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sethuiyer/Dr_Samantha-7b - GGUF
This repo contains GGUF format model files for sethuiyer/Dr_Samantha-7b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Prompt template
Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
Dr_Samantha-7b-Q2_K.gguf | Q2_K | 2.533 GB | smallest, significant quality loss - not recommended for most purposes |
Dr_Samantha-7b-Q3_K_S.gguf | Q3_K_S | 2.948 GB | very small, high quality loss |
Dr_Samantha-7b-Q3_K_M.gguf | Q3_K_M | 3.298 GB | very small, high quality loss |
Dr_Samantha-7b-Q3_K_L.gguf | Q3_K_L | 3.597 GB | small, substantial quality loss |
Dr_Samantha-7b-Q4_0.gguf | Q4_0 | 3.826 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
Dr_Samantha-7b-Q4_K_S.gguf | Q4_K_S | 3.857 GB | small, greater quality loss |
Dr_Samantha-7b-Q4_K_M.gguf | Q4_K_M | 4.081 GB | medium, balanced quality - recommended |
Dr_Samantha-7b-Q5_0.gguf | Q5_0 | 4.652 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
Dr_Samantha-7b-Q5_K_S.gguf | Q5_K_S | 4.652 GB | large, low quality loss - recommended |
Dr_Samantha-7b-Q5_K_M.gguf | Q5_K_M | 4.783 GB | large, very low quality loss - recommended |
Dr_Samantha-7b-Q6_K.gguf | Q6_K | 5.529 GB | very large, extremely low quality loss |
Dr_Samantha-7b-Q8_0.gguf | Q8_0 | 7.161 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/Dr_Samantha-7b-GGUF --include "Dr_Samantha-7b-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/Dr_Samantha-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/Dr_Samantha-7b-GGUF
Base model
sethuiyer/Dr_Samantha-7bDatasets used to train tensorblock/Dr_Samantha-7b-GGUF
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard53.840
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard77.950
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard47.940
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard45.580
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard73.560
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard18.800