---
base_model: Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.09
datasets: Neelectric/OpenR1-Math-220k_CN-K12_OLMo-2_4096toks
library_name: transformers
model_name: OLMo-2-1124-7B-Instruct_SFTv02.09
tags:
- generated_from_trainer
- open-r1
- trl
- sft
- TensorBlock
- GGUF
licence: license
---

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## Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.09 - GGUF

This repo contains GGUF format model files for [Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.09](https://huggingface.co/Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.09).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).

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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_SFTv02.09-Q2_K.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q2_K.gguf) | Q2_K | 2.858 GB | smallest, significant quality loss - not recommended for most purposes |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q3_K_S.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q3_K_S.gguf) | Q3_K_S | 3.302 GB | very small, high quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q3_K_M.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q3_K_M.gguf) | Q3_K_M | 3.652 GB | very small, high quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q3_K_L.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q3_K_L.gguf) | Q3_K_L | 3.951 GB | small, substantial quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q4_0.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q4_0.gguf) | Q4_0 | 4.217 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q4_K_S.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q4_K_S.gguf) | Q4_K_S | 4.248 GB | small, greater quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q4_K_M.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q4_K_M.gguf) | Q4_K_M | 4.472 GB | medium, balanced quality - recommended |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q5_0.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q5_0.gguf) | Q5_0 | 5.078 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q5_K_S.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q5_K_S.gguf) | Q5_K_S | 5.078 GB | large, low quality loss - recommended |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q5_K_M.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q5_K_M.gguf) | Q5_K_M | 5.209 GB | large, very low quality loss - recommended |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q6_K.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q6_K.gguf) | Q6_K | 5.992 GB | very large, extremely low quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.09-Q8_0.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.09-Q8_0.gguf) | Q8_0 | 7.760 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF --include "OLMo-2-1124-7B-Instruct_SFTv02.09-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:

```shell
huggingface-cli download tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.09-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```