Instructions to use sinatras/mixtral-8x7b-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sinatras/mixtral-8x7b-split with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sinatras/mixtral-8x7b-split",
	filename="Q2_K/mixtral-8x7b-instruct-v0.1.Q2_K-00001-of-00009.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 sinatras/mixtral-8x7b-split with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinatras/mixtral-8x7b-split:Q2_K
# Run inference directly in the terminal:
llama-cli -hf sinatras/mixtral-8x7b-split:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinatras/mixtral-8x7b-split:Q2_K
# Run inference directly in the terminal:
llama-cli -hf sinatras/mixtral-8x7b-split:Q2_K

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 sinatras/mixtral-8x7b-split:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf sinatras/mixtral-8x7b-split:Q2_K

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 sinatras/mixtral-8x7b-split:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sinatras/mixtral-8x7b-split:Q2_K

Use Docker

docker model run hf.co/sinatras/mixtral-8x7b-split:Q2_K

LM Studio
Jan
Ollama
How to use sinatras/mixtral-8x7b-split with Ollama:
```
ollama run hf.co/sinatras/mixtral-8x7b-split:Q2_K
```

Unsloth Studio new

How to use sinatras/mixtral-8x7b-split 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 sinatras/mixtral-8x7b-split 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 sinatras/mixtral-8x7b-split to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sinatras/mixtral-8x7b-split to start chatting

Docker Model Runner
How to use sinatras/mixtral-8x7b-split with Docker Model Runner:
```
docker model run hf.co/sinatras/mixtral-8x7b-split:Q2_K
```

Lemonade

How to use sinatras/mixtral-8x7b-split with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sinatras/mixtral-8x7b-split:Q2_K

Run and chat with the model

lemonade run user.mixtral-8x7b-split-Q2_K

List all available models

lemonade list

mixtral-8x7b-split

Mixtral 8x7B Instruct split GGUF artifacts used by the playground wllama preset.

These files are the GGUF artifacts used by the local Transformers.js playground wllama CPU presets. Large files are kept under quantization subdirectories so browser clients can request the first shard URL and expand the remaining shards.

Source And License

Source model/artifact: kat33/Mixtral-8x7B-Instruct-v0.1-Q2_K-GGUF
License: Apache-2.0, inherited from the source model/artifact.

The GGUF conversion, quantization, and splitting steps do not change the upstream model license.

Downloads last month: 16

GGUF

Model size

47B params

Architecture

llama

Hardware compatibility

2-bit

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Model tree for sinatras/mixtral-8x7b-split

Base model

kat33/Mixtral-8x7B-Instruct-v0.1-Q2_K-GGUF

Quantized

(1)

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