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jinaai/reader-lm-1.5b - GGUF

This repo contains GGUF format model files for jinaai/reader-lm-1.5b.

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

Prompt template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename Quant type File Size Description
reader-lm-1.5b-Q2_K.gguf Q2_K 0.630 GB smallest, significant quality loss - not recommended for most purposes
reader-lm-1.5b-Q3_K_S.gguf Q3_K_S 0.709 GB very small, high quality loss
reader-lm-1.5b-Q3_K_M.gguf Q3_K_M 0.768 GB very small, high quality loss
reader-lm-1.5b-Q3_K_L.gguf Q3_K_L 0.820 GB small, substantial quality loss
reader-lm-1.5b-Q4_0.gguf Q4_0 0.871 GB legacy; small, very high quality loss - prefer using Q3_K_M
reader-lm-1.5b-Q4_K_S.gguf Q4_K_S 0.876 GB small, greater quality loss
reader-lm-1.5b-Q4_K_M.gguf Q4_K_M 0.918 GB medium, balanced quality - recommended
reader-lm-1.5b-Q5_0.gguf Q5_0 1.023 GB legacy; medium, balanced quality - prefer using Q4_K_M
reader-lm-1.5b-Q5_K_S.gguf Q5_K_S 1.023 GB large, low quality loss - recommended
reader-lm-1.5b-Q5_K_M.gguf Q5_K_M 1.048 GB large, very low quality loss - recommended
reader-lm-1.5b-Q6_K.gguf Q6_K 1.185 GB very large, extremely low quality loss
reader-lm-1.5b-Q8_0.gguf Q8_0 1.533 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/reader-lm-1.5b-GGUF --include "reader-lm-1.5b-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/reader-lm-1.5b-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
1.54B params
Architecture
qwen2

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Inference Examples
Inference API (serverless) has been turned off for this model.

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