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
```

Match "layer_types" length with "num_hidden_layers"

by tomaarsen HF Staff - opened Mar 27

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-6

Files changed (1) hide show

config.json +1 -6

config.json CHANGED Viewed

@@ -29,12 +29,7 @@
   "layer_norm_eps": 1e-05,
   "layer_types": [
     "full_attention",
-    "sliding_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "sliding_attention",
-    "full_attention"
   ],
   "local_rope_theta": 160000.0,
   "masked_prediction": false,

   "layer_norm_eps": 1e-05,
   "layer_types": [
     "full_attention",
+    "sliding_attention"
   ],
   "local_rope_theta": 160000.0,
   "masked_prediction": false,