Instructions to use E6E831728/fixed-minimal-binary-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use E6E831728/fixed-minimal-binary-code with Transformers:

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

pipe = pipeline("text-generation", model="E6E831728/fixed-minimal-binary-code", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("E6E831728/fixed-minimal-binary-code", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use E6E831728/fixed-minimal-binary-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/E6E831728/fixed-minimal-binary-code

SGLang

How to use E6E831728/fixed-minimal-binary-code 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/fixed-minimal-binary-code" \
    --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/fixed-minimal-binary-code",
		"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/fixed-minimal-binary-code" \
        --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/fixed-minimal-binary-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use E6E831728/fixed-minimal-binary-code with Docker Model Runner:
```
docker model run hf.co/E6E831728/fixed-minimal-binary-code
```

fixed-minimal-binary-code / README.md

E6E831728

Update README.md

baf891e verified 8 days ago

preview code

raw

history blame contribute delete

2.35 kB

	---
	license: apache-2.0
	library_name: transformers
	tags:
	- causal-lm
	- text-generation
	- transformer
	- decoder-only
	- fixed-embeddings
	- binary-token-codes
	- research
	language:
	- en
	---

	# Fixed Minimal Binary Code Model

	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 fixed minimal binary token-code model.

	Instead of a trainable input embedding table, each token ID is represented by its exact minimal binary code.

	For vocabulary size:

	```text
	V = 65,536
	```

	the minimal injective binary code width is:

	```text
	K = ceil(log2(V)) = 16
	```

	The 16-dimensional binary code is tiled to model width 1024.

	The model therefore uses:

	```text
	0 trainable input-embedding parameters
	```

	The output projection remains standard and trainable.

	## Architecture

	- decoder-only Transformer
	- 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

	## Loading example

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

	repo_id = "E6E831728/fixed-minimal-binary-code"

	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 France?\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()))
	```

	## Intended use

	This checkpoint is provided for anonymous review and reproducibility of the paper's main claim: a trainable input embedding table is not necessary for useful language modeling in the studied regime.

	## Limitations

	This model is a research checkpoint. It is not intended for deployment. It may produce incorrect, biased, unsafe, or nonsensical outputs.

	## 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.