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metadata
language:
  - ru
tags:
  - TensorBlock
  - GGUF
base_model: AnatoliiPotapov/T-lite-instruct-0.1
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AnatoliiPotapov/T-lite-instruct-0.1 - GGUF

This repo contains GGUF format model files for AnatoliiPotapov/T-lite-instruct-0.1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template 

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename Quant type File Size Description
T-lite-instruct-0.1-Q2_K.gguf Q2_K 2.961 GB smallest, significant quality loss - not recommended for most purposes
T-lite-instruct-0.1-Q3_K_S.gguf Q3_K_S 3.413 GB very small, high quality loss
T-lite-instruct-0.1-Q3_K_M.gguf Q3_K_M 3.743 GB very small, high quality loss
T-lite-instruct-0.1-Q3_K_L.gguf Q3_K_L 4.025 GB small, substantial quality loss
T-lite-instruct-0.1-Q4_0.gguf Q4_0 4.341 GB legacy; small, very high quality loss - prefer using Q3_K_M
T-lite-instruct-0.1-Q4_K_S.gguf Q4_K_S 4.370 GB small, greater quality loss
T-lite-instruct-0.1-Q4_K_M.gguf Q4_K_M 4.583 GB medium, balanced quality - recommended
T-lite-instruct-0.1-Q5_0.gguf Q5_0 5.215 GB legacy; medium, balanced quality - prefer using Q4_K_M
T-lite-instruct-0.1-Q5_K_S.gguf Q5_K_S 5.215 GB large, low quality loss - recommended
T-lite-instruct-0.1-Q5_K_M.gguf Q5_K_M 5.339 GB large, very low quality loss - recommended
T-lite-instruct-0.1-Q6_K.gguf Q6_K 6.143 GB very large, extremely low quality loss
T-lite-instruct-0.1-Q8_0.gguf Q8_0 7.954 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/T-lite-instruct-0.1-GGUF --include "T-lite-instruct-0.1-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/T-lite-instruct-0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'