---
base_model: langtech-dev/Salamandra-7b-RAG-v2
language:
- bg
- ca
- code
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nn
- \no
- oc
- pl
- pt
- ro
- ru
- sh
- sk
- sl
- sr
- sv
- uk
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/langtech-dev/Salamandra-7b-RAG-v2

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-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/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q2_K.gguf) | Q2_K | 3.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q3_K_S.gguf) | Q3_K_S | 3.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q3_K_L.gguf) | Q3_K_L | 4.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.IQ4_XS.gguf) | IQ4_XS | 4.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q5_K_S.gguf) | Q5_K_S | 5.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q5_K_M.gguf) | Q5_K_M | 5.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q6_K.gguf) | Q6_K | 6.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.Q8_0.gguf) | Q8_0 | 8.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Salamandra-7b-RAG-v2-GGUF/resolve/main/Salamandra-7b-RAG-v2.f16.gguf) | f16 | 15.6 | 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. 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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