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
```

E6E831728 commited on 8 days ago

Commit

a1d4c0e

verified ·

1 Parent(s): 6316699

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -6

README.md CHANGED Viewed

@@ -6,7 +6,6 @@ tags:
 - transformer
 - decoder-only
 - research
-- neurips-2026-anonymous
 ---
 # Learned Input Table Model Classic
@@ -67,8 +66,4 @@ This is a small research language model trained for architectural comparison. It
 ## Training data
-The model was trained on the same FineWeb-Edu + Cosmopedia mixture used for the matched comparisons in the paper. Dataset terms and licenses are those of the original datasets.
-## Citation
-Anonymous submission under review.

 - transformer
 - decoder-only
 - research
 ---
 # Learned Input Table Model Classic
 ## Training data
+The model was trained on the same FineWeb-Edu + Cosmopedia mixture used for the matched comparisons in the paper. Dataset terms and licenses are those of the original datasets.