gelatwo-common-gen-gpt2-large by Meihua Dang: Open Model

Runs On

What it takes to serve gelatwo-common-gen-gpt2-large (774M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	1.5 GB	1.9 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.8 GB	0.9 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.4 GB	0.5 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Meihua Dang, published under mit, revision e9e3149665bb.

gpt2-large fine-tuned on CommonGen, used as the base language model for the CommonGen experiments in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). This is a plain causal language model: it supplies the base distribution that GCD and P-GCD steer. The tractable proposal it is paired with is the HMM at which shares its 50257-token vocabulary. configs/common-gen.yaml in github.com/MhDang/gelatwo already points at this checkpoint, so the CommonGen runs need no override.

Read Meihua Dang's full model card

gpt2-large fine-tuned on CommonGen, used as the base language model for the CommonGen experiments in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926).

This is a plain causal language model: it supplies the base distribution that GCD and P-GCD steer. The tractable proposal it is paired with is the HMM at mhdang/gelatwo-common-gen-dense-4096, which shares its 50257-token vocabulary.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("mhdang/gelatwo-common-gen-gpt2-large")
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2-large")

configs/common-gen.yaml in github.com/MhDang/gelatwo already points at this checkpoint, so the CommonGen runs need no override.

Citation

@inproceedings{dang2026mitigating,
  title = {Mitigating Bias in Locally Constrained Decoding via Tractable Proposals},
  author = {Dang, Meihua and Song, Linxin and Zhang, Honghua and Zhao, Jieyu and Van den Broeck, Guy and Ermon, Stefano},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year = {2026},
}

Configuration

Architecture: GPT2LMHeadModel
Vocabulary size: 50,257
Stored precision: float32
Model type: gpt2

Identity and Version

Repository: mhdang/gelatwo-common-gen-gpt2-large
Publisher: Meihua Dang
Task: Text generation
Modality: Text
Library: Not stated by the source
Parameters: 774M parameters
Languages: Not stated by the source
Revision: e9e3149665bb7815d2bc545fc6e19d8de7e9bae5
First published: 2026-09-18
Last updated: 2026-09-18

Files and Weights

5 files, 3.1 GB in total. The weights are 1 file totalling 3.1 GB in safetensors.

Weights1 file · 3.1 GB

Configuration2 files · 1.1 KB

Documentation1 file · 1.5 KB

Repository1 file · 1.5 KB

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	3.1 GB	fd377cd04334
config.json	Configuration	906 B	—
generation_config.json	Configuration	144 B	—
README.md	Documentation	1.5 KB	—
.gitattributes	Repository	1.5 KB	—

License and Download

License: mit
Access: Open weights, no gate
Download size: 3.1 GB

Download from Meihua Dang

Released by Meihua Dang through its official repository on Hugging Face. Read the license.

Built From

Derived from openai-community/gpt2-large
Described by arXiv:2606.01926

Memory Requirements

Precision	Weights in memory
As published	3.1 GB
16-bit	1.5 GB
8-bit	0.8 GB
4-bit	0.4 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About gelatwo-common-gen-gpt2-large

How much GPU memory does gelatwo-common-gen-gpt2-large need?

About 1.9 GB at 16-bit and 0.5 GB at 4-bit: the weights (774M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run gelatwo-common-gen-gpt2-large on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use gelatwo-common-gen-gpt2-large commercially?

Yes. gelatwo-common-gen-gpt2-large is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

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