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tensoropera/Fox-1-1.6B - GGUF
This repo contains GGUF format model files for tensoropera/Fox-1-1.6B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Prompt template
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Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
Fox-1-1.6B-Q2_K.gguf | Q2_K | 0.860 GB | smallest, significant quality loss - not recommended for most purposes |
Fox-1-1.6B-Q3_K_S.gguf | Q3_K_S | 0.927 GB | very small, high quality loss |
Fox-1-1.6B-Q3_K_M.gguf | Q3_K_M | 0.987 GB | very small, high quality loss |
Fox-1-1.6B-Q3_K_L.gguf | Q3_K_L | 1.039 GB | small, substantial quality loss |
Fox-1-1.6B-Q4_0.gguf | Q4_0 | 1.078 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
Fox-1-1.6B-Q4_K_S.gguf | Q4_K_S | 1.083 GB | small, greater quality loss |
Fox-1-1.6B-Q4_K_M.gguf | Q4_K_M | 1.117 GB | medium, balanced quality - recommended |
Fox-1-1.6B-Q5_0.gguf | Q5_0 | 1.221 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
Fox-1-1.6B-Q5_K_S.gguf | Q5_K_S | 1.221 GB | large, low quality loss - recommended |
Fox-1-1.6B-Q5_K_M.gguf | Q5_K_M | 1.241 GB | large, very low quality loss - recommended |
Fox-1-1.6B-Q6_K.gguf | Q6_K | 1.373 GB | very large, extremely low quality loss |
Fox-1-1.6B-Q8_0.gguf | Q8_0 | 1.776 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/Fox-1-1.6B-GGUF --include "Fox-1-1.6B-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/Fox-1-1.6B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/Fox-1-1.6B-GGUF
Base model
tensoropera/Fox-1-1.6BEvaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard27.660
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard7.400
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard1.280
- acc_norm on GPQA (0-shot)Open LLM Leaderboard1.790
- acc_norm on MuSR (0-shot)Open LLM Leaderboard3.870
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard4.130