metadata
license: apache-2.0
base_model: Menlo/Jan-nano
pipeline_tag: text-generation
library_name: transformers
tags:
- TensorBlock
- GGUF

Menlo/Jan-nano - GGUF
This repo contains GGUF format model files for Menlo/Jan-nano.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.
Our projects
Forge | |
---|---|
![]() |
|
An OpenAI-compatible multi-provider routing layer. | |
🚀 Try it now! 🚀 | |
Awesome MCP Servers | TensorBlock Studio |
![]() |
![]() |
A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
👀 See what we built 👀 | 👀 See what we built 👀 |
Prompt template
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>
</think>
Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
Jan-nano-Q2_K.gguf | Q2_K | 1.669 GB | smallest, significant quality loss - not recommended for most purposes |
Jan-nano-Q3_K_S.gguf | Q3_K_S | 1.887 GB | very small, high quality loss |
Jan-nano-Q3_K_M.gguf | Q3_K_M | 2.076 GB | very small, high quality loss |
Jan-nano-Q3_K_L.gguf | Q3_K_L | 2.240 GB | small, substantial quality loss |
Jan-nano-Q4_0.gguf | Q4_0 | 2.370 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
Jan-nano-Q4_K_S.gguf | Q4_K_S | 2.383 GB | small, greater quality loss |
Jan-nano-Q4_K_M.gguf | Q4_K_M | 2.497 GB | medium, balanced quality - recommended |
Jan-nano-Q5_0.gguf | Q5_0 | 2.824 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
Jan-nano-Q5_K_S.gguf | Q5_K_S | 2.824 GB | large, low quality loss - recommended |
Jan-nano-Q5_K_M.gguf | Q5_K_M | 2.890 GB | large, very low quality loss - recommended |
Jan-nano-Q6_K.gguf | Q6_K | 3.306 GB | very large, extremely low quality loss |
Jan-nano-Q8_0.gguf | Q8_0 | 4.280 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/Menlo_Jan-nano-GGUF --include "Jan-nano-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-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'