Instructions to use Open4bits/llama3.2-1b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Open4bits/llama3.2-1b-gguf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Open4bits/llama3.2-1b-gguf")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Open4bits/llama3.2-1b-gguf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Open4bits/llama3.2-1b-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 Open4bits/llama3.2-1b-gguf:F16 # Run inference directly in the terminal: llama cli -hf Open4bits/llama3.2-1b-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Open4bits/llama3.2-1b-gguf:F16 # Run inference directly in the terminal: llama cli -hf Open4bits/llama3.2-1b-gguf:F16
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 Open4bits/llama3.2-1b-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf Open4bits/llama3.2-1b-gguf:F16
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 Open4bits/llama3.2-1b-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Open4bits/llama3.2-1b-gguf:F16
Use Docker
docker model run hf.co/Open4bits/llama3.2-1b-gguf:F16
- LM Studio
- Jan
- vLLM
How to use Open4bits/llama3.2-1b-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Open4bits/llama3.2-1b-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open4bits/llama3.2-1b-gguf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Open4bits/llama3.2-1b-gguf:F16
- SGLang
How to use Open4bits/llama3.2-1b-gguf with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Open4bits/llama3.2-1b-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open4bits/llama3.2-1b-gguf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Open4bits/llama3.2-1b-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open4bits/llama3.2-1b-gguf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Open4bits/llama3.2-1b-gguf with Ollama:
ollama run hf.co/Open4bits/llama3.2-1b-gguf:F16
- Unsloth Studio
How to use Open4bits/llama3.2-1b-gguf 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 Open4bits/llama3.2-1b-gguf 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 Open4bits/llama3.2-1b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Open4bits/llama3.2-1b-gguf to start chatting
- Docker Model Runner
How to use Open4bits/llama3.2-1b-gguf with Docker Model Runner:
docker model run hf.co/Open4bits/llama3.2-1b-gguf:F16
- Lemonade
How to use Open4bits/llama3.2-1b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Open4bits/llama3.2-1b-gguf:F16
Run and chat with the model
lemonade run user.llama3.2-1b-gguf-F16
List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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license: llama3.2
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base_model:
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- meta-llama/Llama-3.2-1B
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license: llama3.2
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base_model:
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- meta-llama/Llama-3.2-1B
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---
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Here’s a **professional `README.md`** for **Open4bits/llama3.2-1b-gguf**, styled like your Whisper Tiny example:
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---
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# Open4bits / llama3.2-1b-gguf
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This repository provides the **LLaMA 3.2-1B model converted to GGUF format**, published by Open4bits to enable highly efficient local inference with reduced memory usage and broad CPU compatibility.
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The underlying LLaMA 3.2 model and architecture are **owned by Meta AI**. This repository contains only a quantized GGUF conversion of the original model weights.
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The model is designed for fast, lightweight text generation and instruction-following tasks and is well suited for resource-constrained environments.
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---
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## Model Overview
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LLaMA (Large Language Model Meta AI) is a family of transformer-based language models developed by Meta AI.
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This release uses the **3.2 variant with 1 billion parameters**, striking a balance between performance and efficiency.
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---
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## Model Details
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* **Architecture:** LLaMA 3.2
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* **Parameters:** ~1 billion
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* **Format:** GGUF (quantized)
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* **Task:** Text generation, instruction following
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* **Weight tying:** Preserved
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* **Compatibility:** GGUF-compatible inference runtimes (CPU-focused)
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Compared to larger LLaMA variants, this model offers significantly faster inference with lower memory requirements, with proportionally reduced capacity for complex reasoning.
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---
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## Intended Use
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This model is intended for:
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* Local text generation and chat applications
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* CPU-based or low-resource deployments
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* Research, experimentation, and prototyping
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* Offline or self-hosted AI systems
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---
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## Limitations
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* Lower generation quality compared to larger LLaMA 3.2 models
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* Output quality depends on prompt design and decoding settings
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* Not fine-tuned for domain-specific or high-precision tasks
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---
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## License
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This model is released under the **original LLaMA 3.2 license terms** as defined by Meta AI.
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Users must comply with the licensing conditions of the base LLaMA 3.2-1B model.
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---
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## Support
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If you find this model useful, please consider supporting the project.
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Your support helps Open4bits continue releasing and maintaining high-quality open models for the community.
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