update README.md

README.md

@@ -19,17 +19,36 @@ library_name: transformers
 </p>
 
 ## What's New
-- [2025.06.06] **MiniCPM4** series are released! You can find technical report on [arXiv]().🔥🔥🔥
+- [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]().🔥🔥🔥
 
 ## MiniCPM4 Series
 - [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): TODO
-- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): TODO **(<-- you are here)**
+- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): TODO **<-- you are here**
+- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec)
+- [MiniCPM4-8B-Eagle-FRSpec-QAT](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT)
 - [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): TODO
 - [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): TODO
 - [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): TODO
 - [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): TODO
 
 ## 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:**
+  - FRSpec -- Lightweight Speculative Sampling: Achieves draft model acceleration through vocabulary pruning of draft model
+  - ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
 
 ## Usage
 ### Inference with Transformers
@@ -37,9 +56,35 @@ library_name: transformers
 ### Inference with [vLLM](https://github.com/vllm-project/vllm)
 
 ## 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 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.
 
 ## Citation
+
+- Please cite our [paper](TODO) if you find our work valuable.
+
+```bibtex
+TODO
+```