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smallcloudai/Refact-1_6B-fim - GGUF
This repo contains GGUF format model files for smallcloudai/Refact-1_6B-fim.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
Refact-1_6B-fim-Q2_K.gguf | Q2_K | 0.581 GB | smallest, significant quality loss - not recommended for most purposes |
Refact-1_6B-fim-Q3_K_S.gguf | Q3_K_S | 0.673 GB | very small, high quality loss |
Refact-1_6B-fim-Q3_K_M.gguf | Q3_K_M | 0.739 GB | very small, high quality loss |
Refact-1_6B-fim-Q3_K_L.gguf | Q3_K_L | 0.795 GB | small, substantial quality loss |
Refact-1_6B-fim-Q4_0.gguf | Q4_0 | 0.857 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
Refact-1_6B-fim-Q4_K_S.gguf | Q4_K_S | 0.862 GB | small, greater quality loss |
Refact-1_6B-fim-Q4_K_M.gguf | Q4_K_M | 0.902 GB | medium, balanced quality - recommended |
Refact-1_6B-fim-Q5_0.gguf | Q5_0 | 1.030 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
Refact-1_6B-fim-Q5_K_S.gguf | Q5_K_S | 1.030 GB | large, low quality loss - recommended |
Refact-1_6B-fim-Q5_K_M.gguf | Q5_K_M | 1.053 GB | large, very low quality loss - recommended |
Refact-1_6B-fim-Q6_K.gguf | Q6_K | 1.214 GB | very large, extremely low quality loss |
Refact-1_6B-fim-Q8_0.gguf | Q8_0 | 1.571 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/Refact-1_6B-fim-GGUF --include "Refact-1_6B-fim-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/Refact-1_6B-fim-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/Refact-1_6B-fim-GGUF
Base model
smallcloudai/Refact-1_6B-fimDatasets used to train tensorblock/Refact-1_6B-fim-GGUF
Evaluation results
- pass@1 (T=0.01) on HumanEvalself-reported32.000
- pass@1 (T=0.2) on HumanEvalself-reported31.500
- pass@10 (T=0.8) on HumanEvalself-reported53.000
- pass@100 (T=0.8) on HumanEvalself-reported76.900
- pass@1 (T=0.2) on HumanEvalSynthesize Pythonself-reported35.800
- pass@1 (T=0.2) on HumanEvalSynthesize Pythonself-reported31.600
- pass@1 (T=0.2) on HumanEvalSynthesize Pythonself-reported29.100
- pass@1 (T=0.2) on HumanEvalSynthesize Pythonself-reported-1.000
- pass@1 (T=0.2) on HumanEvalSynthesize Pythonself-reported26.300
- pass@1 (T=0.2) on HumanEvalSynthesize Pythonself-reported-1.000