Instructions to use cstr/pixie-rune-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/pixie-rune-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/pixie-rune-v1-GGUF",
	filename="pixie-rune-v1-q4_k.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 cstr/pixie-rune-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/pixie-rune-v1-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/pixie-rune-v1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/pixie-rune-v1-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/pixie-rune-v1-GGUF:Q8_0

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 cstr/pixie-rune-v1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf cstr/pixie-rune-v1-GGUF:Q8_0

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 cstr/pixie-rune-v1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/pixie-rune-v1-GGUF:Q8_0

Use Docker

docker model run hf.co/cstr/pixie-rune-v1-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use cstr/pixie-rune-v1-GGUF with Ollama:
```
ollama run hf.co/cstr/pixie-rune-v1-GGUF:Q8_0
```

Unsloth Studio new

How to use cstr/pixie-rune-v1-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 cstr/pixie-rune-v1-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 cstr/pixie-rune-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cstr/pixie-rune-v1-GGUF to start chatting

Docker Model Runner
How to use cstr/pixie-rune-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/pixie-rune-v1-GGUF:Q8_0
```

Lemonade

How to use cstr/pixie-rune-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/pixie-rune-v1-GGUF:Q8_0

Run and chat with the model

lemonade run user.pixie-rune-v1-GGUF-Q8_0

List all available models

lemonade list

pixie-rune-v1-GGUF / README.md

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metadata

license: apache-2.0
language:
  - multilingual
tags:
  - embeddings
  - gguf
  - ggml
  - text-embeddings
  - xlm-r
  - crispembed
  - ollama
pipeline_tag: feature-extraction
base_model: telepix/PIXIE-Rune-v1.0

pixie-rune-v1 GGUF

GGUF format of telepix/PIXIE-Rune-v1.0 for use with CrispEmbed and Ollama.

Files

File	Quantization	Size
pixie-rune-v1-q4_k.gguf	Q4_K	0 MB
pixie-rune-v1-q8_0.gguf	Q8_0	0 MB
pixie-rune-v1.gguf	F32	0 MB

Recommended: Q8_0 for quality (cos vs HF: cross-lingual OK), Q4_K for size (cross-lingual OK).

Quick Start

CrispEmbed

./crispembed -m pixie-rune-v1 "Hello world"
./crispembed-server -m pixie-rune-v1 --port 8080

Ollama (with CrispStrobe fork)

echo "FROM pixie-rune-v1-q8_0.gguf" > Modelfile
ollama create pixie-rune-v1 -f Modelfile
curl http://localhost:11434/api/embed -d '{"model":"pixie-rune-v1","input":["Hello world"]}'

Python (CrispEmbed)

from crispembed import CrispEmbed
model = CrispEmbed("pixie-rune-v1-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])

Model Details

Property	Value
Architecture	XLM-R
Parameters	560M
Embedding Dimension	1024
Layers	24
Pooling	CLS
Tokenizer	SentencePiece
Language	multilingual
Q8_0 vs HuggingFace	cross-lingual OK
Q4_K vs HuggingFace	cross-lingual OK

Server API

CrispEmbed server supports four API dialects:

POST /embed -- native
POST /v1/embeddings -- OpenAI-compatible
POST /api/embed -- Ollama-compatible
POST /api/embeddings -- Ollama legacy

Credits

Original model: telepix/PIXIE-Rune-v1.0
Inference: CrispEmbed (MIT, ggml-based)