Instructions to use mishig/xet-gguf-edit-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use mishig/xet-gguf-edit-test with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mishig/xet-gguf-edit-test", filename="10000mb.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use mishig/xet-gguf-edit-test with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mishig/xet-gguf-edit-test # Run inference directly in the terminal: llama-cli -hf mishig/xet-gguf-edit-test
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mishig/xet-gguf-edit-test # Run inference directly in the terminal: llama-cli -hf mishig/xet-gguf-edit-test
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 mishig/xet-gguf-edit-test # Run inference directly in the terminal: ./llama-cli -hf mishig/xet-gguf-edit-test
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 mishig/xet-gguf-edit-test # Run inference directly in the terminal: ./build/bin/llama-cli -hf mishig/xet-gguf-edit-test
Use Docker
docker model run hf.co/mishig/xet-gguf-edit-test
- LM Studio
- Jan
- Ollama
How to use mishig/xet-gguf-edit-test with Ollama:
ollama run hf.co/mishig/xet-gguf-edit-test
- Unsloth Studio new
How to use mishig/xet-gguf-edit-test 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 mishig/xet-gguf-edit-test 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 mishig/xet-gguf-edit-test to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mishig/xet-gguf-edit-test to start chatting
- Pi new
How to use mishig/xet-gguf-edit-test with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mishig/xet-gguf-edit-test
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mishig/xet-gguf-edit-test" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mishig/xet-gguf-edit-test with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mishig/xet-gguf-edit-test
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mishig/xet-gguf-edit-test
Run Hermes
hermes
- Docker Model Runner
How to use mishig/xet-gguf-edit-test with Docker Model Runner:
docker model run hf.co/mishig/xet-gguf-edit-test
- Lemonade
How to use mishig/xet-gguf-edit-test with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mishig/xet-gguf-edit-test
Run and chat with the model
lemonade run user.xet-gguf-edit-test-{{QUANT_TAG}}List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
GGUF Header Edit Benchmark
Benchmark script for measuring how long it takes to edit GGUF headers in-place on Hugging Face with streaming blobs (xet) and create a pull request per file.
It fetches metadata, rebuilds the header with a small change, commits an edit (header slice only), and records timings to a CSV.
Result from benchmark.ts
Rule of thumb (linear fit):
time_minutes β 0.36 Γ size_GB + 0.25
β¨ What this does
For each *.gguf file in a model repo:
- Discover files via the Hugging Face model tree API.
- Fetch GGUF + typed metadata with
@huggingface/gguf. - Rebuild the header using
buildGgufHeader(preserving endianness, alignment, and tensor info range). - Commit a slice edit (header bytes only) using
commitIterwithuseXet: trueto avoid full re-uploads. - Create a PR titled
benchmark. - Record timing (wall-clock) to
benchmark-results.csv.
π§± Requirements
- Node 18+
- A Hugging Face token with read + write on the target repo:
HF_TOKEN - NPM packages:
@huggingface/gguf@huggingface/hub
- Network access to
huggingface.co
π§ Setup
npm i
npm run benchmark
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