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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	---
	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](https://huggingface.co/telepix/PIXIE-Rune-v1.0) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) and [Ollama](https://ollama.com).

	## Files

	\| File \| Quantization \| Size \|
	\|------\|-------------\|------\|
	\| [pixie-rune-v1-q4_k.gguf](https://huggingface.co/cstr/pixie-rune-v1-GGUF/resolve/main/pixie-rune-v1-q4_k.gguf) \| Q4_K \| 0 MB \|
	\| [pixie-rune-v1-q8_0.gguf](https://huggingface.co/cstr/pixie-rune-v1-GGUF/resolve/main/pixie-rune-v1-q8_0.gguf) \| Q8_0 \| 0 MB \|
	\| [pixie-rune-v1.gguf](https://huggingface.co/cstr/pixie-rune-v1-GGUF/resolve/main/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
	```bash
	./crispembed -m pixie-rune-v1 "Hello world"
	./crispembed-server -m pixie-rune-v1 --port 8080
	```

	### Ollama (with [CrispStrobe fork](https://github.com/CrispStrobe/ollama/tree/feat/xlmr-embedding))
	```bash
	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)
	```python
	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](https://huggingface.co/telepix/PIXIE-Rune-v1.0)
	- Inference: [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) (MIT, ggml-based)