Instructions to use Qusaiiii/CustomsAccountant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qusaiiii/CustomsAccountant with Transformers:

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

pipe = pipeline("text-generation", model="Qusaiiii/CustomsAccountant")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Qusaiiii/CustomsAccountant")
model = AutoModelForSequenceClassification.from_pretrained("Qusaiiii/CustomsAccountant")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qusaiiii/CustomsAccountant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Qusaiiii/CustomsAccountant

SGLang

How to use Qusaiiii/CustomsAccountant 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 "Qusaiiii/CustomsAccountant" \
    --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": "Qusaiiii/CustomsAccountant",
		"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 "Qusaiiii/CustomsAccountant" \
        --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": "Qusaiiii/CustomsAccountant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Qusaiiii/CustomsAccountant with Docker Model Runner:
```
docker model run hf.co/Qusaiiii/CustomsAccountant
```

CustomsAccountant / config.json

Qusaiiii

Upload folder using huggingface_hub

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	{
	"_name_or_path": "/content/path_to_save_model",
	"activation": "gelu",
	"architectures": [
	"DistilBertForSequenceClassification"
	],
	"attention_dropout": 0.1,
	"dim": 768,
	"dropout": 0.1,
	"hidden_dim": 3072,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1",
	"2": "LABEL_2",
	"3": "LABEL_3"
	},
	"initializer_range": 0.02,
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1,
	"LABEL_2": 2,
	"LABEL_3": 3
	},
	"max_position_embeddings": 512,
	"model_type": "distilbert",
	"n_heads": 12,
	"n_layers": 6,
	"pad_token_id": 0,
	"problem_type": "single_label_classification",
	"qa_dropout": 0.1,
	"seq_classif_dropout": 0.2,
	"sinusoidal_pos_embds": false,
	"tie_weights_": true,
	"torch_dtype": "float32",
	"transformers_version": "4.46.3",
	"vocab_size": 30522
	}