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ibm-research/PowerMoE-3b - GGUF
This repo contains GGUF format model files for ibm-research/PowerMoE-3b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.
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Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
PowerMoE-3b-Q2_K.gguf | Q2_K | 1.266 GB | smallest, significant quality loss - not recommended for most purposes |
PowerMoE-3b-Q3_K_S.gguf | Q3_K_S | 1.488 GB | very small, high quality loss |
PowerMoE-3b-Q3_K_M.gguf | Q3_K_M | 1.644 GB | very small, high quality loss |
PowerMoE-3b-Q3_K_L.gguf | Q3_K_L | 1.774 GB | small, substantial quality loss |
PowerMoE-3b-Q4_0.gguf | Q4_0 | 1.926 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
PowerMoE-3b-Q4_K_S.gguf | Q4_K_S | 1.942 GB | small, greater quality loss |
PowerMoE-3b-Q4_K_M.gguf | Q4_K_M | 2.059 GB | medium, balanced quality - recommended |
PowerMoE-3b-Q5_0.gguf | Q5_0 | 2.338 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
PowerMoE-3b-Q5_K_S.gguf | Q5_K_S | 2.338 GB | large, low quality loss - recommended |
PowerMoE-3b-Q5_K_M.gguf | Q5_K_M | 2.407 GB | large, very low quality loss - recommended |
PowerMoE-3b-Q6_K.gguf | Q6_K | 2.776 GB | very large, extremely low quality loss |
PowerMoE-3b-Q8_0.gguf | Q8_0 | 3.593 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/ibm-research_PowerMoE-3b-GGUF --include "PowerMoE-3b-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/ibm-research_PowerMoE-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/ibm-research_PowerMoE-3b-GGUF
Base model
ibm-research/PowerMoE-3bEvaluation results
- accuracy-norm on ARCself-reported58.100
- accuracy on ARCself-reported65.000
- accuracy-norm on ARCself-reported71.500
- accuracy-norm on ARCself-reported41.000
- accuracy-norm on ARCself-reported79.100
- accuracy-norm on ARCself-reported65.000
- accuracy on ARCself-reported42.800
- accuracy on ARCself-reported25.900
- accuracy on ARCself-reported14.800
- pass@1 on humanevalself-reported20.100