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.
@@ -52,9 +52,165 @@ MiniCPM 4 is an extremely efficient edge-side large model that has undergone eff
   - ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
 
 ## Usage
+
+### Inference with [CPM.cu](https://github.com/OpenBMB/cpm.cu)
+
+We recommend using [CPM.cu](https://github.com/OpenBMB/cpm.cu) for the inference of MiniCPM4. CPM.cu is a CUDA inference framework developed by OpenBMB, which integrates efficient sparse, speculative sampling, and quantization techniques, fully leveraging the efficiency advantages of MiniCPM4.
+
+You can install CPM.cu by running the following command:
+
+```bash
+git clone https://github.com/OpenBMB/cpm.cu.git --recursive
+cd cpm.cu
+python3 setup.py install
+```
+
+MiniCPM4 natively supports context lengths of up to 32,768 tokens. To reproduce the long-text acceleration effect in the paper, we recommend using the LongRoPE factors that have been validated. Change the `rope_scaling` field in the `config.json` file as the following to enable LongRoPE.
+```json
+{
+    ...,
+    "rope_scaling": {
+        "rope_type": "longrope", 
+        "long_factor": [0.9977997200264581, 1.014658295992452, 1.0349680404997148, 1.059429246056193, 1.0888815016813513, 1.1243301355211495, 1.166977103606075, 1.2182568066927284, 1.2798772354275727, 1.3538666751582975, 1.4426259039919596, 1.5489853358570191, 1.6762658237220625, 1.8283407612492941, 2.0096956085876183, 2.225478927469756, 2.481536379650452, 2.784415934557119, 3.1413289096347365, 3.560047844772632, 4.048719380066383, 4.752651957515948, 5.590913044973868, 6.584005926629993, 7.7532214876576155, 9.119754865903639, 10.704443927019176, 12.524994176518703, 14.59739595363613, 16.93214476166354, 19.53823297353041, 22.417131025031697, 25.568260840911098, 28.991144156566317, 32.68408069090375, 36.65174474170465, 40.90396065611201, 45.4664008671033, 50.37147343433591, 55.6804490772103, 61.470816952306556, 67.8622707390618, 75.00516023410414, 83.11898235973767, 92.50044360202462, 103.57086856690864, 116.9492274587385, 118.16074567836519, 119.18497548708795, 120.04810876261652, 120.77352815196981, 121.38182790207875, 121.89094985353891, 122.31638758099915, 122.6714244963338, 122.9673822552567, 123.21386397019609, 123.41898278254268, 123.58957065488238, 123.73136519024158, 123.84917421274221, 123.94701903496814, 124.02825801299717, 124.09569231686116],
+        "short_factor": [0.9977997200264581, 1.014658295992452, 1.0349680404997148, 1.059429246056193, 1.0888815016813513, 1.1243301355211495, 1.166977103606075, 1.2182568066927284, 1.2798772354275727, 1.3538666751582975, 1.4426259039919596, 1.5489853358570191, 1.6762658237220625, 1.8283407612492941, 2.0096956085876183, 2.225478927469756, 2.481536379650452, 2.784415934557119, 3.1413289096347365, 3.560047844772632, 4.048719380066383, 4.752651957515948, 5.590913044973868, 6.584005926629993, 7.7532214876576155, 9.119754865903639, 10.704443927019176, 12.524994176518703, 14.59739595363613, 16.93214476166354, 19.53823297353041, 22.417131025031697, 25.568260840911098, 28.991144156566317, 32.68408069090375, 36.65174474170465, 40.90396065611201, 45.4664008671033, 50.37147343433591, 55.6804490772103, 61.470816952306556, 67.8622707390618, 75.00516023410414, 83.11898235973767, 92.50044360202462, 103.57086856690864, 116.9492274587385, 118.16074567836519, 119.18497548708795, 120.04810876261652, 120.77352815196981, 121.38182790207875, 121.89094985353891, 122.31638758099915, 122.6714244963338, 122.9673822552567, 123.21386397019609, 123.41898278254268, 123.58957065488238, 123.73136519024158, 123.84917421274221, 123.94701903496814, 124.02825801299717, 124.09569231686116],
+        "original_max_position_embeddings": 32768
+    }
+}
+```
+
+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
+import torch
+torch.manual_seed(0)
+
+path = 'openbmb/MiniCPM4-8B'
+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)
+```
+
+MiniCPM4-8B supports `InfLLM v2`, a sparse attention mechanism designed for efficient long-sequence inference. It requires the [infllmv2_cuda_impl](https://github.com/OpenBMB/infllmv2_cuda_impl) library.
+
+You can install it by running the following command:
+```bash
+git clone -b feature_infer https://github.com/OpenBMB/infllmv2_cuda_impl.git
+cd infllmv2_cuda_impl
+git submodule update --init --recursive
+pip install -e . # or python setup.py install 
+```
+
+To enable InfLLM v2, you need to add the `sparse_config` field in `config.json`:
+```json
+{
+    ...,
+    "sparse_config": {
+        "kernel_size": 32,
+        "kernel_stride": 16,
+        "init_blocks": 1,
+        "block_size": 64,
+        "window_size": 2048,
+        "topk": 64,
+        "use_nope": false,
+        "dense_len": 8192
+    }
+}
+```
+
+These parameters control the behavior of InfLLM v2:
+* `kernel_size` (default: 32): size of semantic kernels.
+* `kernel_stride` (default: 16): stride between adjacent kernels.
+* `init_blocks` (default: 1): The number of initial blocks that every query token attends to. This ensures attention to the beginning of the sequence.
+* `block_size` (default: 64): block size for key-value blocks.
+* `window_size` (default: 2048): The size of the local sliding window. 
+* `topk` (default: 64): Specifies that each token computes attention with only the top-k most relevant key-value blocks.
+* `use_nope` (default: false): whether to use the NOPE technique in block selection for improved performance.
+* `dense_len` (default: 8192): Since Sparse Attention offers limited benefits for short sequences, the model can use standard (dense) attention for shorter texts. The model will use dense attention for sequences with a token length below `dense_len` and switch to sparse attention for sequences exceeding this length. Set this to `-1` to always use sparse attention regardless of sequence length.
+
+MiniCPM4 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques for effective handling of long texts. We have validated the model's performance on context lengths of up to 131,072 tokens by modifying the LongRoPE factor.
+
+You can apply the LongRoPE factor modification by modifying the model files. Specifically, in the `config.json` file, adjust the `rope_scaling` fields.
+```json
+{
+    ...,
+    "rope_scaling": {
+        "rope_type": "longrope", 
+        "long_factor": [0.9977997200264581, 1.014658295992452, 1.0349680404997148, 1.059429246056193, 1.0888815016813513, 1.1243301355211495, 1.166977103606075, 1.2182568066927284, 1.2798772354275727, 1.3538666751582975, 1.4426259039919596, 1.5489853358570191, 1.6762658237220625, 1.8283407612492941, 2.0096956085876183, 2.225478927469756, 2.481536379650452, 2.784415934557119, 3.1413289096347365, 3.560047844772632, 4.048719380066383, 4.752651957515948, 5.590913044973868, 6.584005926629993, 7.7532214876576155, 9.119754865903639, 10.704443927019176, 12.524994176518703, 14.59739595363613, 16.93214476166354, 19.53823297353041, 22.417131025031697, 25.568260840911098, 28.991144156566317, 32.68408069090375, 36.65174474170465, 40.90396065611201, 45.4664008671033, 50.37147343433591, 55.6804490772103, 61.470816952306556, 67.8622707390618, 75.00516023410414, 83.11898235973767, 92.50044360202462, 103.57086856690864, 116.9492274587385, 118.16074567836519, 119.18497548708795, 120.04810876261652, 120.77352815196981, 121.38182790207875, 121.89094985353891, 122.31638758099915, 122.6714244963338, 122.9673822552567, 123.21386397019609, 123.41898278254268, 123.58957065488238, 123.73136519024158, 123.84917421274221, 123.94701903496814, 124.02825801299717, 124.09569231686116],
+        "short_factor": [0.9977997200264581, 1.014658295992452, 1.0349680404997148, 1.059429246056193, 1.0888815016813513, 1.1243301355211495, 1.166977103606075, 1.2182568066927284, 1.2798772354275727, 1.3538666751582975, 1.4426259039919596, 1.5489853358570191, 1.6762658237220625, 1.8283407612492941, 2.0096956085876183, 2.225478927469756, 2.481536379650452, 2.784415934557119, 3.1413289096347365, 3.560047844772632, 4.048719380066383, 4.752651957515948, 5.590913044973868, 6.584005926629993, 7.7532214876576155, 9.119754865903639, 10.704443927019176, 12.524994176518703, 14.59739595363613, 16.93214476166354, 19.53823297353041, 22.417131025031697, 25.568260840911098, 28.991144156566317, 32.68408069090375, 36.65174474170465, 40.90396065611201, 45.4664008671033, 50.37147343433591, 55.6804490772103, 61.470816952306556, 67.8622707390618, 75.00516023410414, 83.11898235973767, 92.50044360202462, 103.57086856690864, 116.9492274587385, 118.16074567836519, 119.18497548708795, 120.04810876261652, 120.77352815196981, 121.38182790207875, 121.89094985353891, 122.31638758099915, 122.6714244963338, 122.9673822552567, 123.21386397019609, 123.41898278254268, 123.58957065488238, 123.73136519024158, 123.84917421274221, 123.94701903496814, 124.02825801299717, 124.09569231686116],
+        "original_max_position_embeddings": 32768
+    }
+}
+```
+
+### 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.
+
+```bash
+```
 
 ## 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.
@@ -84,8 +240,12 @@ MiniCPM4 is pre-trained on 32K long texts and achieves length extension through
 
 ## 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}
+}
 ```