Instructions to use Patrick-18/Dexy-Decision-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Patrick-18/Dexy-Decision-9B with Transformers:

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

pipe = pipeline("text-generation", model="Patrick-18/Dexy-Decision-9B")

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

tokenizer = AutoTokenizer.from_pretrained("Patrick-18/Dexy-Decision-9B")
model = AutoModelForCausalLM.from_pretrained("Patrick-18/Dexy-Decision-9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Patrick-18/Dexy-Decision-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Patrick-18/Dexy-Decision-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Patrick-18/Dexy-Decision-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Patrick-18/Dexy-Decision-9B

SGLang

How to use Patrick-18/Dexy-Decision-9B 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 "Patrick-18/Dexy-Decision-9B" \
    --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": "Patrick-18/Dexy-Decision-9B",
		"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 "Patrick-18/Dexy-Decision-9B" \
        --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": "Patrick-18/Dexy-Decision-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Patrick-18/Dexy-Decision-9B with Docker Model Runner:
```
docker model run hf.co/Patrick-18/Dexy-Decision-9B
```

Dexy-Decision-9B

File size: 753 Bytes

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{
  "state": {
    "message": "Please review the attached invoice before payment. The amount is 120 USD."
  },
  "questions": {
    "document_type": {
      "type": "choice",
      "instructions": "What document type is mentioned in `message`?",
      "criteria": {
        "invoice": "An invoice requesting payment",
        "contract": "A legal agreement",
        "resume": "A job application resume"
      }
    },
    "mentions_payment": {
      "type": "noul",
      "instructions": "Does `message` mention payment?"
    },
    "urgency": {
      "type": "score",
      "instructions": "How urgent is `message`?",
      "criteria": [
        "No urgency stated",
        "Moderate urgency",
        "Immediate action required"
      ]
    }
  }
}