenBMB/cpm.cu).
+
 ### Inference with Transformers
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -195,4 +199,112 @@ Then you can use the chat interface by running the following command:
 ```python
 import openai
 
-client =
+client = openai.Client(base_url=f"http://localhost:30000/v1", api_key="None")
+
+response = client.chat.completions.create(
+    model="openbmb/MiniCPM4-8B",
+    messages=[
+        {"role": "user", "content": "Write an article about Artificial Intelligence."},
+    ],
+    temperature=0.7,
+    max_tokens=1024,
+)
+
+print(response.choices[0].message.content)
+```
+
+### Inference with [vLLM](https://github.com/vllm-project/vllm)
+For now, you need to install the latest version of vLLM.
+```
+pip install -U vllm \
+    --pre \
+    --extra-index-url https://wheels.vllm.ai/nightly
+```
+
+Then you can inference MiniCPM4-8B with vLLM:
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_name = "openbmb/MiniCPM4-8B"
+prompt = [{"role": "user", "content": "Please recommend 5 tourist attractions in Beijing. "}]
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
+
+llm = LLM(
+    model=model_name,
+    trust_remote_code=True,
+    max_num_batched_tokens=32768, 
+    dtype="bfloat16", 
+    gpu_memory_utilization=0.8, 
+)
+sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)
+
+outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
+
+print(outputs[0].outputs[0].text)
+```
+
+Also, you can start the inference server by running the following command:
+> **Note**: In vLLM's chat API, `add_special_tokens` is `False` by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set `extra_body={"add_special_tokens": True}`.
+
+```bash
+vllm serve openbmb/MiniCPM4-8B 
+```
+
+Then you can use the chat interface by running the following code:
+
+```python
+import openai
+
+client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")
+
+response = client.chat.completions.create(
+    model="openbmb/MiniCPM4-8B",
+    messages=[
+        {"role": "user", "content": "Write an article about Artificial Intelligence."},
+    ],
+    temperature=0.7,
+    max_tokens=1024,
+    extra_body=dict(add_special_tokens=True),  # Ensures special tokens are added for chat template
+    
+)
+
+print(response.choices[0].message.content)
+```
+
+## Evaluation Results
+On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement.
+
+![benchmark](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm4/efficiency.png?raw=true)
+
+#### Comprehensive Evaluation
+MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.
+
+![benchmark](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm4/benchmark.png?raw=true)
+
+#### Long Text Evaluation
+MiniCPM4 is pre-trained on 32K long texts and achieves length extension through YaRN technology. In the 128K long text needle-in-a-haystack task, MiniCPM4 demonstrates outstanding performance.
+
+![long-niah](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm4/128k-niah.png?raw=true)
+
+## Statement
+- As a language model, MiniCPM generates content by learning from a vast amount of text. 
+- However, it does not possess the ability to comprehend or express personal opinions or value judgments. 
+- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. 
+- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
+
+## LICENSE
+- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
+
+## Citation
+- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
+
+```bibtex
+@article{minicpm4,
+  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
+  author={MiniCPM Team},
+  year={2025}
+}
+```