---
base_model: jondurbin/bagel-8x7b-v0.2
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
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/jondurbin/bagel-8x7b-v0.2

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-8x7b-v0.2-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q2_K.gguf) | Q2_K | 17.4 |  |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q3_K_S.gguf) | Q3_K_S | 20.5 |  |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q3_K_L.gguf) | Q3_K_L | 24.3 |  |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q5_K_S.gguf) | Q5_K_S | 32.3 |  |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q8_0.gguf) | Q8_0 | 49.7 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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