File size: 7,613 Bytes

---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
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
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
base_model: BSC-LT/salamandra-7b-instruct
tags:
- TensorBlock
- GGUF
---

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## BSC-LT/salamandra-7b-instruct - GGUF

This repo contains GGUF format model files for [BSC-LT/salamandra-7b-instruct](https://huggingface.co/BSC-LT/salamandra-7b-instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4658](https://github.com/ggerganov/llama.cpp/commit/855cd0734aca26c86cc23d94aefd34f934464ac9).

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## Prompt template

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [salamandra-7b-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q2_K.gguf) | Q2_K | 3.305 GB | smallest, significant quality loss - not recommended for most purposes |
| [salamandra-7b-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q3_K_S.gguf) | Q3_K_S | 3.755 GB | very small, high quality loss |
| [salamandra-7b-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q3_K_M.gguf) | Q3_K_M | 4.048 GB | very small, high quality loss |
| [salamandra-7b-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q3_K_L.gguf) | Q3_K_L | 4.300 GB | small, substantial quality loss |
| [salamandra-7b-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q4_0.gguf) | Q4_0 | 4.647 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [salamandra-7b-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q4_K_S.gguf) | Q4_K_S | 4.672 GB | small, greater quality loss |
| [salamandra-7b-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q4_K_M.gguf) | Q4_K_M | 4.851 GB | medium, balanced quality - recommended |
| [salamandra-7b-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q5_0.gguf) | Q5_0 | 5.487 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [salamandra-7b-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q5_K_S.gguf) | Q5_K_S | 5.487 GB | large, low quality loss - recommended |
| [salamandra-7b-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q5_K_M.gguf) | Q5_K_M | 5.592 GB | large, very low quality loss - recommended |
| [salamandra-7b-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q6_K.gguf) | Q6_K | 6.380 GB | very large, extremely low quality loss |
| [salamandra-7b-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/salamandra-7b-instruct-GGUF/blob/main/salamandra-7b-instruct-Q8_0.gguf) | Q8_0 | 8.261 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/salamandra-7b-instruct-GGUF --include "salamandra-7b-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/salamandra-7b-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```