Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.04-GGUF

tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.04-GGUF

Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.04-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.

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Downloads · 30 days

4% of all-time downloads

All-time downloads

529

Public

Repo size

55.8 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.gguf2.9 GB · 100%

At a glance

Library: transformers
Access: Public
Created: Jul 28, 2025
Updated: Jan 27, 2026
SHA: 8617feef

Base models

Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.04

Datasets

Neelectric/OpenR1-Math-cn_k12-91k

Model card

From the Hugging Face model README

Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.04 - GGUF

<div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div>

This repo contains GGUF format model files for Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.04.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

Our projects

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Prompt template

<|endoftext|><|system|>
{system_prompt}
<|user|>
{prompt}
<|assistant|>

Model file specification

Filename	Quant type	File Size	Description
OLMo-2-1124-7B-Instruct_SFTv02.04-Q2_K.gguf	Q2_K	2.858 GB	smallest, significant quality loss - not recommended for most purposes
OLMo-2-1124-7B-Instruct_SFTv02.04-Q3_K_S.gguf	Q3_K_S	3.302 GB	very small, high quality loss
OLMo-2-1124-7B-Instruct_SFTv02.04-Q3_K_M.gguf	Q3_K_M	3.652 GB	very small, high quality loss
OLMo-2-1124-7B-Instruct_SFTv02.04-Q3_K_L.gguf	Q3_K_L	3.951 GB	small, substantial quality loss
OLMo-2-1124-7B-Instruct_SFTv02.04-Q4_0.gguf	Q4_0	4.217 GB	legacy; small, very high quality loss - prefer using Q3_K_M
OLMo-2-1124-7B-Instruct_SFTv02.04-Q4_K_S.gguf	Q4_K_S	4.248 GB	small, greater quality loss
OLMo-2-1124-7B-Instruct_SFTv02.04-Q4_K_M.gguf	Q4_K_M	4.472 GB	medium, balanced quality - recommended
OLMo-2-1124-7B-Instruct_SFTv02.04-Q5_0.gguf	Q5_0	5.078 GB	legacy; medium, balanced quality - prefer using Q4_K_M
OLMo-2-1124-7B-Instruct_SFTv02.04-Q5_K_S.gguf	Q5_K_S	5.078 GB	large, low quality loss - recommended
OLMo-2-1124-7B-Instruct_SFTv02.04-Q5_K_M.gguf	Q5_K_M	5.209 GB	large, very low quality loss - recommended
OLMo-2-1124-7B-Instruct_SFTv02.04-Q6_K.gguf	Q6_K	5.992 GB	very large, extremely low quality loss
OLMo-2-1124-7B-Instruct_SFTv02.04-Q8_0.gguf	Q8_0	7.760 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/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.04-GGUF --include "OLMo-2-1124-7B-Instruct_SFTv02.04-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/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.04-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Library: transformers
Created: Jul 28, 2025
Updated: Jan 27, 2026