Instructions to use lightblue/Karasu-Mixtral-8x22B-v0.1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lightblue/Karasu-Mixtral-8x22B-v0.1-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lightblue/Karasu-Mixtral-8x22B-v0.1-gguf", filename="Karasu-Mixtral-8x22B-v0.1-Q3_K_M-00001-of-00005.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lightblue/Karasu-Mixtral-8x22B-v0.1-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M # Run inference directly in the terminal: llama cli -hf lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M # Run inference directly in the terminal: llama cli -hf lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_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 lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_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 lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M
Use Docker
docker model run hf.co/lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M
- LM Studio
- Jan
- Ollama
How to use lightblue/Karasu-Mixtral-8x22B-v0.1-gguf with Ollama:
ollama run hf.co/lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M
- Unsloth Studio
How to use lightblue/Karasu-Mixtral-8x22B-v0.1-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 lightblue/Karasu-Mixtral-8x22B-v0.1-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 lightblue/Karasu-Mixtral-8x22B-v0.1-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lightblue/Karasu-Mixtral-8x22B-v0.1-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use lightblue/Karasu-Mixtral-8x22B-v0.1-gguf with Docker Model Runner:
docker model run hf.co/lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M
- Lemonade
How to use lightblue/Karasu-Mixtral-8x22B-v0.1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lightblue/Karasu-Mixtral-8x22B-v0.1-gguf:Q3_K_M
Run and chat with the model
lemonade run user.Karasu-Mixtral-8x22B-v0.1-gguf-Q3_K_M
List all available models
lemonade list
Fails to load
Hey! Thanks for the rapid Instruct finetune.
This gguf, sadly, doesn't load for me. Both the latest koboldcpp and a llama.cpp build from yesterday silently crash on load.
Didnt try this one, but I was yesterday sucesfull in loading Mixttral the non-instruct version, using gguf and kobolcpp without issues.
Yeah, the base model loads fine.
Yup also tried it also. It crashes instantly when loaded.
Do we need to re-combine these somehow?
Yeah, not sure why split it up into 6 quintilian pieces I think that might be the issue. But pretty sure recent versions of llama cpp can handle this as long as you load the first file, but I've never seen it split this much so maybe that's a issue. There is instructions on how to recombine them though, I haven't tried for myself though
You can combine them like this, but I havent tried, because there is too many pieces and I am lazy :) On Windows: "COPY /b Mixtral-8x22B-v0.1.Q2_K-00001-of-00005.gguf + Mixtral-8x22B-v0.1.Q2_K-00002-of-00005.gguf + Mixtral-8x22B-v0.1.Q2_K-00003-of-00005.gguf + Mixtral-8x22B-v0.1.Q2_K-00004-of-00005.gguf + Mixtral-8x22B-v0.1.Q2_K-00005-of-00005.gguf"
Hey, sorry, it was like 1am when I did the conversion > quantization > splitting. Will look at this again today!
No problem :3
Can you be my free QA tester here? It might be fixed now.
Previously I ran
./convert.py --outfile Karasu-Mixtral-8x22B-v0.1-q3_k_m --outtype f16 /workspace/llm_training/axolotl/mixtral_8x22B_training/merged_model_multiling
./quantize /workspace/Karasu-Mixtral-8x22B-v0.1.gguf /workspace/Karasu-Mixtral-8x22B-v0.1_q3_k_m.gguf Q3_K_M
./gguf-split --split --split-max-size 5G /workspace/Karasu-Mixtral-8x22B-v0.1_q3_k_m.gguf /workspace/somewhere-sensible
This time, I ran:
./convert-hf-to-gguf.py --outfile /workspace/Karasu-Mixtral-8x22B-v0.1.gguf --outtype f16 /workspace/llm_training/axolotl/mixtral_8x22B_training/merged_model_multiling
./quantize /workspace/Karasu-Mixtral-8x22B-v0.1.gguf /workspace/Karasu-Mixtral-8x22B-v0.1-Q3_K_M.gguf Q3_K_M
./gguf-split --split --split-max-tensors 128 /workspace/Karasu-Mixtral-8x22B-v0.1-Q3_K_M.gguf /workspace/split_gguf_q3km/Karasu-Mixtral-8x22B-v0.1-Q3_K_M
I think the crucial difference was using convert-hf-to-gguf.py rather than convert.py. convert-hf-to-gguf.py took a lot longer to load and in my monkey brain, longer loading means it's doing something more meaningful. Everything else is pretty much the same as before, but the splitting turned out nicely this time (5GB per file). Will maybe investigate the difference between convert.py and convert-hf-to-gguf.py as I presumed that convert.py would basically be a catch-all for all different types of files.
Seems to work with koboldcpp but I don't know for sure if its loading all 5 parts.
You might also want to take a look at an experimental repo I made that splits into smaller pieces:
https://huggingface.co/lightblue/Karasu-Mixtral-8x22B-v0.1-gguf-test
I tested in Kobold CPP, the 3KM 5 split. Works without any issues, loads everything :)
Yep, five split is working for me. Gonna close this up. Thanks again!
Woohoo! Enjoy!
Works for me too