GPT-2-optim-GGUF

tensorblock/GPT-2-optim-GGUF

GPT-2-optim-GGUF is a machine learning model from tensorblock. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

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Downloads · 30 days

3% of all-time downloads

All-time downloads

1.7K

Public

Repo size

1.4 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.gguf81.2 MB · 100%

At a glance

Library: transformers
License: mit
Access: Public
Created: Nov 25, 2024
Updated: Jan 27, 2026
SHA: 610b4db7

Try a prompt

Base models

Alignment-Lab-AI/GPT-2-optim

Datasets

HuggingFaceFW/fineweb-edu

Model card

From the Hugging Face model README

Alignment-Lab-AI/GPT-2-optim - GGUF

This repo contains GGUF format model files for Alignment-Lab-AI/GPT-2-optim.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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Model file specification

Filename	Quant type	File Size	Description
GPT-2-optim-Q2_K.gguf	Q2_K	0.081 GB	smallest, significant quality loss - not recommended for most purposes
GPT-2-optim-Q3_K_S.gguf	Q3_K_S	0.090 GB	very small, high quality loss
GPT-2-optim-Q3_K_M.gguf	Q3_K_M	0.098 GB	very small, high quality loss
GPT-2-optim-Q3_K_L.gguf	Q3_K_L	0.102 GB	small, substantial quality loss
GPT-2-optim-Q4_0.gguf	Q4_0	0.107 GB	legacy; small, very high quality loss - prefer using Q3_K_M
GPT-2-optim-Q4_K_S.gguf	Q4_K_S	0.107 GB	small, greater quality loss
GPT-2-optim-Q4_K_M.gguf	Q4_K_M	0.113 GB	medium, balanced quality - recommended
GPT-2-optim-Q5_0.gguf	Q5_0	0.122 GB	legacy; medium, balanced quality - prefer using Q4_K_M
GPT-2-optim-Q5_K_S.gguf	Q5_K_S	0.122 GB	large, low quality loss - recommended
GPT-2-optim-Q5_K_M.gguf	Q5_K_M	0.127 GB	large, very low quality loss - recommended
GPT-2-optim-Q6_K.gguf	Q6_K	0.138 GB	very large, extremely low quality loss
GPT-2-optim-Q8_0.gguf	Q8_0	0.178 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/GPT-2-optim-GGUF --include "GPT-2-optim-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/GPT-2-optim-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Library: transformers
License: mit
Languages: en
Created: Nov 25, 2024
Updated: Jan 27, 2026