Arcee
This model was released on 2025-06-18 and added to Hugging Face Transformers on 2025-06-24.
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 torchfrom 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 torchfrom 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))ArceeConfig
Section titled “ArceeConfig”[[autodoc]] ArceeConfig
ArceeModel
Section titled “ArceeModel”[[autodoc]] ArceeModel - forward
ArceeForCausalLM
Section titled “ArceeForCausalLM”[[autodoc]] ArceeForCausalLM - forward
ArceeForSequenceClassification
Section titled “ArceeForSequenceClassification”[[autodoc]] ArceeForSequenceClassification - forward
ArceeForQuestionAnswering
Section titled “ArceeForQuestionAnswering”[[autodoc]] ArceeForQuestionAnswering - forward
ArceeForTokenClassification
Section titled “ArceeForTokenClassification”[[autodoc]] ArceeForTokenClassification - forward