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---
base_model: jondurbin/bagel-dpo-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
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-dpo-8x7b-v0.2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-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-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ1_S.gguf) | i1-IQ1_S | 9.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ1_M.gguf) | i1-IQ1_M | 10.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.7 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.0 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ2_S.gguf) | i1-IQ2_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ2_M.gguf) | i1-IQ2_M | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ3_S.gguf) | i1-IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.2 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q4_0.gguf) | i1-Q4_0 | 26.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-i1-GGUF/resolve/main/bagel-dpo-8x7b-v0.2.i1-Q6_K.gguf) | i1-Q6_K | 38.5 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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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