Instructions to use sagar27kumar/LlamaLite-3B-TQ2_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sagar27kumar/LlamaLite-3B-TQ2_0", filename="Llama-3.2-3B-TQ2_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0 # Run inference directly in the terminal: llama-cli -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0 # Run inference directly in the terminal: llama-cli -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_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 sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0 # Run inference directly in the terminal: ./llama-cli -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_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 sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
Use Docker
docker model run hf.co/sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
- LM Studio
- Jan
- vLLM
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sagar27kumar/LlamaLite-3B-TQ2_0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sagar27kumar/LlamaLite-3B-TQ2_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
- Ollama
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with Ollama:
ollama run hf.co/sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
- Unsloth Studio
How to use sagar27kumar/LlamaLite-3B-TQ2_0 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 sagar27kumar/LlamaLite-3B-TQ2_0 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 sagar27kumar/LlamaLite-3B-TQ2_0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sagar27kumar/LlamaLite-3B-TQ2_0 to start chatting
- Pi
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_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": "sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_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 sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
Run Hermes
hermes
- Docker Model Runner
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with Docker Model Runner:
docker model run hf.co/sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
- Lemonade
How to use sagar27kumar/LlamaLite-3B-TQ2_0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sagar27kumar/LlamaLite-3B-TQ2_0:TQ2_0
Run and chat with the model
lemonade run user.LlamaLite-3B-TQ2_0-TQ2_0
List all available models
lemonade list
LlamaLite-3B-TQ2_0 (GGUF Format)
This is a quantized version of meta-llama/Llama-3.2-3B-Instruct, using TQ2_0 quantization for optimized performance and reduced size. The model is stored in GGUF format for compatibility with llama.cpp and other lightweight inference engines.
Model Details
- Base Model: Llama-3.2-3B-Instruct
- Quantization Type:
TQ2_0 - Model Size: ~1.52GB
- Format: GGUF
- Intended Use: Text Generation, Chatbots, AI Assistants
- License: MIT
Download & Usage
1️⃣ Install Dependencies
pip install huggingface_hub
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Model tree for sagar27kumar/LlamaLite-3B-TQ2_0
Base model
meta-llama/Llama-3.2-3B-Instruct