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
- llama-cpp-python
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
- 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 new
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
- 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
Model Card for Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
Building upon Mistral Small 3 (2501) and Mistral Small 3.1 (2503), Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 introduces cutting-edge 6-bit quantization technology to enhance efficiency and reduce memory usage without sacrificing performance. This model supports long context capabilities up to 128k tokens and maintains top-tier performance in both text and vision tasks with 24 billion parameters.
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
Key Features
6-Bit Quantization: Improved memory efficiency, allowing the model to run on lower-end hardware without compromising quality.
Knowledge-Dense Architecture: Fits within a single RTX 4090 or a 32GB RAM MacBook once quantized.
Enhanced Long Context Understanding: Supports up to 128k tokens, providing superior performance for extended documents.
State-of-the-Art Vision Understanding: Optimized for tasks involving both text and visual comprehension.
Ideal Use Cases
Fast-Response Conversational Agents
Low-Latency Function Calling
Subject Matter Experts via Fine-Tuning
Local Inference for Hobbyists and Organizations Handling Sensitive Data
Programming and Math Reasoning
Long Document Understanding
Visual Understanding
Deployment
This model can be deployed locally with 6-bit quantization, ensuring both high performance and efficiency on compatible hardware.
Key Features
Vision: Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi.
Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Advanced Reasoning: State-of-the-art conversational and reasoning capabilities.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 128k context window.
System Prompt: Maintains strong adherence and support for system prompts.
Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.
GGUF Format Conversion
This model was converted to GGUF format from Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux):
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 --hf-file private-bitsix-mistral-small-3.1-24b-instruct-2503.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo openfree/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 --hf-file private-bitsix-mistral-small-3.1-24b-instruct-2503.gguf -c 2048
Setup Instructions
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (e.g., LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo openfree/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 --hf-file private-bitsix-mistral-small-3.1-24b-instruct-2503.gguf -p "The meaning to life and the universe is"
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Model tree for ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
Base model
mistralai/Mistral-Small-3.1-24B-Base-2503