---
license: apache-2.0
datasets:
- ai2_arc
- jondurbin/airoboros-3.2
- codeparrot/apps
- facebook/belebele
- boolq
- jondurbin/cinematika-v0.1
- drop
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- cais/mmlu
- Muennighoff/natural-instructions
- openbookqa
- piqa
- Vezora/Tested-22k-Python-Alpaca
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- spider
- squad_v2
- migtissera/Synthia-v1.3
- datasets/winogrande
- nvidia/HelpSteer
- Intel/orca_dpo_pairs
- unalignment/toxic-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- allenai/ultrafeedback_binarized_cleaned
- Squish42/bluemoon-fandom-1-1-rp-cleaned
- LDJnr/Capybara
- JULIELab/EmoBank
- kingbri/PIPPA-shareGPT
tags:
- TensorBlock
- GGUF
base_model: jondurbin/bagel-8x7b-v0.2
---

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            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## jondurbin/bagel-8x7b-v0.2 - GGUF

This repo contains GGUF format model files for [jondurbin/bagel-8x7b-v0.2](https://huggingface.co/jondurbin/bagel-8x7b-v0.2).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [bagel-8x7b-v0.2-Q2_K.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q2_K.gguf) | Q2_K | 17.311 GB | smallest, significant quality loss - not recommended for most purposes |
| [bagel-8x7b-v0.2-Q3_K_S.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q3_K_S.gguf) | Q3_K_S | 20.433 GB | very small, high quality loss |
| [bagel-8x7b-v0.2-Q3_K_M.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q3_K_M.gguf) | Q3_K_M | 22.546 GB | very small, high quality loss |
| [bagel-8x7b-v0.2-Q3_K_L.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q3_K_L.gguf) | Q3_K_L | 24.170 GB | small, substantial quality loss |
| [bagel-8x7b-v0.2-Q4_0.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q4_0.gguf) | Q4_0 | 26.444 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [bagel-8x7b-v0.2-Q4_K_S.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q4_K_S.gguf) | Q4_K_S | 26.746 GB | small, greater quality loss |
| [bagel-8x7b-v0.2-Q4_K_M.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q4_K_M.gguf) | Q4_K_M | 28.448 GB | medium, balanced quality - recommended |
| [bagel-8x7b-v0.2-Q5_0.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q5_0.gguf) | Q5_0 | 32.231 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [bagel-8x7b-v0.2-Q5_K_S.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q5_K_S.gguf) | Q5_K_S | 32.231 GB | large, low quality loss - recommended |
| [bagel-8x7b-v0.2-Q5_K_M.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q5_K_M.gguf) | Q5_K_M | 33.230 GB | large, very low quality loss - recommended |
| [bagel-8x7b-v0.2-Q6_K.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q6_K.gguf) | Q6_K | 38.381 GB | very large, extremely low quality loss |
| [bagel-8x7b-v0.2-Q8_0.gguf](https://huggingface.co/tensorblock/bagel-8x7b-v0.2-GGUF/blob/main/bagel-8x7b-v0.2-Q8_0.gguf) | Q8_0 | 49.626 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/bagel-8x7b-v0.2-GGUF --include "bagel-8x7b-v0.2-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/bagel-8x7b-v0.2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```