GGUF
English
slm
llama
tiny
tinyllama
TensorBlock
GGUF
Inference Endpoints
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venkycs/Zyte-1B - GGUF

This repo contains GGUF format model files for venkycs/Zyte-1B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Prompt template

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Model file specification

Filename Quant type File Size Description
Zyte-1B-Q2_K.gguf Q2_K 0.432 GB smallest, significant quality loss - not recommended for most purposes
Zyte-1B-Q3_K_S.gguf Q3_K_S 0.499 GB very small, high quality loss
Zyte-1B-Q3_K_M.gguf Q3_K_M 0.548 GB very small, high quality loss
Zyte-1B-Q3_K_L.gguf Q3_K_L 0.592 GB small, substantial quality loss
Zyte-1B-Q4_0.gguf Q4_0 0.637 GB legacy; small, very high quality loss - prefer using Q3_K_M
Zyte-1B-Q4_K_S.gguf Q4_K_S 0.640 GB small, greater quality loss
Zyte-1B-Q4_K_M.gguf Q4_K_M 0.668 GB medium, balanced quality - recommended
Zyte-1B-Q5_0.gguf Q5_0 0.766 GB legacy; medium, balanced quality - prefer using Q4_K_M
Zyte-1B-Q5_K_S.gguf Q5_K_S 0.766 GB large, low quality loss - recommended
Zyte-1B-Q5_K_M.gguf Q5_K_M 0.782 GB large, very low quality loss - recommended
Zyte-1B-Q6_K.gguf Q6_K 0.903 GB very large, extremely low quality loss
Zyte-1B-Q8_0.gguf Q8_0 1.170 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/Zyte-1B-GGUF --include "Zyte-1B-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/Zyte-1B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
287
GGUF
Model size
1.1B params
Architecture
llama

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

Model tree for tensorblock/Zyte-1B-GGUF

Base model

venkycs/Zyte-1B
Quantized
(1)
this model

Datasets used to train tensorblock/Zyte-1B-GGUF