---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sc
- af
- ca
- ro
- gl
- it
- pt
- es
- bg
- mk
- sr
- uk
- ru
- id
- ms
- th
- vi
- mg
- fr
- hu
- el
- cs
- pl
- lt
- lv
- ka
- zh
- ja
- ko
- fi
- et
- gu
- hi
- mr
- ne
- ur
- az
- kk
- ky
- tr
- uz
- ar
- he
- fa
base_model: haoranxu/X-ALMA-13B-Pretrain
tags:
- TensorBlock
- GGUF
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
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            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## haoranxu/X-ALMA-13B-Pretrain - GGUF

This repo contains GGUF format model files for [haoranxu/X-ALMA-13B-Pretrain](https://huggingface.co/haoranxu/X-ALMA-13B-Pretrain).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template


```
<s>[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [X-ALMA-13B-Pretrain-Q2_K.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q2_K.gguf) | Q2_K | 4.521 GB | smallest, significant quality loss - not recommended for most purposes |
| [X-ALMA-13B-Pretrain-Q3_K_S.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q3_K_S.gguf) | Q3_K_S | 5.270 GB | very small, high quality loss |
| [X-ALMA-13B-Pretrain-Q3_K_M.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q3_K_M.gguf) | Q3_K_M | 5.903 GB | very small, high quality loss |
| [X-ALMA-13B-Pretrain-Q3_K_L.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q3_K_L.gguf) | Q3_K_L | 6.454 GB | small, substantial quality loss |
| [X-ALMA-13B-Pretrain-Q4_0.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q4_0.gguf) | Q4_0 | 6.860 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [X-ALMA-13B-Pretrain-Q4_K_S.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q4_K_S.gguf) | Q4_K_S | 6.913 GB | small, greater quality loss |
| [X-ALMA-13B-Pretrain-Q4_K_M.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q4_K_M.gguf) | Q4_K_M | 7.326 GB | medium, balanced quality - recommended |
| [X-ALMA-13B-Pretrain-Q5_0.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q5_0.gguf) | Q5_0 | 8.356 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [X-ALMA-13B-Pretrain-Q5_K_S.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q5_K_S.gguf) | Q5_K_S | 8.356 GB | large, low quality loss - recommended |
| [X-ALMA-13B-Pretrain-Q5_K_M.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q5_K_M.gguf) | Q5_K_M | 8.596 GB | large, very low quality loss - recommended |
| [X-ALMA-13B-Pretrain-Q6_K.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q6_K.gguf) | Q6_K | 9.946 GB | very large, extremely low quality loss |
| [X-ALMA-13B-Pretrain-Q8_0.gguf](https://huggingface.co/tensorblock/X-ALMA-13B-Pretrain-GGUF/blob/main/X-ALMA-13B-Pretrain-Q8_0.gguf) | Q8_0 | 12.881 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/X-ALMA-13B-Pretrain-GGUF --include "X-ALMA-13B-Pretrain-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/X-ALMA-13B-Pretrain-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```