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sail/Sailor-4B - GGUF
This repo contains GGUF format model files for sail/Sailor-4B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
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-4B-Q2_K.gguf | Q2_K | 1.621 GB | smallest, significant quality loss - not recommended for most purposes |
Sailor-4B-Q3_K_S.gguf | Q3_K_S | 1.857 GB | very small, high quality loss |
Sailor-4B-Q3_K_M.gguf | Q3_K_M | 2.027 GB | very small, high quality loss |
Sailor-4B-Q3_K_L.gguf | Q3_K_L | 2.175 GB | small, substantial quality loss |
Sailor-4B-Q4_0.gguf | Q4_0 | 2.330 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
Sailor-4B-Q4_K_S.gguf | Q4_K_S | 2.344 GB | small, greater quality loss |
Sailor-4B-Q4_K_M.gguf | Q4_K_M | 2.455 GB | medium, balanced quality - recommended |
Sailor-4B-Q5_0.gguf | Q5_0 | 2.775 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
Sailor-4B-Q5_K_S.gguf | Q5_K_S | 2.775 GB | large, low quality loss - recommended |
Sailor-4B-Q5_K_M.gguf | Q5_K_M | 2.840 GB | large, very low quality loss - recommended |
Sailor-4B-Q6_K.gguf | Q6_K | 3.248 GB | very large, extremely low quality loss |
Sailor-4B-Q8_0.gguf | Q8_0 | 4.205 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-4B-GGUF --include "Sailor-4B-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-4B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
- 295
Model tree for tensorblock/Sailor-4B-GGUF
Datasets used to train tensorblock/Sailor-4B-GGUF
Evaluation results
- EM (3-Shot) on XQuAD-Thaiself-reported46.820
- F1 (3-Shot) on XQuAD-Thaiself-reported63.340
- EM (3-Shot) on TyDiQA-Indonesianself-reported53.980
- F1 (3-Shot) on TyDiQA-Indonesianself-reported73.480
- EM (3-Shot) on XQuAD-Vietnameseself-reported47.650
- F1 (3-Shot) on XQuAD-Vietnameseself-reported67.090
- EM (3-Shot) on XCOPA-Thaiself-reported53.400
- EM (3-Shot) on XCOPA-Indonesianself-reported69.200
- EM (3-Shot) on XCOPA-Vietnameseself-reported68.200
- EM (3-Shot) on M3Exam-Thaiself-reported27.880