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unsloth/DeepScaleR-1.5B-Preview - GGUF

This repo contains GGUF format model files for unsloth/DeepScaleR-1.5B-Preview.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4823.

Prompt template


Model file specification

Filename Quant type File Size Description
DeepScaleR-1.5B-Preview-Q2_K.gguf Q2_K 0.753 GB smallest, significant quality loss - not recommended for most purposes
DeepScaleR-1.5B-Preview-Q3_K_S.gguf Q3_K_S 0.861 GB very small, high quality loss
DeepScaleR-1.5B-Preview-Q3_K_M.gguf Q3_K_M 0.924 GB very small, high quality loss
DeepScaleR-1.5B-Preview-Q3_K_L.gguf Q3_K_L 0.980 GB small, substantial quality loss
DeepScaleR-1.5B-Preview-Q4_0.gguf Q4_0 1.066 GB legacy; small, very high quality loss - prefer using Q3_K_M
DeepScaleR-1.5B-Preview-Q4_K_S.gguf Q4_K_S 1.072 GB small, greater quality loss
DeepScaleR-1.5B-Preview-Q4_K_M.gguf Q4_K_M 1.117 GB medium, balanced quality - recommended
DeepScaleR-1.5B-Preview-Q5_0.gguf Q5_0 1.259 GB legacy; medium, balanced quality - prefer using Q4_K_M
DeepScaleR-1.5B-Preview-Q5_K_S.gguf Q5_K_S 1.259 GB large, low quality loss - recommended
DeepScaleR-1.5B-Preview-Q5_K_M.gguf Q5_K_M 1.285 GB large, very low quality loss - recommended
DeepScaleR-1.5B-Preview-Q6_K.gguf Q6_K 1.464 GB very large, extremely low quality loss
DeepScaleR-1.5B-Preview-Q8_0.gguf Q8_0 1.895 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/DeepScaleR-1.5B-Preview-GGUF --include "DeepScaleR-1.5B-Preview-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/DeepScaleR-1.5B-Preview-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
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GGUF
Model size
1.78B params
Architecture
qwen2

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