Instructions to use SciTools/Llama3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use SciTools/Llama3.2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SciTools/Llama3.2", filename="Llama-3.2-1B-Instruct-Q8_0.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 SciTools/Llama3.2 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SciTools/Llama3.2:Q8_0 # Run inference directly in the terminal: llama-cli -hf SciTools/Llama3.2:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SciTools/Llama3.2:Q8_0 # Run inference directly in the terminal: llama-cli -hf SciTools/Llama3.2:Q8_0
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 SciTools/Llama3.2:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf SciTools/Llama3.2:Q8_0
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 SciTools/Llama3.2:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf SciTools/Llama3.2:Q8_0
Use Docker
docker model run hf.co/SciTools/Llama3.2:Q8_0
- LM Studio
- Jan
- Ollama
How to use SciTools/Llama3.2 with Ollama:
ollama run hf.co/SciTools/Llama3.2:Q8_0
- Unsloth Studio
How to use SciTools/Llama3.2 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 SciTools/Llama3.2 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 SciTools/Llama3.2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SciTools/Llama3.2 to start chatting
- Pi
How to use SciTools/Llama3.2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf SciTools/Llama3.2:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "SciTools/Llama3.2:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use SciTools/Llama3.2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf SciTools/Llama3.2:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default SciTools/Llama3.2:Q8_0
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use SciTools/Llama3.2 with Docker Model Runner:
docker model run hf.co/SciTools/Llama3.2:Q8_0
- Lemonade
How to use SciTools/Llama3.2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SciTools/Llama3.2:Q8_0
Run and chat with the model
lemonade run user.Llama3.2-Q8_0
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SciTools/Llama3.2:Q8_0# Run inference directly in the terminal:
llama-cli -hf SciTools/Llama3.2:Q8_0Use 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 SciTools/Llama3.2:Q8_0# Run inference directly in the terminal:
./llama-cli -hf SciTools/Llama3.2:Q8_0Build 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 SciTools/Llama3.2:Q8_0# Run inference directly in the terminal:
./build/bin/llama-cli -hf SciTools/Llama3.2:Q8_0Use Docker
docker model run hf.co/SciTools/Llama3.2:Q8_0Llama 3.2 GGUF (4_K_M Quantized)
This repository hosts GGUF-format quantized versions of Llama 3.2 models at multiple parameter sizes.
These files are intended for use with SciTools’ Understand and Onboard, as well as other tools and runtimes that support the GGUF format (for example, llama.cpp-based applications).
Model Details
- Base models: Llama 3.2 (various parameter sizes)
- Format: GGUF
- Intended use: Local inference, code understanding, general-purpose chat
- Languages: Multilingual (as supported by Llama 3.2)
Available Variants
This repository includes multiple Llama 3.2 parameter sizes, each quantized independently. Refer to the file names for exact parameter counts.
Quantization Process
- Quantization was performed by Unsloth and TensorBlock.
- No further modifications, rebalancing, or fine-tuning were applied.
- The quantization parameters and defaults were not altered from the original sources.
The goal is to provide faithful, reproducible GGUF variants that behave as closely as possible to their upstream counterparts.
What We Did Not Do
To be explicit:
- No additional fine-tuning
- No instruction rebalancing
- No safety, alignment, or prompt modifications
- No merging or model surgery
If a model behaves a certain way, that behavior comes from Llama 3.2 combined with quantization, not from any downstream changes here.
Intended Use
These models are suitable for:
- SciTools Understand and SciTools Onboard
- Local AI workflows
- Code comprehension and exploration
- Interactive chat and analysis
- Integration into developer tools that support GGUF
They are not intended for:
- Safety-critical or regulated decision-making
- Use cases requiring guaranteed factual accuracy
- Production deployment without independent evaluation
Limitations
- Output quality varies by parameter size and task.
- Like all large language models, Llama 3.2 may produce hallucinations or incorrect information.
Evaluate carefully for your specific workload.
License & Attribution
- Original models: Meta (Llama 3.2)
- Quantization: Unsloth and TensorBlock
- Format: GGUF (llama.cpp ecosystem)
Please refer to the original Llama 3.2 license and usage terms. This repository redistributes quantized artifacts only and does not change the underlying licensing conditions.
Acknowledgements
Thanks to Meta for releasing the Llama 3.2 models, and to Unsloth and TensorBlock for providing high-quality, reproducible quantization that enables efficient local inference across a wide range of tools.
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf SciTools/Llama3.2:Q8_0# Run inference directly in the terminal: llama-cli -hf SciTools/Llama3.2:Q8_0