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----
-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.
-- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. (**<-- you are here**)
-- [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 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.
-![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}
-}
-```

+---
+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.
+- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. (**<-- you are here**)
+- [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 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."},
+# ]
+# 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)
+```
+### 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/MiniCPM4-0.5B --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/MiniCPM4-0.5B",
+    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 MiniCPM4-0.5B with vLLM:
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+model_name = "openbmb/MiniCPM4-0.5B"
+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)
+```
+Also, you can start the inference server by running the following command:
+> **Note**: In vLLM's chat API, `add_special_tokens` is `False` by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set `extra_body={"add_special_tokens": True}`.
+```bash
+vllm serve openbmb/MiniCPM4-0.5B
+```
+Then you can use the chat interface by running the following code:
+```python
+import openai
+client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")
+response = client.chat.completions.create(
+    model="openbmb/MiniCPM4-0.5B",
+    messages=[
+        {"role": "user", "content": "Write an article about Artificial Intelligence."},
+    ],
+    temperature=0.7,
+    max_tokens=1024,
+    extra_body=dict(add_special_tokens=True),  # Ensures special tokens are added for chat template
+)
+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}
+}
+```