Instructions to use TheBloke/Mixtral_7Bx2_MoE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/Mixtral_7Bx2_MoE-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/Mixtral_7Bx2_MoE-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TheBloke/Mixtral_7Bx2_MoE-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 TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TheBloke/Mixtral_7Bx2_MoE-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 TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TheBloke/Mixtral_7Bx2_MoE-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 TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TheBloke/Mixtral_7Bx2_MoE-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 TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use TheBloke/Mixtral_7Bx2_MoE-GGUF with Ollama:
ollama run hf.co/TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use TheBloke/Mixtral_7Bx2_MoE-GGUF with Docker Model Runner:
docker model run hf.co/TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M
- Lemonade
How to use TheBloke/Mixtral_7Bx2_MoE-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheBloke/Mixtral_7Bx2_MoE-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Mixtral_7Bx2_MoE-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
[Bug Report] <0x0A> is output instead of a newline
The character sequence <0x0A> is output every time instead of a newline.
This redditor suggests there may be a problem with the tokenizer in the original non-GGUF model that carried over.
Are you using LM Studio? I saw something similar but I bet it has to do with the Preset you have chosen.
It happens using Ollama as well -- the model is outputting the token for a newline, but it's not interpreted as such.
Could be solved programatically if you're using this model in a server environment with its outputs passed to a program, otherwise - I'm not sure of another solution.
I have the same bug. I tried on few versions of koboldCpp, in their KoboldLite front-end, in SillyTavern with different chat templates, but bug stays. For me this is the best Mixtral model i tried, even better that x8 moe models. Its really good in staying in character, speech style etc.
same as above.
I have the same issue. I think this problem about the original model(Mixtral 7Bx2 MoE) missing tokenizer.model file.
Here is how i fix:
git clone https://huggingface.co/cloudyu/Mixtral_7Bx2_MoEcd Mixtral_7Bx2_MoE && curl -L -O https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/resolve/main/tokenizer.model- use llama.cpp reconvert model
python convert.py ../Mixtral_7Bx2_MoE ./quantize ../Mixtral_7Bx2_MoE/ggml-model-f16.gguf ../Mixtral_7Bx2_MoE/ggml-model-q4_K_M.gguf q4_K_M
I can't load this model by ctranformers