tensorblock/Samantha2.0-Phi4-ita-16bit-GGUF

WasamiKirua/Samantha2.0-Phi4-ita-16bit - GGUF

This repo contains GGUF format model files for WasamiKirua/Samantha2.0-Phi4-ita-16bit.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4882.

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

Model file specification

Filename	Quant type	File Size	Description
Samantha2.0-Phi4-ita-16bit-Q2_K.gguf	Q2_K	5.609 GB	smallest, significant quality loss - not recommended for most purposes
Samantha2.0-Phi4-ita-16bit-Q3_K_S.gguf	Q3_K_S	6.505 GB	very small, high quality loss
Samantha2.0-Phi4-ita-16bit-Q3_K_M.gguf	Q3_K_M	7.191 GB	very small, high quality loss
Samantha2.0-Phi4-ita-16bit-Q3_K_L.gguf	Q3_K_L	7.789 GB	small, substantial quality loss
Samantha2.0-Phi4-ita-16bit-Q4_0.gguf	Q4_0	8.383 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Samantha2.0-Phi4-ita-16bit-Q4_K_S.gguf	Q4_K_S	8.444 GB	small, greater quality loss
Samantha2.0-Phi4-ita-16bit-Q4_K_M.gguf	Q4_K_M	8.890 GB	medium, balanced quality - recommended
Samantha2.0-Phi4-ita-16bit-Q5_0.gguf	Q5_0	10.152 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Samantha2.0-Phi4-ita-16bit-Q5_K_S.gguf	Q5_K_S	10.152 GB	large, low quality loss - recommended
Samantha2.0-Phi4-ita-16bit-Q5_K_M.gguf	Q5_K_M	10.413 GB	large, very low quality loss - recommended
Samantha2.0-Phi4-ita-16bit-Q6_K.gguf	Q6_K	12.030 GB	very large, extremely low quality loss
Samantha2.0-Phi4-ita-16bit-Q8_0.gguf	Q8_0	15.581 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/Samantha2.0-Phi4-ita-16bit-GGUF --include "Samantha2.0-Phi4-ita-16bit-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/Samantha2.0-Phi4-ita-16bit-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'