README_EN.md · openpangu/openPangu-2.0-Flash at main

Reddit r/LocalLLaMA Models

Summary

openPangu-2.0-Flash is a 92B-parameter MoE model with 6B activated parameters, trained on Ascend, featuring 512k context length and fast thinking capabilities. It achieves strong performance on reasoning and coding benchmarks, using architectural innovations like MLA attention and multi-token prediction.

No content available
Original Article
View Cached Full Text

Cached at: 07/01/26, 12:09 PM

README_EN.md · openpangu/openPangu-2.0-Flash at main

Source: https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md Tian-Fantasea’s picture

Update LICENSE Link

8fbe71f

verified

about 6 hours ago

中文| English

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#1-introduction1. Introduction

openPangu-2.0-Flash is an MoE model trained on Ascend. The model has 92B total parameters and 6B activated parameters. Its context length is 512k. The total pretraining data contains 34T tokens. During Post-training, openPangu-2.0-Flash is trained through unified SFT with slow and fast thinking capability, multiple specialist RL traning, on-policy distillation combining multiple RL specialists.

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#2-architecture2. Architecture

openPangu-2.0-Flash brings several major architectural improvements:

  • Efficient attention: The model retains MLA for efficient inference and combines DSA and SWA in a 1:2 layer ratio. SWA layers handle local-window modeling, while DSA layers capture sparse global context. This design lowers compute, memory footprint, and memory access costs for long-context inference while preserving accuracy.
  • Residual topology: The conventional residual path is replaced with a 4-stream mHC design, improving representation diversity and generalization.
  • Multi-token prediction (MTP): The model uses three MTP heads to draft 3 additional tokens per step, enabling faster inference through self-speculative decoding.
  • Optimizer: Training uses the Muon optimizer for faster convergence.

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#3-evaluation-results3. Evaluation Results

BenchmarkMetricopenPangu-2.0-Flash-ThinkingopenPangu-2.0-Flash-Non-ThinkingGeneralCL-BenchAcc20.415.5IFEvalPrompt Strict95.989.3IFBenchPrompt Strict79.654.4AgentIF(CSR+ISR)/244.943.9SysBenchISR91.187.9MultichallengeAcc68.451.9ReasoningAIME [email protected] w/ [email protected] Feb [email protected] w/ [email protected] w/ PythonAcc80.8-BBEHHarmonic [email protected]Agent[email protected]@385.682.5Claw-EvalPass^357.758.2WildClawBenchAvg@[email protected]CodingLiveCodeBench [email protected]@376.570.9SWE-bench [email protected]@345.945.8

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#4-deployment4. Deployment

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#5-model-license5. Model License

Unless otherwise noted, the openPangu-2.0-Flash model is licensed under the terms and conditions of OPENPANGU MODEL LICENSE AGREEMENT VERSION 2.0, which is intended to be used permissively and enable the further development of artificial intelligence technologies. Please refer to theLICENSEfile located in the root directory of the model repository for details.

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#6-disclaimer6. Disclaimer

Due to the technical limitations inherent in the technology on which the openPangu-2.0-Flash model (“Model”) relies and the fact that the artificial intelligence generated content is automatically produced by Model, Huawei cannot make any guarantees regarding the following matters:

  • The output of this Model is automatically generated via AI algorithms, it does not rule out the possibility that some of the information may be flawed, unreasonable, or cause discomfort, and the generated content does not represent Huawei’s attitude or standpoint;
  • There is no guarantee that this Model is 100% accurate, reliable, functional, timely, secure and safety, error-free, uninterrupted, continuously stable, or free of any faults;
  • The output of this Model does not constitute any advices or decisions for you, and it does not guarantee the authenticity, completeness, accuracy, timeliness, legality, functionality, or practicality of the generated content. The generated content cannot replace professionals in medical, legal, and other fields in answering your questions. The generated content is for your reference only and does not represent any attitude, standpoint, or position of Huawei. You need to make independent judgments based on your actual situation, and Huawei does not assume any responsibilities.

https://huggingface.co/openpangu/openPangu-2.0-Flash/blob/main/README_EN.md#7-contact7. Contact

If you have any question, please raise an issue or contact us at[email protected].

Similar Articles

stepfun-ai/Step-3.7-Flash-GGUF

Hugging Face Models Trending

StepFun releases GGUF quantizations of their 198B-parameter sparse MoE vision-language model Step-3.7-Flash, enabling local deployment with up to 256K context and selectable reasoning levels.

stepfun-ai/Step-3.7-Flash

Hugging Face Models Trending

Step 3.7 Flash is a 198B-parameter sparse MoE vision-language model with 11B active parameters per token, supporting 256k context and three reasoning levels, designed for high-throughput agentic workflows.