Instructions to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF", filename="Umbra-v2.1-MoE-4x10.7-IQ2_XXS.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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with Ollama:
ollama run hf.co/LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
- Unsloth Studio
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF to start chatting
- Docker Model Runner
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with Docker Model Runner:
docker model run hf.co/LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
- Lemonade
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Umbra-v2.1-MoE-4x10.7-GGUF-Q4_K_M
List all available models
lemonade list
Please release IQ2_XXS variant too
Could you please release IQ2_XXS quant too which is introduced at Jan 2024 and can be run on low end computers too?
Which models would this be appropriate for? 7B? Mixtral 4x7B? 13B? I can add it to the list, but would like it to be useful for folks.
I meant to release IQ2_XXS for this model LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF.
There's more info about IQ2_XXS here: https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/quantize.cpp
It's going to take a change to the quantization pipeline; it's not just a simple change to add a new quant size. I'll add the extra steps to my scripts and generate an IQ2_XXS model as a first test when the changes have been added.
Thank you so much for that. I appreciate it.
IQ2_XXS quant uploading. Now I know why I've never seen that size quant around: it's crazy slow to generate the imatrix and then use it to generate a quantized model. It takes longer to quantize an XXS model than it does to generate all other quants combined (Q3 -> Q8 with 1-3 variants of each.)
Not sure I can guarantee that I'll be generating these quants with every model. Feel free to ping me on specific models, but the resources needed to generate them is a bit excessive (and for models that people may not use.)
Thank you so much. I didn't know that it's super slow to generate that. I'm so grateful to you.