> This page is for version 0.5.0 · 26.06.
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> - 0.5.0 · 26.06: https://docs.nvidia.com/nemo/automodel/v0.5/llms.txt
> - 0.4.0 · 26.04: https://docs.nvidia.com/nemo/automodel/v0.4/llms.txt

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# ERNIE 4.5

[ERNIE 4.5](https://huggingface.co/baidu) is Baidu's dense and Mixture-of-Experts language model family with long-context text checkpoints on Hugging Face.

> **Info**
>
> |                    |                                                  |
> | ------------------ | ------------------------------------------------ |
> | **Task**           | Text Generation                                  |
> | **Architectures**  | `Ernie4_5ForCausalLM`, `Ernie4_5_MoeForCausalLM` |
> | **Parameters**     | 0.36B dense; 21B total / 3B active MoE           |
> | **Context Length** | 131,072 tokens                                   |
> | **HF Org**         | [baidu](https://huggingface.co/baidu)            |

## Available Models

* **ERNIE-4.5-0.3B-PT**: dense text checkpoint with 0.36B parameters.
* **ERNIE-4.5-21B-A3B-PT**: text MoE checkpoint with 21B total parameters and 3B activated parameters per token.

## Architectures

* `Ernie4_5ForCausalLM`: dense Hugging Face implementation path.
* `Ernie4_5_MoeForCausalLM`: custom NeMo AutoModel implementation with expert parallelism support.

## Example HF Models

| Model                | HF ID                                                                             |
| -------------------- | --------------------------------------------------------------------------------- |
| ERNIE 4.5 0.3B PT    | [`baidu/ERNIE-4.5-0.3B-PT`](https://huggingface.co/baidu/ERNIE-4.5-0.3B-PT)       |
| ERNIE 4.5 21B A3B PT | [`baidu/ERNIE-4.5-21B-A3B-PT`](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-PT) |

## Example Recipes

| Recipe                                                                                                                                                   | Description                                                            |
| -------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------- |
| [ernie4\_5\_0p3b\_hellaswag.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/ernie4_5/ernie4_5_0p3b_hellaswag.yaml)        | SFT — ERNIE 4.5 0.3B on HellaSwag with the Hugging Face implementation |
| [ernie4\_5\_21b\_a3b\_hellaswag.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/ernie4_5/ernie4_5_21b_a3b_hellaswag.yaml) | SFT — ERNIE 4.5 21B A3B on HellaSwag with TE attention and DeepEP      |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run a dense recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/ernie4_5/ernie4_5_0p3b_hellaswag.yaml
```

**4. Run the MoE recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/ernie4_5/ernie4_5_21b_a3b_hellaswag.yaml
```

#### Run with Docker

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

**2. Navigate to the AutoModel directory**:

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/ernie4_5/ernie4_5_21b_a3b_hellaswag.yaml
```

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft) and the [Large MoE Fine-Tuning Guide](/recipes-e2e-examples/large-moe-fine-tuning).

## Hugging Face Model Cards

* [baidu/ERNIE-4.5-0.3B-PT](https://huggingface.co/baidu/ERNIE-4.5-0.3B-PT)
* [baidu/ERNIE-4.5-21B-A3B-PT](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-PT)