Instructions to use E6E831728/learned-input-table-model-classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use E6E831728/learned-input-table-model-classic with Transformers:

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

pipe = pipeline("text-generation", model="E6E831728/learned-input-table-model-classic", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("E6E831728/learned-input-table-model-classic", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use E6E831728/learned-input-table-model-classic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "E6E831728/learned-input-table-model-classic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "E6E831728/learned-input-table-model-classic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/E6E831728/learned-input-table-model-classic

SGLang

How to use E6E831728/learned-input-table-model-classic 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 "E6E831728/learned-input-table-model-classic" \
    --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": "E6E831728/learned-input-table-model-classic",
		"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 "E6E831728/learned-input-table-model-classic" \
        --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": "E6E831728/learned-input-table-model-classic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use E6E831728/learned-input-table-model-classic with Docker Model Runner:
```
docker model run hf.co/E6E831728/learned-input-table-model-classic
```

learned-input-table-model-classic / model.safetensors

E6E831728

Upload folder using huggingface_hub

d4a72bf verified 8 days ago

download

history blame contribute delete

2.15 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 658cfd0c8073eb43a0b354291e3b2bfbba3f7d03c83cef97bee4ab475ceaf7be
Size of remote file:: 2.15 GB
SHA256:: 451e5ada12b68a90ee6585aedadb54a8f2823f14a136dd35b930e48e1fd659d3

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.