---
base_model: OpenLLM-Ro/RoMistral-7b-Instruct
datasets:
- OpenLLM-Ro/ro_sft_alpaca
- OpenLLM-Ro/ro_sft_alpaca_gpt4
- OpenLLM-Ro/ro_sft_dolly
- OpenLLM-Ro/ro_sft_selfinstruct_gpt4
- OpenLLM-Ro/ro_sft_norobots
- OpenLLM-Ro/ro_sft_orca
- OpenLLM-Ro/ro_sft_camel
- OpenLLM-Ro/ro_sft_oasst
- OpenLLM-Ro/ro_sft_ultrachat
language:
- ro
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct

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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q2_K.gguf) | Q2_K | 2.8 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.IQ3_XS.gguf) | IQ3_XS | 3.1 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.3 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.IQ3_M.gguf) | IQ3_M | 3.4 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q3_K_L.gguf) | Q3_K_L | 3.9 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.0 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.1 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.2 |  |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/RoMistral-7b-Instruct-GGUF/resolve/main/RoMistral-7b-Instruct.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |

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.

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