Skip to content

Arcee

This model was released on 2025-06-18 and added to Hugging Face Transformers on 2025-06-24.

PyTorch FlashAttention SDPA

Arcee is a decoder-only transformer model based on the Llama architecture with a key modification: it uses ReLU² (ReLU-squared) activation in the MLP blocks instead of SiLU, following recent research showing improved training efficiency with squared activations. This architecture is designed for efficient training and inference while maintaining the proven stability of the Llama design.

The Arcee model is architecturally similar to Llama but uses x * relu(x) in MLP layers for improved gradient flow and is optimized for efficiency in both training and inference scenarios.

The example below demonstrates how to generate text with Arcee using Pipeline or the AutoModel.

import torch
from transformers import pipeline
pipeline = pipeline(
task="text-generation",
model="arcee-ai/AFM-4.5B",
dtype=torch.float16,
device=0
)
output = pipeline("The key innovation in Arcee is")
print(output[0]["generated_text"])
import torch
from transformers import AutoTokenizer, ArceeForCausalLM
tokenizer = AutoTokenizer.from_pretrained("arcee-ai/AFM-4.5B")
model = ArceeForCausalLM.from_pretrained(
"arcee-ai/AFM-4.5B",
dtype=torch.float16,
device_map="auto"
)
inputs = tokenizer("The key innovation in Arcee is", return_tensors="pt")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

[[autodoc]] ArceeConfig

[[autodoc]] ArceeModel - forward

[[autodoc]] ArceeForCausalLM - forward

[[autodoc]] ArceeForSequenceClassification - forward

[[autodoc]] ArceeForQuestionAnswering - forward

[[autodoc]] ArceeForTokenClassification - forward