Instructions to use BasedAGI/Skyfall-36B-V2-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use BasedAGI/Skyfall-36B-V2-i1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BasedAGI/Skyfall-36B-V2-i1-GGUF", filename="skyfall-36b-v2-i1-IQ1_M.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 BasedAGI/Skyfall-36B-V2-i1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf BasedAGI/Skyfall-36B-V2-i1-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 BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BasedAGI/Skyfall-36B-V2-i1-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 BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use BasedAGI/Skyfall-36B-V2-i1-GGUF with Ollama:
ollama run hf.co/BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M
- Unsloth Studio new
How to use BasedAGI/Skyfall-36B-V2-i1-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 BasedAGI/Skyfall-36B-V2-i1-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 BasedAGI/Skyfall-36B-V2-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BasedAGI/Skyfall-36B-V2-i1-GGUF to start chatting
- Docker Model Runner
How to use BasedAGI/Skyfall-36B-V2-i1-GGUF with Docker Model Runner:
docker model run hf.co/BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M
- Lemonade
How to use BasedAGI/Skyfall-36B-V2-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BasedAGI/Skyfall-36B-V2-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Skyfall-36B-V2-i1-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Quantized to i1-GGUF using SpongeQuant, the Oobabooga of LLM quantization.
What is a GGUF?
GGUF is a file format used for running large language models (LLMs) on different types of computers. It supports both regular processors (CPUs) and graphics cards (GPUs), making it easier to run models across a wide range of hardware. Many LLMs require powerful and expensive GPUs, but GGUF improves compatibility and efficiency by optimizing how models are loaded and executed. If a GPU doesn't have enough memory, GGUF can offload parts of the model to the CPU, allowing it to run even when GPU resources are limited. GGUF is designed to work well with quantized models, which use less memory and run faster, making them ideal for lower-end hardware. However, it can also store full-precision models when needed. Thanks to these optimizations, GGUF allows LLMs to run efficiently on everything from high-end GPUs to laptops and even CPU-only systems.
What is an i1-GGUF?
i1-GGUF is an enhanced type of GGUF model that uses imatrix quantizationโa smarter way of reducing model size while preserving key details. Instead of shrinking everything equally, it analyzes the importance of different model components and keeps the most crucial parts more accurate. Like standard GGUF, i1-GGUF allows LLMs to run on various hardware, including CPUs and lower-end GPUs. However, because it prioritizes important weights, i1-GGUF models deliver better responses than traditional GGUF models while maintaining efficiency.
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Model tree for BasedAGI/Skyfall-36B-V2-i1-GGUF
Base model
TheDrummer/Skyfall-36B-v2
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BasedAGI/Skyfall-36B-V2-i1-GGUF", filename="", )