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---
language:
- en
- zh
- id
- th
- vi
- ms
- lo
datasets:
- cerebras/SlimPajama-627B
- Skywork/SkyPile-150B
- allenai/MADLAD-400
- cc100
tags:
- multilingual
- sea
- sailor
- TensorBlock
- GGUF
license: apache-2.0
base_model: sail/Sailor-1.8B
inference: false
model-index:
- name: Sailor-1.8B
results:
- task:
type: text-generation
dataset:
name: XQuAD-Thai
type: XQuAD-Thai
metrics:
- type: EM (3-Shot)
value: 32.72
name: EM (3-Shot)
- type: F1 (3-Shot)
value: 48.66
name: F1 (3-Shot)
- task:
type: text-generation
dataset:
name: TyDiQA-Indonesian
type: TyDiQA-Indonesian
metrics:
- type: EM (3-Shot)
value: 40.88
name: EM (3-Shot)
- type: F1 (3-Shot)
value: 65.37
name: F1 (3-Shot)
- task:
type: text-generation
dataset:
name: XQuAD-Vietnamese
type: XQuAD-Vietnamese
metrics:
- type: EM (3-Shot)
value: 34.22
name: EM (3-Shot)
- type: F1 (3-Shot)
value: 53.35
name: F1 (3-Shot)
- task:
type: text-generation
dataset:
name: XCOPA-Thai
type: XCOPA-Thai
metrics:
- type: EM (3-Shot)
value: 53.8
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: XCOPA-Indonesian
type: XCOPA-Indonesian
metrics:
- type: EM (3-Shot)
value: 64.2
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: XCOPA-Vietnamese
type: XCOPA-Vietnamese
metrics:
- type: EM (3-Shot)
value: 63.2
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: M3Exam-Thai
type: M3Exam-Thai
metrics:
- type: EM (3-Shot)
value: 25.38
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: M3Exam-Indonesian
type: M3Exam-Indonesian
metrics:
- type: EM (3-Shot)
value: 28.3
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: M3Exam-Vietnamese
type: M3Exam-Vietnamese
metrics:
- type: EM (3-Shot)
value: 34.71
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: BELEBELE-Thai
type: BELEBELE-Thai
metrics:
- type: EM (3-Shot)
value: 34.22
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: BELEBELE-Indonesian
type: BELEBELE-Indonesian
metrics:
- type: EM (3-Shot)
value: 34.89
name: EM (3-Shot)
- task:
type: text-generation
dataset:
name: BELEBELE-Vietnamese
type: BELEBELE-Vietnamese
metrics:
- type: EM (3-Shot)
value: 35.33
name: EM (3-Shot)
---
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
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>
</p>
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</div>
## sail/Sailor-1.8B - GGUF
This repo contains GGUF format model files for [sail/Sailor-1.8B](https://huggingface.co/sail/Sailor-1.8B).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](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
```
<|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 |
| -------- | ---------- | --------- | ----------- |
| [Sailor-1.8B-Q2_K.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q2_K.gguf) | Q2_K | 0.847 GB | smallest, significant quality loss - not recommended for most purposes |
| [Sailor-1.8B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q3_K_S.gguf) | Q3_K_S | 0.954 GB | very small, high quality loss |
| [Sailor-1.8B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q3_K_M.gguf) | Q3_K_M | 1.016 GB | very small, high quality loss |
| [Sailor-1.8B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q3_K_L.gguf) | Q3_K_L | 1.056 GB | small, substantial quality loss |
| [Sailor-1.8B-Q4_0.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q4_0.gguf) | Q4_0 | 1.120 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Sailor-1.8B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q4_K_S.gguf) | Q4_K_S | 1.158 GB | small, greater quality loss |
| [Sailor-1.8B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q4_K_M.gguf) | Q4_K_M | 1.218 GB | medium, balanced quality - recommended |
| [Sailor-1.8B-Q5_0.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q5_0.gguf) | Q5_0 | 1.311 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Sailor-1.8B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q5_K_S.gguf) | Q5_K_S | 1.328 GB | large, low quality loss - recommended |
| [Sailor-1.8B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q5_K_M.gguf) | Q5_K_M | 1.377 GB | large, very low quality loss - recommended |
| [Sailor-1.8B-Q6_K.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q6_K.gguf) | Q6_K | 1.579 GB | very large, extremely low quality loss |
| [Sailor-1.8B-Q8_0.gguf](https://huggingface.co/tensorblock/Sailor-1.8B-GGUF/blob/main/Sailor-1.8B-Q8_0.gguf) | Q8_0 | 1.958 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/Sailor-1.8B-GGUF --include "Sailor-1.8B-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/Sailor-1.8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|