ov-gpt2-fp32-no-cache-GGUF

tensorblock/ov-gpt2-fp32-no-cache-GGUF

ov-gpt2-fp32-no-cache-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.

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Downloads · 30 days

3% of all-time downloads

All-time downloads

1.8K

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

Access: Public
Created: Nov 9, 2024
Updated: Jan 27, 2026
SHA: eac295e4

Base models

vuiseng9/ov-gpt2-fp32-no-cache

Model card

From the Hugging Face model README

vuiseng9/ov-gpt2-fp32-no-cache - GGUF

This repo contains GGUF format model files for vuiseng9/ov-gpt2-fp32-no-cache.

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
ov-gpt2-fp32-no-cache-Q2_K.gguf	Q2_K	0.076 GB	smallest, significant quality loss - not recommended for most purposes
ov-gpt2-fp32-no-cache-Q3_K_S.gguf	Q3_K_S	0.084 GB	very small, high quality loss
ov-gpt2-fp32-no-cache-Q3_K_M.gguf	Q3_K_M	0.091 GB	very small, high quality loss
ov-gpt2-fp32-no-cache-Q3_K_L.gguf	Q3_K_L	0.095 GB	small, substantial quality loss
ov-gpt2-fp32-no-cache-Q4_0.gguf	Q4_0	0.099 GB	legacy; small, very high quality loss - prefer using Q3_K_M
ov-gpt2-fp32-no-cache-Q4_K_S.gguf	Q4_K_S	0.100 GB	small, greater quality loss
ov-gpt2-fp32-no-cache-Q4_K_M.gguf	Q4_K_M	0.105 GB	medium, balanced quality - recommended
ov-gpt2-fp32-no-cache-Q5_0.gguf	Q5_0	0.114 GB	legacy; medium, balanced quality - prefer using Q4_K_M
ov-gpt2-fp32-no-cache-Q5_K_S.gguf	Q5_K_S	0.114 GB	large, low quality loss - recommended
ov-gpt2-fp32-no-cache-Q5_K_M.gguf	Q5_K_M	0.118 GB	large, very low quality loss - recommended
ov-gpt2-fp32-no-cache-Q6_K.gguf	Q6_K	0.129 GB	very large, extremely low quality loss
ov-gpt2-fp32-no-cache-Q8_0.gguf	Q8_0	0.165 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/ov-gpt2-fp32-no-cache-GGUF --include "ov-gpt2-fp32-no-cache-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/ov-gpt2-fp32-no-cache-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Created: Nov 9, 2024
Updated: Jan 27, 2026