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tensorblock/
Menlo_Jan-nano-128k-GGUF

Name: Menlo_Jan-nano-128k-GGUF
Availability: InStock

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Menlo/Jan-nano-128k - GGUF

This repo contains GGUF format model files for Menlo/Jan-nano-128k.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

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

</think>

Filename	Quant type	File Size	Description
Jan-nano-128k-Q2_K.gguf	Q2_K	1.669 GB	smallest, significant quality loss - not recommended for most purposes
Jan-nano-128k-Q3_K_S.gguf	Q3_K_S	1.887 GB	very small, high quality loss
Jan-nano-128k-Q3_K_M.gguf	Q3_K_M	2.076 GB	very small, high quality loss
Jan-nano-128k-Q3_K_L.gguf	Q3_K_L	2.240 GB	small, substantial quality loss
Jan-nano-128k-Q4_0.gguf	Q4_0	2.370 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Jan-nano-128k-Q4_K_S.gguf	Q4_K_S	2.383 GB	small, greater quality loss
Jan-nano-128k-Q4_K_M.gguf	Q4_K_M	2.497 GB	medium, balanced quality - recommended
Jan-nano-128k-Q5_0.gguf	Q5_0	2.824 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Jan-nano-128k-Q5_K_S.gguf	Q5_K_S	2.824 GB	large, low quality loss - recommended
Jan-nano-128k-Q5_K_M.gguf	Q5_K_M	2.890 GB	large, very low quality loss - recommended
Jan-nano-128k-Q6_K.gguf	Q6_K	3.306 GB	very large, extremely low quality loss
Jan-nano-128k-Q8_0.gguf	Q8_0	4.280 GB	very large, extremely low quality loss - not recommended

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Menlo_Jan-nano-128k-GGUF --include "Jan-nano-128k-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/Menlo_Jan-nano-128k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'