Baidu ERNIE Family Overview: From the 4.5-300B Flagship to 5.5-Flash

Baidu's ERNIE (Wenxin) is one of the earliest open Chinese model families. Since 2025, Baidu has released the complete weights of ERNIE 4.5 and 5.5 generations under the baidu organization on HuggingFace, covering text, multimodal vision, OCR, and image generation lines, with PaddlePaddle, FP8, W4A8C8, and 2Bits quantization variants. For developers who want to run a domestic flagship locally or in a private environment, ERNIE is one of the heaviest options outside of Qwen. This article maps the whole family and explains what each variant is for.

Why the PaddlePaddle ecosystem matters

ERNIE's native PaddlePaddle format is not just a deployment detail — it reflects Baidu's strategy of pairing open weights with a mature serving ecosystem. Paddle Inference and Paddle Serving provide optimized deployment paths for ERNIE, and the FP8 / W4A8C8 / 2Bits variants are designed around Paddle's quantization support.

For teams already using Paddle Inference, ERNIE integrates with minimal friction. For teams on vLLM or llama.cpp, the GGUF/GPTQ community conversions bridge the gap, but expect to spend more time verifying quality than with the Qwen line (which has larger community-maintained GGUF coverage).

Practical takeaway: if your stack is Paddle-based, ERNIE is arguably the smoothest Chinese flagship to deploy; if your stack is vLLM/llama.cpp, treat ERNIE as a secondary choice after Qwen and DeepSeek.

OCR line deployment notes

The OCR models deserve a note on usage patterns. Unlike chat models, OCR models are typically called per document or per page, which changes the operational profile:

• High call volume, small inputs: OCR calls are cheap per call but can be very frequent in a document pipeline
• Batch-friendly: pages can be processed in parallel, so throughput scales with hardware
• Structured output expected: tables and forms should come back as structured data, not free text

If you are building a document pipeline, run a small benchmark with your real documents before committing to a model — accuracy differences between Unlimited-OCR and Qianfan-OCR can be significant on specific document types.

Family map: five product lines

Flagship line: ERNIE-4.5-300B-A47B and 5.5-Flash

ERNIE-4.5-300B-A47B is the 4.5-generation flagship MoE: roughly 300B total parameters, 47B active per token (A47B). That means inference overhead is close to a 47B dense model while knowledge capacity is 300B-class. Four deployment variants exist:

ERNIE-5.5-Flash-Base is the newest generation released in 2026-10, in BF16 and standard weights. As a "Flash" naming, it targets low latency; its relation to 4.5-300B is similar to Qwen3.8-Flash versus its flagship — same era, different positioning.

Reasoning line: 4.5-21B-A3B-Thinking

ERNIE-4.5-21B-A3B-Thinking is the chain-of-thought reasoning model: 21B total, 3B active, with about 58K downloads — the most-downloaded text model in the ERNIE open family. It positions like a lightweight DeepSeek-R1: it outputs a thinking process before the final answer, suited to math, code review, and logic tasks.

Deployment note: the thinking process usually lands in a separate field (like reasoning_content) with the final answer in content. In multi-turn chat, do not append thinking content to history, or you waste context and disturb the answer.

Vision line: VL-28B-A3B and VL-424B-A47B

ERNIE's vision-language models come in two tiers:

• 4.5-VL-28B-A3B-Thinking: 28B total, 3B active — light multimodal with reasoning
• 4.5-VL-424B-A47B: flagship multimodal, 424B total, 47B active

The light VL covers routine image understanding and document QA; the flagship VL covers complex visual reasoning. Both have Base/PT variants — start from Base if you fine-tune yourself.

Edge line: ERNIE-4.5-0.3B

ERNIE-4.5-0.3B is only 0.3 billion parameters, the smallest in the family, in Instruct and Base versions. Its use is embedded devices, edge demos, or as a lightweight classifier in a pipeline. Do not expect complex reasoning from it — the value is that almost any device can run it.

OCR line: Unlimited-OCR and Qianfan

OCR is the highest-downloaded line in the ERNIE open family:

• Unlimited-OCR: roughly 4.9M downloads, the most widely used version
• Unlimited-OCR-2: the new version released in 2026-10, 270K downloads in a month, multilingual support
• Qianfan-OCR: 31K downloads, paired with the Qianfan platform

For scanned documents, table extraction, and receipt recognition, a specialized OCR model is more accurate and cheaper than making a general LLM look at images.

How to deploy ERNIE

ERNIE's native format is PaddlePaddle — the biggest difference from Qwen's safetensors-plus-GGUF ecosystem:

Note: official ERNIE weights are primarily Paddle format; GGUF versions come from community conversions (mlx-community, individual users), whose quality and completeness follow each model card. Before running ERNIE on llama.cpp, confirm the GGUF conversion date and source.

Selection quick reference

FAQ

Related reading

• DeepSeek V4 Family Overview — the other thoroughly open Chinese family
• Hunyuan hy4 and A13B Local Deployment — Tencent Hunyuan's lightweight line
• Kimi K3 and GLM Integration Guide — two families callable on EXAIV
• Qwen GGUF Local Deployment Primer — local deployment basics
• Model Catalog — currently callable models on EXAIV