Instructions to use ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503",
	filename="Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503.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 ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
# Run inference directly in the terminal:
llama-cli -hf ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
# Run inference directly in the terminal:
llama-cli -hf ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503

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 ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
# Run inference directly in the terminal:
./llama-cli -hf ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503

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 ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503

Use Docker

docker model run hf.co/ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503

LM Studio
Jan
Ollama
How to use ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 with Ollama:
```
ollama run hf.co/ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
```

Unsloth Studio

How to use ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 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 ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 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 ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 to start chatting

Atomic Chat new
Docker Model Runner
How to use ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 with Docker Model Runner:
```
docker model run hf.co/ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
```

Lemonade

How to use ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503

Run and chat with the model

lemonade run user.Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503-{{QUANT_TAG}}

List all available models

lemonade list

ginipick commited on Mar 31, 2025

Commit

b48d800

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Update README.md

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@@ -12,6 +12,11 @@ Building upon Mistral Small 3 (2501) and Mistral Small 3.1 (2503), Private-BitSi
 This model is an instruction-finetuned and 6-bit quantized version of: Mistral-Small-3.1-24B-Base-2503.
 # Key Features
 6-Bit Quantization: Improved memory efficiency, allowing the model to run on lower-end hardware without compromising quality.

 This model is an instruction-finetuned and 6-bit quantized version of: Mistral-Small-3.1-24B-Base-2503.
+# CPP Agent(Usage): Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
+https://huggingface.co/spaces/ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
 # Key Features
 6-Bit Quantization: Improved memory efficiency, allowing the model to run on lower-end hardware without compromising quality.