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 / README.md

E6E831728

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	---
	license: apache-2.0
	library_name: transformers
	tags:
	- causal-lm
	- text-generation
	- transformer
	- decoder-only
	- research
	language:
	- en
	---

	# Learned Input Table Model Classic

	This is an anonymized research checkpoint for the paper:

	Language Models Without a Trainable Input Embedding Table: Learning from Fixed Minimal Binary Token Codes

	## Model variant

	This repository contains the learned input table baseline.

	The model is a 32-layer decoder-only Transformer with:

	- vocabulary size: 65,536
	- model width: 1024
	- number of layers: 32
	- number of attention heads: 32
	- context length: 1024
	- rotary positional embeddings
	- GELU activations
	- untied trainable output projection

	This baseline uses a standard trainable input embedding table of size:

	```text
	65,536 x 1024 = 67,108,864 trainable input parameters
	```

	## Intended use

	This checkpoint is provided for anonymous review and reproducibility of the paper's controlled comparison. It is intended for research use only.

	## Loading example

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	repo_id = "E6E831728/learned-input-table-model-classic"

	tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
	model.eval()

	prompt = "Question: What is the capital of United Kingdom?\nAnswer:"
	input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)

	with torch.no_grad():
	output_ids = model.generate(input_ids, max_new_tokens=3, do_sample=False)

	print(tokenizer.decode(output_ids[0].tolist()))
	```

	## Limitations

	This is a small research language model trained for architectural comparison. It is not instruction-tuned for safe deployment and should not be used as a production system.

	## 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.