Instructions to use Kquant03/MistralTrix-4x9B-ERP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Kquant03/MistralTrix-4x9B-ERP-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Kquant03/MistralTrix-4x9B-ERP-GGUF", filename="ggml-model-q2_k.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Kquant03/MistralTrix-4x9B-ERP-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 Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Kquant03/MistralTrix-4x9B-ERP-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 Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Kquant03/MistralTrix-4x9B-ERP-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 Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Kquant03/MistralTrix-4x9B-ERP-GGUF with Ollama:
ollama run hf.co/Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M
- Unsloth Studio
How to use Kquant03/MistralTrix-4x9B-ERP-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 Kquant03/MistralTrix-4x9B-ERP-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 Kquant03/MistralTrix-4x9B-ERP-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Kquant03/MistralTrix-4x9B-ERP-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Kquant03/MistralTrix-4x9B-ERP-GGUF with Docker Model Runner:
docker model run hf.co/Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M
- Lemonade
How to use Kquant03/MistralTrix-4x9B-ERP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Kquant03/MistralTrix-4x9B-ERP-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MistralTrix-4x9B-ERP-GGUF-Q4_K_M
List all available models
lemonade list
Would you consider EXL2?
I'd love to give this a shot but I've been severely TabbyAPI-pilled recently and honestly I can't go back to using ooba for my text completion API. I might consider Kobold but going back and forth between them sounds like a drag.
With that said, if you have the spare time/resources, I'd really appreciate an EXL2 version.
On my RTX 4090 I can do 8x7Bs at 3.5bpw with full context, provided I enable 8-bit cache, if that helps at all.
No worries if you can't swing it, of course. Just figured I'd put it out there.
I'd love to give this a shot but I've been severely TabbyAPI-pilled recently and honestly I can't go back to using ooba for my text completion API. I might consider Kobold but going back and forth between them sounds like a drag.
With that said, if you have the spare time/resources, I'd really appreciate an EXL2 version.
On my RTX 4090 I can do 8x7Bs at 3.5bpw with full context, provided I enable 8-bit cache, if that helps at all.
No worries if you can't swing it, of course. Just figured I'd put it out there.
Actually, this is one model that I was very excited and happy about. However, when trying to push it to GGUF or run it on BF16 apparently the 9B mistral trix model just simply does not work with mergekit MoE.
That being said, if you have an idea for an MoE model that you would want in EXL2 I will merge it together then talk to LoneStriker about converting to 3.5 BPW for you
ahhh I see!!
i'll keep that in mind then!