Add link to paper and update model card

README.md

@@ -1,11 +1,12 @@
 ---
-license: apache-2.0
 language:
 - zh
 - en
-pipeline_tag: text-generation
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 ---
+
 <div align="center">
 <img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> 
 </div>
@@ -18,6 +19,8 @@ library_name: transformers
 👋 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>
 
+This repository contains the model described in [MiniCPM4: Ultra-Efficient LLMs on End Devices](https://huggingface.co/papers/2506.07900).
+
 ## 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).🔥🔥🔥
 
@@ -28,8 +31,8 @@ MiniCPM4 series are highly efficient large language models (LLMs) designed expli
 - [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. (**<-- you are here**)
-- [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.
+- [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. (**<-- you are here**)\
+- [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.
@@ -119,13 +122,6 @@ 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
@@ -207,78 +203,4 @@ You can apply the LongRoPE factor modification by modifying the model files. Spe
     "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)
-```
-
-
-## 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}
-}
-```
+        "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, 122.3163875809991