Instructions to use Shresth12345/entity-extraction-smollm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shresth12345/entity-extraction-smollm with Transformers:

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

pipe = pipeline("text-generation", model="Shresth12345/entity-extraction-smollm")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shresth12345/entity-extraction-smollm")
model = AutoModelForCausalLM.from_pretrained("Shresth12345/entity-extraction-smollm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Shresth12345/entity-extraction-smollm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shresth12345/entity-extraction-smollm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shresth12345/entity-extraction-smollm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Shresth12345/entity-extraction-smollm

SGLang

How to use Shresth12345/entity-extraction-smollm 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 "Shresth12345/entity-extraction-smollm" \
    --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": "Shresth12345/entity-extraction-smollm",
		"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 "Shresth12345/entity-extraction-smollm" \
        --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": "Shresth12345/entity-extraction-smollm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Shresth12345/entity-extraction-smollm with Docker Model Runner:
```
docker model run hf.co/Shresth12345/entity-extraction-smollm
```

entity-extraction-smollm / usage_example.json

Shresth12345

Upload fine-tuned entity extraction model

ca19b60 verified about 1 year ago

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579 Bytes

{
  "example_usage": {
    "input": "Extract entities from: Meeting with John tomorrow at 2pm for 1 hour",
    "expected_output": {
      "action": "Meeting",
      "date": "tomorrow",
      "time": "2:00 PM",
      "attendees": [
        "John"
      ],
      "location": null,
      "duration": "1 hour",
      "recurrence": null,
      "notes": null
    }
  },
  "supported_fields": [
    "action",
    "date",
    "time",
    "attendees",
    "location",
    "duration",
    "recurrence",
    "notes"
  ],
  "input_format": "Extract entities from: [your event description]"
}

	{
	"example_usage": {
	"input": "Extract entities from: Meeting with John tomorrow at 2pm for 1 hour",
	"expected_output": {
	"action": "Meeting",
	"date": "tomorrow",
	"time": "2:00 PM",
	"attendees": [
	"John"
	],
	"location": null,
	"duration": "1 hour",
	"recurrence": null,
	"notes": null
	}
	},
	"supported_fields": [
	"action",
	"date",
	"time",
	"attendees",
	"location",
	"duration",
	"recurrence",
	"notes"
	],
	"input_format": "Extract entities from: [your event description]"
	}