- ERNIE 4.5: Baidu’s Multimodal Powerhouse Goes Open Source

ERNIE 4.5: Baidu’s Multimodal Powerhouse Goes Open Source: Baidu has officially open-sourced ERNIE 4.5, a cutting-edge family of multimodal large models, setting a new benchmark for efficient training, flexible deployment, and performance across text and vision tasks. Packed with innovative engineering and thoughtful design, the ERNIE 4.5 model suite is now available under the permissive , making it free to use—even commercially.

Let’s break down the highlights of this release and what makes ERNIE 4.5 so remarkable.

🧠 What Is ERNIE 4.5?

ERNIE 4.5 is a multimodal model family featuring:

10 models, including:
- A massive 424B parameter Mixture-of-Experts (MoE) model.
- Smaller but efficient models with 47B and 3B active parameters.
- A dense 0.3B parameter model for lightweight use.
A heterogeneous MoE architecture that enables:
- Shared parameters across text and vision modalities.
- Modality-specific routing and representation.
Robust instruction-following, reasoning, and visual understanding capabilities.

These models are optimized for both pre-training and real-world downstream tasks, with multiple variants tailored for language-only, vision-language, and multimodal reasoning.

🧪 Key Innovations in ERNIE 4.5

1. Multimodal Heterogeneous MoE Pre-Training

ERNIE 4.5 uses a novel MoE setup that allows for joint training on text and vision inputs. Key architectural choices include:

Modality-isolated routing: Ensures separation of vision and text streams during expert assignment.
Token-balanced loss and router orthogonal loss: Encourage balanced learning across modalities.

These innovations allow ERNIE 4.5 to boost performance in multimodal reasoning tasks without sacrificing linguistic fluency.

2. Scaling-Efficient Infrastructure

To improve both training and inference throughput, Baidu introduced:

FP8 mixed precision and 4-bit/2-bit quantization.
Intra-node expert parallelism and dynamic resource scheduling.
PD Disaggregation for optimal load balancing during inference.

These efficiencies led to 47% Model FLOPs Utilization (MFU) during pre-training—exceptionally high for models of this scale.

3. Modality-Specific Post-Training

ERNIE 4.5 variants were post-trained for specific tasks via:

Supervised Fine-Tuning (SFT)
Direct Preference Optimization (DPO)
Unified Preference Optimization (UPO)

These methods enhance the model’s ability to follow instructions and generalize across knowledge-heavy tasks.

⚙️ ERNIEKit: Fine-Tuning Made Simple

Included in the release is ERNIEKit, a development toolkit offering:

Pretraining workflows and task alignment.
Advanced optimization like LoRA, QAT, and PTQ.
Ready-to-run YAML templates for fine-tuning configurations.

Example:

bash

erniekit train examples/configs/ERNIE-4.5-300B-A47B/sft/run_sft_wint8mix_lora_8k.yaml

This toolkit dramatically reduces overhead for developers and researchers aiming to build on top of ERNIE models.

🏎️ FastDeploy: High-Speed Model Inference

FastDeploy is Baidu’s streamlined toolkit for model deployment across hardware platforms. Highlights include:

One-line deployment for both local and service inference.
OpenAI-compatible APIs for easy integration.
Support for:
- Speculative decoding
- Low-bit quantization
- Context caching
- Multi-machine PD disaggregation

Sample code snippet:

python

from fastdeploy import LLM, SamplingParams

prompt = "Describe the universe in one sentence."
params = SamplingParams(temperature=0.7, top_p=0.9)

llm = LLM(model="baidu/ERNIE-4.5-0.3B-Paddle", max_model_len=32768)
output = llm.generate(prompt, params)

These features make ERNIE 4.5 ideal for real-time and latency-sensitive applications.

🏗️ PaddlePaddle: The Framework Behind ERNIE

PaddlePaddle (Parallel Distributed Deep Learning) is Baidu’s in-house deep learning platform. ERNIE 4.5 is fully trained and optimized on PaddlePaddle, which provides:

Support for massive model parallelism and custom operators.
A robust compiler for efficient multi-hardware training.
Rich ecosystem support including PaddleNLP, PaddleDetection, and PaddleHub.

While PaddlePaddle powers ERNIE behind the scenes, PyTorch-compatible weights are also available for interoperability with global ML workflows.

📜 Understanding Apache 2.0 Licensing

The is one of the most permissive open-source licenses. Here’s what it allows you to do:

✅ Use, modify, and distribute the software freely. ✅ Commercial use is explicitly permitted. ✅ Patent grants and contribution guidelines ensure IP clarity. ❗ You must include a copy of the license and provide attribution.

This licensing model makes it easy to adopt ERNIE 4.5 in both open-source and commercial environments with minimal legal friction.

🐿️ Final Nuts

With the release of ERNIE 4.5 under an open-source license, Baidu is extending a powerful invitation to the global AI community: build, adapt, and evolve. Whether you’re a research lab exploring new architectures or a startup integrating multimodal AI into your platform, ERNIE 4.5 offers performance, flexibility, and developer-friendly tooling that’s hard to beat.

For more technical details, explore the official release post, GitHub repository, and ERNIEKit documentation.

any questions feel free to contact us or comment below

Just A Squirrel Who Loves Art & AI Tech

Chip Dee

Deez Nuts

🚀 ERNIE 4.5: Baidu’s Multimodal Powerhouse Goes Open Source