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Med_GPT2-GGUF

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GGUF
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medical
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GGUF
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metadata
license: apache-2.0
datasets:
  - gamino/wiki_medical_terms
language:
  - en
pipeline_tag: text-generation
tags:
  - medical
  - TensorBlock
  - GGUF
base_model: Sharathhebbar24/Med_GPT2
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Sharathhebbar24/Med_GPT2 - GGUF

This repo contains GGUF format model files for Sharathhebbar24/Med_GPT2.

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
Med_GPT2-Q2_K.gguf Q2_K 0.081 GB smallest, significant quality loss - not recommended for most purposes
Med_GPT2-Q3_K_S.gguf Q3_K_S 0.090 GB very small, high quality loss
Med_GPT2-Q3_K_M.gguf Q3_K_M 0.098 GB very small, high quality loss
Med_GPT2-Q3_K_L.gguf Q3_K_L 0.102 GB small, substantial quality loss
Med_GPT2-Q4_0.gguf Q4_0 0.107 GB legacy; small, very high quality loss - prefer using Q3_K_M
Med_GPT2-Q4_K_S.gguf Q4_K_S 0.107 GB small, greater quality loss
Med_GPT2-Q4_K_M.gguf Q4_K_M 0.113 GB medium, balanced quality - recommended
Med_GPT2-Q5_0.gguf Q5_0 0.122 GB legacy; medium, balanced quality - prefer using Q4_K_M
Med_GPT2-Q5_K_S.gguf Q5_K_S 0.122 GB large, low quality loss - recommended
Med_GPT2-Q5_K_M.gguf Q5_K_M 0.127 GB large, very low quality loss - recommended
Med_GPT2-Q6_K.gguf Q6_K 0.138 GB very large, extremely low quality loss
Med_GPT2-Q8_0.gguf Q8_0 0.178 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/Med_GPT2-GGUF --include "Med_GPT2-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/Med_GPT2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'