TomGrc/FusionNet_passthrough - GGUF
This repo contains GGUF format model files for TomGrc/FusionNet_passthrough.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
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Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| FusionNet_passthrough-Q2_K.gguf | Q2_K | 7.855 GB | smallest, significant quality loss - not recommended for most purposes |
| FusionNet_passthrough-Q3_K_S.gguf | Q3_K_S | 9.165 GB | very small, high quality loss |
| FusionNet_passthrough-Q3_K_M.gguf | Q3_K_M | 10.226 GB | very small, high quality loss |
| FusionNet_passthrough-Q3_K_L.gguf | Q3_K_L | 11.137 GB | small, substantial quality loss |
| FusionNet_passthrough-Q4_0.gguf | Q4_0 | 11.963 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| FusionNet_passthrough-Q4_K_S.gguf | Q4_K_S | 12.053 GB | small, greater quality loss |
| FusionNet_passthrough-Q4_K_M.gguf | Q4_K_M | 12.741 GB | medium, balanced quality - recommended |
| FusionNet_passthrough-Q5_0.gguf | Q5_0 | 14.596 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| FusionNet_passthrough-Q5_K_S.gguf | Q5_K_S | 14.596 GB | large, low quality loss - recommended |
| FusionNet_passthrough-Q5_K_M.gguf | Q5_K_M | 14.997 GB | large, very low quality loss - recommended |
| FusionNet_passthrough-Q6_K.gguf | Q6_K | 17.395 GB | very large, extremely low quality loss |
| FusionNet_passthrough-Q8_0.gguf | Q8_0 | 22.529 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/FusionNet_passthrough-GGUF --include "FusionNet_passthrough-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/FusionNet_passthrough-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Hardware compatibility
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Model tree for tensorblock/FusionNet_passthrough-GGUF
Base model
TomGrc/FusionNet_passthroughEvaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.450
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.720
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.280
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard67.650
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.290
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard24.260

