Instructions to use dbarker9/UniNER-7B-all-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dbarker9/UniNER-7B-all-onnx with Transformers:

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

pipe = pipeline("text-generation", model="dbarker9/UniNER-7B-all-onnx")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dbarker9/UniNER-7B-all-onnx")
model = AutoModelForCausalLM.from_pretrained("dbarker9/UniNER-7B-all-onnx")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dbarker9/UniNER-7B-all-onnx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dbarker9/UniNER-7B-all-onnx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dbarker9/UniNER-7B-all-onnx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dbarker9/UniNER-7B-all-onnx

SGLang

How to use dbarker9/UniNER-7B-all-onnx 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 "dbarker9/UniNER-7B-all-onnx" \
    --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": "dbarker9/UniNER-7B-all-onnx",
		"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 "dbarker9/UniNER-7B-all-onnx" \
        --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": "dbarker9/UniNER-7B-all-onnx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dbarker9/UniNER-7B-all-onnx with Docker Model Runner:
```
docker model run hf.co/dbarker9/UniNER-7B-all-onnx
```

dbarker9 commited on Nov 3, 2023

Commit

20e9a11

1 Parent(s): 512a800

Upload decoder_with_past_model.onnx_data with huggingface_hub

Browse files

Files changed (2) hide show

.gitattributes +1 -0
decoder_with_past_model.onnx_data +3 -0

.gitattributes CHANGED Viewed

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 decoder_model.onnx_data filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 decoder_model.onnx_data filter=lfs diff=lfs merge=lfs -text
+decoder_with_past_model.onnx_data filter=lfs diff=lfs merge=lfs -text

decoder_with_past_model.onnx_data ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:5e909785f9ecb495f3cf81f37d16a024b6b9666524a0e69545cddfd9fc26ec58
+size 26953662464