Instructions to use Vaultek/Quartz-R1-8B-Genesis-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vaultek/Quartz-R1-8B-Genesis-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Vaultek/Quartz-R1-8B-Genesis-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Vaultek/Quartz-R1-8B-Genesis-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Vaultek/Quartz-R1-8B-Genesis-GGUF with Ollama:
ollama run hf.co/Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
- Unsloth Studio
How to use Vaultek/Quartz-R1-8B-Genesis-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Vaultek/Quartz-R1-8B-Genesis-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Vaultek/Quartz-R1-8B-Genesis-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Vaultek/Quartz-R1-8B-Genesis-GGUF to start chatting
- Docker Model Runner
How to use Vaultek/Quartz-R1-8B-Genesis-GGUF with Docker Model Runner:
docker model run hf.co/Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
- Lemonade
How to use Vaultek/Quartz-R1-8B-Genesis-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Vaultek/Quartz-R1-8B-Genesis-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Quartz-R1-8B-Genesis-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 2,434 Bytes
961d240 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ---
base_model:
- Vaultek/Quartz-R1-8B-Genesis
tags:
- GGUF
language:
- ru
- en
library_name: transformers
---
Квантизованная версия Quartz-R1-8B-Genesis. Информация о модели доступна в основном репозитории: [`Quartz-R1-8B-Genesis`](https://huggingface.co/Vaultek/Quartz-R1-8B-Genesis/).
# Использование с помощью llama.cpp
Для начала нужно собрать [llama.cpp](https://github.com/ggml-org/llama.cpp) (или обновить, если уже есть):
```bash
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build --config Release -j$(nproc)
cd ..
```
Запуск модели в интерактивном режиме:
```bash
llama.cpp/build/bin/llama-cli -m quartz_r1_genesis_clean.Q4_K_M.gguf -c 32768 -np 4
```
Запуск сервера:
```bash
llama.cpp/build/bin/llama-server -m quartz_r1_genesis_clean.Q4_K_M.gguf -c 32768 -np 4
```
Если позволяют ресурсы, можно ускорить инференс, добавив `-t 10`.
# Квантизации и их качество
Вот созданный ikawrakow график, сравнивающий квантизации (на его странице, но данные совпадают)
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
| Link | Type | Size/GB |
|:-----|:-----|--------:|
| [GGUF_Q2_K](https://huggingface.co/Vaultek/Quartz-R1-8B-Genesis-GGUF/resolve/main/quartz_r1_genesis_clean.Q2_K.gguf) | Q2_K | 3.18 |
| [GGUF_Q3_K_M](https://huggingface.co/Vaultek/Quartz-R1-8B-Genesis-GGUF/resolve/main/quartz_r1_genesis_clean.Q3_K_M.gguf) | Q3_K_M | 4.02 |
| [GGUF_Q4_K_M](https://huggingface.co/Vaultek/Quartz-R1-8B-Genesis-GGUF/resolve/main/quartz_r1_genesis_clean.Q4_K_M.gguf) | Q4_K_M | 4.92 |
| [GGUF_Q5_K_M](https://huggingface.co/Vaultek/Quartz-R1-8B-Genesis-GGUF/resolve/main/quartz_r1_genesis_clean.Q5_K_M.gguf) | Q5_K_M | 5.73 |
| [GGUF_Q8_0](https://huggingface.co/Vaultek/Quartz-R1-8B-Genesis-GGUF/resolve/main/quartz_r1_genesis_clean.Q8_0.gguf) | Q8_0 | 8.54 |
| [GGUF_F16](https://huggingface.co/mradermacher/Quartz-R1-8B-Genesis-GGUF/resolve/main/Quartz-R1-8B-Genesis.f16.gguf) | f16 | 16.2 |
|