update README.md

README.md

@@ -12,14 +12,14 @@ library_name: transformers
 
 <p align="center">
 <a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
-<a href="TODO" target="_blank">Technical Report</a> 
+<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" 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>
 </p>
 
 ## What's New
-- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report on [arXiv](TODO).🔥🔥🔥
+- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥
 
 ## MiniCPM4 Series
 MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
@@ -30,7 +30,7 @@ MiniCPM4 series are highly efficient large language models (LLMs) designed expli
 - [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
 - [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
 - [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
-- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy user requirements.
+- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
 
 ## Introduction
 MiniCPM 4 is an extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.
@@ -53,8 +53,106 @@ MiniCPM 4 is an extremely efficient edge-side large model that has undergone eff
 
 ## Usage
 ### Inference with Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+torch.manual_seed(0)
+
+path = 'openbmb/MiniCPM4-0.5B'
+device = "cuda"
+tokenizer = AutoTokenizer.from_pretrained(path)
+model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
+
+# User can directly use the chat interface
+responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
+print(responds)
+
+# User can also use the generate interface
+# messages = [
+#     {"role": "user", "content": "Write an article about Artificial Intelligence."},
+# ]
+# model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)
+
+# model_outputs = model.generate(
+#     model_inputs,
+#     max_new_tokens=1024,
+#     top_p=0.7,
+#     temperature=0.7
+# )
+# output_token_ids = [
+#     model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
+# ]
+
+# responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
+# print(responses)
+```
+
+### Inference with [SGLang](https://github.com/sgl-project/sglang)
+
+For now, you need to install our forked version of SGLang.
+```bash
+git clone -b openbmb https://github.com/OpenBMB/sglang.git
+cd sglang
+
+pip install --upgrade pip
+pip install -e "python[all]"
+```
+
+You can start the inference server by running the following command:
+```bash
+python -m sglang.launch_server --model openbmb/MiniCPM4-8B --trust-remote-code --port 30000 --chat-template chatml
+```
+
+Then you can use the chat interface by running the following command:
+```python
+import openai
+
+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)
+```
 
 ## 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.
@@ -78,14 +176,15 @@ MiniCPM4 is pre-trained on 32K long texts and achieves length extension through
 - Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
 
 ## LICENSE
-- This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
-- The usage of MiniCPM model weights must strictly follow [MiniCPM Model License](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
-- The models and weights of MiniCPM are completely free for academic research. after filling out a [questionnaire](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use.
+- 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](TODO) if you find our work valuable.
+- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
 
 ```bibtex
-TODO
+@article{minicpm4,
+  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
+  author={MiniCPM Team},
+  year={2025}
+}
 ```