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---
base_model: jondurbin/airoboros-dpo-70b-3.3
datasets:
- jondurbin/airoboros-3.2
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- glaiveai/glaive-function-calling-v2
- grimulkan/LimaRP-augmented
- piqa
- Vezora/Tested-22k-Python-Alpaca
- mattpscott/airoboros-summarization
- unalignment/toxic-dpo-v0.2
- allenai/ultrafeedback_binarized_cleaned
- argilla/distilabel-intel-orca-dpo-pairs
- jondurbin/airoboros-3.2
- jondurbin/contextual-dpo-v0.1
- jondurbin/gutenberg-dpo-v0.1
- jondurbin/py-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- jondurbin/gutenberg-dpo-v0.1
- lmsys/lmsys-chat-1m
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
license_name: llama3
quantized_by: mradermacher
tags:
- llama-3
---
## About
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static quants of https://huggingface.co/jondurbin/airoboros-dpo-70b-3.3
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-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/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/airoboros-dpo-70b-3.3-GGUF/resolve/main/airoboros-dpo-70b-3.3.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
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.
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