Ernie 4.5 Moe

This model was released on 2025-06-30 and added 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 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 at its core combined with a specialized MoE based on Mixtral with additional shared experts.

Other models from the family can be found at Ernie 4.5 and Ernie 4.5 VL MoE.

Usage Tips

Generate text

import torch
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",
    dtype=torch.bfloat16,
)

# prepare the model input
inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
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

import torch
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",
    dtype=torch.bfloat16,
    tp_plan="auto",
)

# prepare the model input
inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
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

import torch
from transformers import BitsAndBytesConfig, 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",
    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")
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. The original code can be found here.

Ernie4_5_MoeConfig

[[autodoc]] Ernie4_5_MoeConfig

Ernie4_5_MoeModel

[[autodoc]] Ernie4_5_MoeModel - forward

Ernie4_5_MoeForCausalLM

[[autodoc]] Ernie4_5_MoeForCausalLM - forward - generate