---
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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</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
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## 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'
```