HuggingFace_transformer/docs/source/en/model_doc/modernbert-decoder.md

ModernBERT Decoder

ModernBERT Decoder is the same architecture as ModernBERT but trained from scratch with a causal language modeling (CLM) objective. This allows for using the same architecture for comparing encoders and decoders. This is the decoder architecture implementation of ModernBERT, designed for autoregressive text generation tasks.

Like the encoder version, ModernBERT Decoder incorporates modern architectural improvements such as rotary positional embeddings to support sequences of up to 8192 tokens, unpadding to avoid wasting compute on padding tokens, GeGLU layers, and alternating attention patterns. However, it uses causal (unidirectional) attention to enable autoregressive generation.

Tip

Click on the ModernBERT Decoder models in the right sidebar for more examples of how to apply ModernBERT Decoder to different text generation tasks.

The example below demonstrates how to use ModernBERT Decoder for text generation with [Pipeline], [AutoModel], and from the command line.

import torch
from transformers import pipeline

generator = pipeline(
    task="text-generation",
    model="blab-jhu/test-32m-dec",
    torch_dtype=torch.float16,
    device=0
)
generator("The future of artificial intelligence is", max_length=50, num_return_sequences=1)

# For sequence classification
classifier = pipeline(
    task="text-classification",
    model="blab-jhu/test-32m-dec",
    torch_dtype=torch.float16,
    device=0
)
classifier("This movie is really great!")

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
model = AutoModelForCausalLM.from_pretrained(
    "blab-jhu/test-32m-dec",
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "The future of artificial intelligence is"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=50,
        num_return_sequences=1,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Generated text: {generated_text}")

# For sequence classification
from transformers import AutoModelForSequenceClassification

classifier_model = AutoModelForSequenceClassification.from_pretrained(
    "blab-jhu/test-32m-dec",
    torch_dtype=torch.float16,
    device_map="auto",
    num_labels=2
)

text = "This movie is really great!"
inputs = tokenizer(text, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = classifier_model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1)

print(f"Predicted class: {predicted_class.item()}")
print(f"Prediction probabilities: {predictions}")

echo "The future of artificial intelligence is" | transformers run --task text-generation --model your-username/modernbert-decoder-base --device 0

ModernBertDecoderConfig

autodoc ModernBertDecoderConfig

ModernBertDecoderModel

autodoc ModernBertDecoderModel - forward

ModernBertDecoderForCausalLM

autodoc ModernBertDecoderForCausalLM - forward

ModernBertDecoderForSequenceClassification

autodoc ModernBertDecoderForSequenceClassification - forward

Usage tips

The ModernBertDecoder model can be fine-tuned for various text generation tasks using the HuggingFace Transformers library. It supports efficient inference with features like:

• Causal attention: Ensures autoregressive generation by masking future tokens
• Sliding window attention: Alternates between local and global attention patterns for efficiency
• Rotary positional embeddings: Enables handling of longer sequences up to 8000 tokens
• FlashAttention support: Optimized attention computation for faster training and inference