openbmb
/

MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu

@@ -1,81 +1,82 @@
----
-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.
-- [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. (**<-- you are here**)
-- [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
-MiniCPM4-8B-Eagle-FRSpec-QAT is a quantization-friendly Eagle model trained with MiniCPM4-8B in QAT. It clould be apply on our inference framework [cpm.cu](https://github.com/OpenBMB/cpm.cu) with FRSpec,  accelerating the generation speed by 7 times compared to Qwen3-8B.
-## Usage
-### Inference with cpm.cu
-```
-# case 1: verify model is fp16 or bf16
-cd cpm.cu/tests
-python3 test_generate.py \
-    --no-apply-quant \
-    --apply-eagle-quant
-# case 2: verify model is quanted with Marlin (W4A16, group size = 128)
-cd cpm.cu/tests
-python3 test_generate.py \
-    --apply-quant \
-    --apply-eagle-quant
-```
-## Evaluation
-Tested on two representative edge devices, the Jetson AGX Orin and RTX 4090, MiniCPM4 with MiniCPM4-8B-Eagle-FRSpec-QAT demonstrates significantly superior processing speed over models of comparable size for long-text processing tasks. Its performance advantage becomes increasingly pronounced as the text length increases. On the Jetson AGX Orin platform, MiniCPM4 achieves approximately a 7x improvement in generation speed compared to Qwen3-8B.
-![speed test](https://raw.githubusercontent.com/OpenBMB/MiniCPM/refs/heads/minicpm-4/assets/minicpm4/efficiency.png)
-## 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
+---
+<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.
+- [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. (**<-- you are here**)
+- [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
+MiniCPM4-8B-Eagle-FRSpec-QAT is a quantization-friendly Eagle model trained with MiniCPM4-8B in QAT. The model was introduced in [MiniCPM4: Ultra-Efficient LLMs on End Devices](https://huggingface.co/papers/2506.07900). It clould be apply on our inference framework [cpm.cu](https://github.com/OpenBMB/cpm.cu) with FRSpec,  accelerating the generation speed by 7 times compared to Qwen3-8B.
+## Usage
+### Inference with cpm.cu
+```
+# case 1: verify model is fp16 or bf16
+cd cpm.cu/tests
+python3 test_generate.py \
+    --no-apply-quant \
+    --apply-eagle-quant
+# case 2: verify model is quanted with Marlin (W4A16, group size = 128)
+cd cpm.cu/tests
+python3 test_generate.py \
+    --apply-quant \
+    --apply-eagle-quant
+```
+## Evaluation
+Tested on two representative edge devices, the Jetson AGX Orin and RTX 4090, MiniCPM4 with MiniCPM4-8B-Eagle-FRSpec-QAT demonstrates significantly superior processing speed over models of comparable size for long-text processing tasks. Its performance advantage becomes increasingly pronounced as the text length increases. On the Jetson AGX Orin platform, MiniCPM4 achieves approximately a 7x improvement in generation speed compared to Qwen3-8B.
+![speed test](https://raw.githubusercontent.com/OpenBMB/MiniCPM/refs/heads/minicpm-4/assets/minicpm4/efficiency.png)
+## 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}
+}
+```