Improve model card: Add paper/project links and `code-generation`, `datasets`, `metrics` tags

README.md

@@ -1,123 +1,348 @@
----
-license: apache-2.0
-language:
-- zh
-- en
-pipeline_tag: text-generation
-library_name: transformers
----
-<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> 
-</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 [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.
-- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. (**<-- you are here**)
-- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
-- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
-- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
-- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
-- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
-- [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 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.
-
-- 🏗️ **Efficient Model Architecture:**
-  - InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts
-
-- 🧠 **Efficient Learning Algorithms:**
-  - Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
-  - BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
-  - Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
-
-- 📚 **High-Quality Training Data:**
-  - UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset [UltraFinweb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
-  - UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
-
-- ⚡ **Efficient Inference System:**
-  - CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
-  - 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).
-
-## 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}
-}
-```
+---
+language:
+- zh
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- code-generation
+datasets:
+- openbmb/Ultra-FineWeb
+metrics:
+- accuracy
+---
+
+<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://huggingface.co/papers/2506.07900" target="_blank">Paper</a> |
+<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
+<a href="https://huggingface.co/collections/openbmb/minicpm4-6841ab29d180257e940baa9b" target="_blank">Project Page</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 the paper [here](https://huggingface.co/papers/2506.07900).🔥🔥🔥
+
+## 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.
+- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. (**<-- you are here**)
+- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
+- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
+- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
+- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
+- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
+- [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 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.
+
+- 🏗️ **Efficient Model Architecture:**
+  - InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts
+
+- 🧠 **Efficient Learning Algorithms:**
+  - Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
+  - BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
+  - Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
+
+- 📚 **High-Quality Training Data:**
+  - UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset [UltraFinweb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
+  - UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
+
+- ⚡ **Efficient Inference System:**
+  - CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
+  - 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
+```
+
+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/long_prompt_gen.py # 生成 prompt.txt
+python3 tests/test_generate.py --prompt-file prompt.txt
+```
+
+For more details about CPM.cu, please refer to [the repo CPM.cu](https://github.com/OpenBMB/cpm.cu).
+
+### HuggingFace
+
+```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."},
+]
+prompt_text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([prompt_text], return_tensors="pt").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['input_ids']))
+]
+
+responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
+print(responses)
+```
+
+本模型支持稀疏注意力机制 InfLLM v2，可高效处理长序列推理。如需启用该功能，请先安装依赖库 [infllmv2_cuda_impl](https://github.com/OpenBMB/infllmv2_cuda_impl)
+
+运行以下命令即可安装：
+
+```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 
+```
+
+启用 InfLLM v2 需在 `config.json` 配置文件中添加 `sparse_config` 字段：
+
+```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 InfLLM v2 的行为:
+
+* `kernel_size`（默认值：32）：语义核的大小。  
+* `kernel_stride`（默认值：16）：相邻语义核的步长。  
+* `init_blocks`（默认值：1）：每个 query token 关注的初始的块数量，用于确保关注序列开头部分。  
+* `block_size`（默认值：64）：key-value blocks 的块大小。  
+* `window_size`（默认值：2048）：局部滑动窗口大小。  
+* `topk`（默认值：64）：每个 token 仅与最相关的 top-k 个 key-value blocks 计算注意力。  
+* `use_nope`（默认值：false）：是否在块选择中使用NOPE技术以提升性能。  
+* `dense_len`（默认值：8192）：稀疏注意力对短序列收益有限，当 token 长度低于此阈值时自动切换为标准注意力。设为 `-1` 则强制始终使用稀疏注意力。
+
+Minicpm4 原生支持 32,768 tokens 的上下文长度。若对话总长度（输入 + 输出）远超此限制，建议通过 RoPE 缩放技术扩展上下文。我们已验证通过调整 LongRoPE 因子，模型可稳定支持 131,072 tokens 的超长上下文。
+
+修改方法：在 `config.json` 文件中调整 `rope_scaling` 字段参数即可。
+
+```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,
+      4.615569542115128,
+      5.2684819496549835,
+      6.014438591970396,
+      6.858830049237097,
+      7.804668263503327,
+      8.851768731513417,
+      9.99600492938444,
+      11.228766118181639,
+      12.536757560834843,
+      13.902257701387796,
+      15.303885189125953,
+      16.717837610115794,
+      18.119465097853947,
+      19.484965238406907,
+      20.792956681060105,
+      22.02571786985731,
+      23.16995406772833,
+      24.217054535738416,
+      25.16289275000465,
+      26.007284207271347,
+      26.753240849586767,
+      27.40615325712662,
+      27.973003419175363,
+      28.461674954469114,
+      28.880393889607006,
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+}
+```
+
+## 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://huggingface.co/papers/2506.07900) if you find our work valuable.
+
+```bibtex
+@article{minicpm4,
+  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
+  author={MiniCPM Team},
+  year={2025},
+  journal={arXiv preprint arXiv:2506.07900}
+}
+```