---
license: apache-2.0
library_name: transformers
pipeline_tag: translation
language:
- bg
- ca
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nb
- 'no'
- nn
- oc
- pl
- pt
- ro
- ru
- sl
- sk
- sr
- sv
- uk
- ast
- an
base_model: BSC-LT/salamandraTA-2b-instruct
tags:
- TensorBlock
- GGUF
---

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## BSC-LT/salamandraTA-2b-instruct - GGUF

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        Join our Discord to learn more about what we're building ↗
    </a>
</div>

This repo contains GGUF format model files for [BSC-LT/salamandraTA-2b-instruct](https://huggingface.co/BSC-LT/salamandraTA-2b-instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).

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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 |
| -------- | ---------- | --------- | ----------- |
| [salamandraTA-2b-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q2_K.gguf) | Q2_K | 1.087 GB | smallest, significant quality loss - not recommended for most purposes |
| [salamandraTA-2b-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q3_K_S.gguf) | Q3_K_S | 1.215 GB | very small, high quality loss |
| [salamandraTA-2b-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q3_K_M.gguf) | Q3_K_M | 1.277 GB | very small, high quality loss |
| [salamandraTA-2b-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q3_K_L.gguf) | Q3_K_L | 1.317 GB | small, substantial quality loss |
| [salamandraTA-2b-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q4_0.gguf) | Q4_0 | 1.410 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [salamandraTA-2b-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q4_K_S.gguf) | Q4_K_S | 1.447 GB | small, greater quality loss |
| [salamandraTA-2b-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q4_K_M.gguf) | Q4_K_M | 1.506 GB | medium, balanced quality - recommended |
| [salamandraTA-2b-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q5_0.gguf) | Q5_0 | 1.626 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [salamandraTA-2b-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q5_K_S.gguf) | Q5_K_S | 1.642 GB | large, low quality loss - recommended |
| [salamandraTA-2b-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q5_K_M.gguf) | Q5_K_M | 1.691 GB | large, very low quality loss - recommended |
| [salamandraTA-2b-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q6_K.gguf) | Q6_K | 1.920 GB | very large, extremely low quality loss |
| [salamandraTA-2b-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/BSC-LT_salamandraTA-2b-instruct-GGUF/blob/main/salamandraTA-2b-instruct-Q8_0.gguf) | Q8_0 | 2.401 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/BSC-LT_salamandraTA-2b-instruct-GGUF --include "salamandraTA-2b-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/BSC-LT_salamandraTA-2b-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```