GGUF
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GGUF
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vikash06/doctorLLM - GGUF

This repo contains GGUF format model files for vikash06/doctorLLM.

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
doctorLLM-Q2_K.gguf Q2_K 2.533 GB smallest, significant quality loss - not recommended for most purposes
doctorLLM-Q3_K_S.gguf Q3_K_S 2.948 GB very small, high quality loss
doctorLLM-Q3_K_M.gguf Q3_K_M 3.298 GB very small, high quality loss
doctorLLM-Q3_K_L.gguf Q3_K_L 3.597 GB small, substantial quality loss
doctorLLM-Q4_0.gguf Q4_0 3.826 GB legacy; small, very high quality loss - prefer using Q3_K_M
doctorLLM-Q4_K_S.gguf Q4_K_S 3.857 GB small, greater quality loss
doctorLLM-Q4_K_M.gguf Q4_K_M 4.081 GB medium, balanced quality - recommended
doctorLLM-Q5_0.gguf Q5_0 4.652 GB legacy; medium, balanced quality - prefer using Q4_K_M
doctorLLM-Q5_K_S.gguf Q5_K_S 4.652 GB large, low quality loss - recommended
doctorLLM-Q5_K_M.gguf Q5_K_M 4.783 GB large, very low quality loss - recommended
doctorLLM-Q6_K.gguf Q6_K 5.529 GB very large, extremely low quality loss
doctorLLM-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/doctorLLM-GGUF --include "doctorLLM-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/doctorLLM-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
21
GGUF
Model size
6.74B params
Architecture
llama

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Inference API
Unable to determine this model's library. Check the docs .

Model tree for tensorblock/doctorLLM-GGUF

Base model

vikash06/doctorLLM
Quantized
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this model

Dataset used to train tensorblock/doctorLLM-GGUF