MindNLP

Run HuggingFace Models on MindSpore with Zero Code Changes

The easiest way to use 200,000+ HuggingFace models on Ascend NPU, GPU, and CPU

Quick Start • Features • Installation • Why MindNLP • Documentation

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🎯 What is MindNLP?

MindNLP bridges the gap between HuggingFace's massive model ecosystem and MindSpore's hardware acceleration. With just import mindnlp, you can run any HuggingFace model on Ascend NPU, NVIDIA GPU, or CPU - no code changes required.

import mindnlp  # That's it! HuggingFace now runs on MindSpore
from transformers import pipeline

pipe = pipeline("text-generation", model="Qwen/Qwen2-0.5B")
print(pipe("Hello, I am")[0]["generated_text"])

⚡ Quick Start

Text Generation with LLMs

import mindspore
import mindnlp
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="Qwen/Qwen3-8B",
    ms_dtype=mindspore.bfloat16,
    device_map="auto"
)

messages = [{"role": "user", "content": "Write a haiku about coding"}]
print(pipe(messages, max_new_tokens=100)[0]["generated_text"][-1]["content"])

Image Generation with Stable Diffusion

import mindspore
import mindnlp
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    ms_dtype=mindspore.float16
)
image = pipe("A sunset over mountains, oil painting style").images[0]
image.save("sunset.png")

BERT for Text Classification

import mindnlp
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")

inputs = tokenizer("MindNLP is awesome!", return_tensors="pt")
outputs = model(**inputs)

✨ Features

🤗 Full HuggingFace Compatibility 200,000+ models from HuggingFace Hub Transformers - All model architectures Diffusers - Stable Diffusion, SDXL, ControlNet Zero code changes - Just import mindnlp	🚀 Hardware Acceleration Ascend NPU - Full support for Huawei AI chips NVIDIA GPU - CUDA acceleration CPU - Optimized CPU execution Multi-device - Automatic device placement
🔧 Advanced Capabilities Mixed precision - FP16/BF16 training & inference Quantization - INT8/INT4 with BitsAndBytes Distributed - Multi-GPU/NPU training PEFT/LoRA - Parameter-efficient fine-tuning	📦 Easy Integration PyTorch-compatible API via mindtorch Safetensors support for fast loading Model Hub mirrors for faster downloads Comprehensive documentation

📦 Installation

# From PyPI (recommended)
pip install mindnlp

# From source (latest features)
pip install git+https://github.com/mindspore-lab/mindnlp.git

📋 Version Compatibility

MindNLP	MindSpore	Python
0.6.x	≥2.7.1	3.10-3.11
0.5.x	2.5.0-2.7.0	3.10-3.11
0.4.x	2.2.x-2.5.0	3.9-3.11

💡 Why MindNLP?

Feature	MindNLP	PyTorch + HF	TensorFlow + HF
HuggingFace Models	✅ 200K+	✅ 200K+	⚠️ Limited
Ascend NPU Support	✅ Native	❌	❌
Zero Code Migration	✅	-	❌
Unified API	✅	✅	❌
Chinese Model Support	✅ Excellent	✅ Good	⚠️ Limited

🏆 Key Advantages

1. Instant Migration: Your existing HuggingFace code works immediately
2. Ascend Optimization: Native support for Huawei NPU hardware
3. Production Ready: Battle-tested in enterprise deployments
4. Active Community: Regular updates and responsive support

🗺️ Supported Models

MindNLP supports all models from HuggingFace Transformers and Diffusers. Here are some popular ones:

Category	Models
LLMs	Qwen, Llama, ChatGLM, Mistral, Phi, Gemma, BLOOM, Falcon
Vision	ViT, CLIP, Swin, ConvNeXt, SAM, BLIP
Audio	Whisper, Wav2Vec2, HuBERT, MusicGen
Diffusion	Stable Diffusion, SDXL, ControlNet
Multimodal	LLaVA, Qwen-VL, ALIGN

👉 View all supported models

📚 Resources

• 📖 Documentation
• 🚀 Quick Start Guide
• 📝 Tutorials
• 💬 GitHub Discussions
• 🐛 Issue Tracker

🤝 Contributing

We welcome contributions! See our Contributing Guide for details.

# Clone and install for development
git clone https://github.com/mindspore-lab/mindnlp.git
cd mindnlp
pip install -e ".[dev]"

👥 Community

Join the MindSpore NLP SIG (Special Interest Group) for discussions, events, and collaboration:

⭐ Star History

If you find MindNLP useful, please consider giving it a star ⭐ - it helps the project grow!

📄 License

MindNLP is released under the Apache 2.0 License.

📖 Citation

@misc{mindnlp2022,
    title={MindNLP: Easy-to-use and High-performance NLP and LLM Framework Based on MindSpore},
    author={MindNLP Contributors},
    howpublished={\url{https://github.com/mindspore-lab/mindnlp}},
    year={2022}
}

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Made with ❤️ by the MindSpore Lab team