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keeeeenw/MicroLlama - GGUF
This repo contains GGUF format model files for keeeeenw/MicroLlama.
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 |
---|---|---|---|
MicroLlama-Q2_K.gguf | Q2_K | 0.117 GB | smallest, significant quality loss - not recommended for most purposes |
MicroLlama-Q3_K_S.gguf | Q3_K_S | 0.135 GB | very small, high quality loss |
MicroLlama-Q3_K_M.gguf | Q3_K_M | 0.145 GB | very small, high quality loss |
MicroLlama-Q3_K_L.gguf | Q3_K_L | 0.155 GB | small, substantial quality loss |
MicroLlama-Q4_0.gguf | Q4_0 | 0.168 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
MicroLlama-Q4_K_S.gguf | Q4_K_S | 0.169 GB | small, greater quality loss |
MicroLlama-Q4_K_M.gguf | Q4_K_M | 0.177 GB | medium, balanced quality - recommended |
MicroLlama-Q5_0.gguf | Q5_0 | 0.200 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
MicroLlama-Q5_K_S.gguf | Q5_K_S | 0.200 GB | large, low quality loss - recommended |
MicroLlama-Q5_K_M.gguf | Q5_K_M | 0.204 GB | large, very low quality loss - recommended |
MicroLlama-Q6_K.gguf | Q6_K | 0.233 GB | very large, extremely low quality loss |
MicroLlama-Q8_0.gguf | Q8_0 | 0.302 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/MicroLlama-GGUF --include "MicroLlama-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/MicroLlama-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/MicroLlama-GGUF
Base model
keeeeenw/MicroLlamaDataset used to train tensorblock/MicroLlama-GGUF
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard19.850
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard2.830
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard0.000
- acc_norm on GPQA (0-shot)Open LLM Leaderboard1.450
- acc_norm on MuSR (0-shot)Open LLM Leaderboard4.790
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard1.530