Instructions to use BAEM1N/PIXIE-Rune-v1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BAEM1N/PIXIE-Rune-v1.0-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("BAEM1N/PIXIE-Rune-v1.0-GGUF")

sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

llama-cpp-python

How to use BAEM1N/PIXIE-Rune-v1.0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BAEM1N/PIXIE-Rune-v1.0-GGUF",
	filename="PIXIE-Rune-v1.0-F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use BAEM1N/PIXIE-Rune-v1.0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BAEM1N/PIXIE-Rune-v1.0-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf BAEM1N/PIXIE-Rune-v1.0-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BAEM1N/PIXIE-Rune-v1.0-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf BAEM1N/PIXIE-Rune-v1.0-GGUF:F16

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 BAEM1N/PIXIE-Rune-v1.0-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf BAEM1N/PIXIE-Rune-v1.0-GGUF:F16

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 BAEM1N/PIXIE-Rune-v1.0-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BAEM1N/PIXIE-Rune-v1.0-GGUF:F16

Use Docker

docker model run hf.co/BAEM1N/PIXIE-Rune-v1.0-GGUF:F16

LM Studio
Jan
Ollama
How to use BAEM1N/PIXIE-Rune-v1.0-GGUF with Ollama:
```
ollama run hf.co/BAEM1N/PIXIE-Rune-v1.0-GGUF:F16
```

Unsloth Studio new

How to use BAEM1N/PIXIE-Rune-v1.0-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 BAEM1N/PIXIE-Rune-v1.0-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 BAEM1N/PIXIE-Rune-v1.0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BAEM1N/PIXIE-Rune-v1.0-GGUF to start chatting

Docker Model Runner
How to use BAEM1N/PIXIE-Rune-v1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/BAEM1N/PIXIE-Rune-v1.0-GGUF:F16
```

Lemonade

How to use BAEM1N/PIXIE-Rune-v1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BAEM1N/PIXIE-Rune-v1.0-GGUF:F16

Run and chat with the model

lemonade run user.PIXIE-Rune-v1.0-GGUF-F16

List all available models

lemonade list

PIXIE-Rune-v1.0 GGUF

GGUF conversion of telepix/PIXIE-Rune-v1.0.

#2 on Korean Embedding Leaderboard (NDCG@5,10 avg: 84.68)

Files

File	Quant	Size
PIXIE-Rune-v1.0-Q8_0.gguf	Q8_0	599 MB
PIXIE-Rune-v1.0-F16.gguf	F16	1.15 GB

Usage with llama.cpp

llama-server -m PIXIE-Rune-v1.0-Q8_0.gguf --port 9020 --embedding --pooling mean

Model Info

Architecture: XLM-RoBERTa (24 layers, hidden=1024)
Parameters: ~335M
Embedding dimension: 1024
Max sequence length: 8192

Downloads last month: 20

GGUF

Model size

0.6B params

Architecture

bert

Hardware compatibility

8-bit

16-bit

Inference Providers NEW

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Model tree for BAEM1N/PIXIE-Rune-v1.0-GGUF

Base model

telepix/PIXIE-Rune-v1.0

Quantized

(3)

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