*This model was contributed to Hugging Face Transformers on 2025-07-21.*
# Ernie 4.5 Moe
## Overview
The Ernie 4.5 Moe model was released in the [Ernie 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) release by baidu.
This family of models contains multiple different architectures and model sizes. This model in specific targets the base text
model with mixture of experts (moe) - one with 21B total, 3B active parameters and another one with 300B total, 47B active parameters.
It uses the standard [Llama](./llama) at its core combined with a specialized MoE based on [Mixtral](./mixtral) with additional shared
experts.
Other models from the family can be found at [Ernie 4.5](./ernie4_5) and [Ernie 4.5 VL MoE](./ernie4_5_vl_moe.md).
## Usage Tips
### Generate text
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
)
# prepare the model input
inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt").to(model.device)
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# decode the generated ids
generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
```
### Distributed Generation with Tensor Parallelism
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
tp_plan="auto",
)
# prepare the model input
inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt").to(model.device)
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# decode the generated ids
generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
```
### Quantization with Bitsandbytes
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
quantization_config=BitsAndBytesConfig(load_in_4bit=True),
)
# prepare the model input
inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt").to(model.device)
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# decode the generated ids
generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
```
This model was contributed by [Anton Vlasjuk](https://huggingface.co/AntonV).
The original code can be found [here](https://github.com/PaddlePaddle/ERNIE).
## Ernie4_5_MoeConfig
[[autodoc]] Ernie4_5_MoeConfig
## Ernie4_5_MoeModel
[[autodoc]] Ernie4_5_MoeModel
- forward
## Ernie4_5_MoeForCausalLM
[[autodoc]] Ernie4_5_MoeForCausalLM
- forward
- generate
