Instructions to use onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM")
model = AutoModelForCausalLM.from_pretrained("onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM

SGLang

How to use onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM with Docker Model Runner:
```
docker model run hf.co/onnx-internal-testing/tiny-random-ModernBertDecoderForCausalLM
```

tiny-random-ModernBertDecoderForCausalLM

File size: 1,448 Bytes

{
  "architectures": [
    "ModernBertDecoderForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token": "[CLS]",
  "bos_token_id": 50281,
  "causal_mask": true,
  "classifier_activation": "gelu",
  "classifier_bias": false,
  "classifier_dropout": 0.0,
  "classifier_pooling": "mean",
  "cls_token_id": 50281,
  "decoder_bias": true,
  "deterministic_flash_attn": false,
  "embedding_dropout": 0.0,
  "eos_token": "[SEP]",
  "eos_token_id": 50282,
  "global_attn_every_n_layers": 3,
  "global_rope_theta": 160000.0,
  "gradient_checkpointing": false,
  "hidden_activation": "gelu",
  "hidden_size": 32,
  "initializer_cutoff_factor": 2.0,
  "initializer_range": 0.02,
  "intermediate_size": 32,
  "is_causal": true,
  "layer_norm_eps": 1e-05,
  "layer_types": [
    "full_attention",
    "sliding_attention"
  ],
  "local_rope_theta": 160000.0,
  "masked_prediction": false,
  "max_position_embeddings": 7999,
  "mlp_bias": false,
  "mlp_dropout": 0.0,
  "model_type": "modernbert-decoder",
  "norm_bias": false,
  "norm_eps": 1e-05,
  "num_attention_heads": 4,
  "num_hidden_layers": 2,
  "pad_token_id": 50283,
  "position_embedding_type": "sans_pos",
  "reference_compile": false,
  "sep_token_id": 50282,
  "sliding_window": 64,
  "tokenizer_class": "PreTrainedTokenizerFast",
  "torch_dtype": "float32",
  "transformers_version": "4.54.0.dev0",
  "unk_token": "[UNK]",
  "use_cache": true,
  "vocab_size": 50368
}