TomGrc/FusionNet - GGUF
This repo contains GGUF format model files for TomGrc/FusionNet.
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-Q2_K.gguf | Q2_K | 4.003 GB | smallest, significant quality loss - not recommended for most purposes |
| FusionNet-Q3_K_S.gguf | Q3_K_S | 4.665 GB | very small, high quality loss |
| FusionNet-Q3_K_M.gguf | Q3_K_M | 5.196 GB | very small, high quality loss |
| FusionNet-Q3_K_L.gguf | Q3_K_L | 5.651 GB | small, substantial quality loss |
| FusionNet-Q4_0.gguf | Q4_0 | 6.072 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| FusionNet-Q4_K_S.gguf | Q4_K_S | 6.119 GB | small, greater quality loss |
| FusionNet-Q4_K_M.gguf | Q4_K_M | 6.462 GB | medium, balanced quality - recommended |
| FusionNet-Q5_0.gguf | Q5_0 | 7.397 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| FusionNet-Q5_K_S.gguf | Q5_K_S | 7.397 GB | large, low quality loss - recommended |
| FusionNet-Q5_K_M.gguf | Q5_K_M | 7.598 GB | large, very low quality loss - recommended |
| FusionNet-Q6_K.gguf | Q6_K | 8.805 GB | very large, extremely low quality loss |
| FusionNet-Q8_0.gguf | Q8_0 | 11.404 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-GGUF --include "FusionNet-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-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-GGUF
Base model
TomGrc/FusionNetEvaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard71.250
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.420
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.360
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard71.950
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.270
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard65.050

