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---
base_model: allenai/tulu-2-dpo-70b
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- allenai/tulu-v2-sft-mixture
language:
- en
library_name: transformers
license: other
license_link: https://allenai.org/impact-license
license_name: ai2-impact-license-low-risk
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/allenai/tulu-2-dpo-70b
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/tulu-2-dpo-70b-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/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tulu-2-dpo-70b-GGUF/resolve/main/tulu-2-dpo-70b.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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. 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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