You can deploy this model and start earning money today!

tensorblock/
QVikhr-2.5-1.5B-Instruct-SMPO-GGUF

Name: QVikhr-2.5-1.5B-Instruct-SMPO-GGUF
Availability: InStock

quantized

Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO

Views

No views yet

Website

Discord

GitHub

Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO - GGUF

This repo contains GGUF format model files for Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO.

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

Our projects

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio
MCP Servers	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

Model file specification

Filename	Quant type	File Size	Description
QVikhr-2.5-1.5B-Instruct-SMPO-Q2_K.gguf	Q2_K	0.676 GB	smallest, significant quality loss - not recommended for most purposes
QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_S.gguf	Q3_K_S	0.761 GB	very small, high quality loss
QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_M.gguf	Q3_K_M	0.824 GB	very small, high quality loss
QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_L.gguf	Q3_K_L	0.880 GB	small, substantial quality loss
QVikhr-2.5-1.5B-Instruct-SMPO-Q4_0.gguf	Q4_0	0.935 GB	legacy; small, very high quality loss - prefer using Q3_K_M
QVikhr-2.5-1.5B-Instruct-SMPO-Q4_K_S.gguf	Q4_K_S	0.940 GB	small, greater quality loss
QVikhr-2.5-1.5B-Instruct-SMPO-Q4_K_M.gguf	Q4_K_M	0.986 GB	medium, balanced quality - recommended
QVikhr-2.5-1.5B-Instruct-SMPO-Q5_0.gguf	Q5_0	1.098 GB	legacy; medium, balanced quality - prefer using Q4_K_M
QVikhr-2.5-1.5B-Instruct-SMPO-Q5_K_S.gguf	Q5_K_S	1.098 GB	large, low quality loss - recommended
QVikhr-2.5-1.5B-Instruct-SMPO-Q5_K_M.gguf	Q5_K_M	1.125 GB	large, very low quality loss - recommended
QVikhr-2.5-1.5B-Instruct-SMPO-Q6_K.gguf	Q6_K	1.272 GB	very large, extremely low quality loss
QVikhr-2.5-1.5B-Instruct-SMPO-Q8_0.gguf	Q8_0	1.646 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/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF --include "QVikhr-2.5-1.5B-Instruct-SMPO-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/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'