README.md

metadata

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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sail/Sailor-1.8B - GGUF

This repo contains GGUF format model files for sail/Sailor-1.8B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Run them on the TensorBlock client using your local machine ↗

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	Q2_K	0.847 GB	smallest, significant quality loss - not recommended for most purposes
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	Q3_K_M	1.016 GB	very small, high quality loss
Sailor-1.8B-Q3_K_L.gguf	Q3_K_L	1.056 GB	small, substantial quality loss
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	Q4_K_S	1.158 GB	small, greater quality loss
Sailor-1.8B-Q4_K_M.gguf	Q4_K_M	1.218 GB	medium, balanced quality - recommended
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	Q5_K_S	1.328 GB	large, low quality loss - recommended
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	Q6_K	1.579 GB	very large, extremely low quality loss
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

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

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:

huggingface-cli download tensorblock/Sailor-1.8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'