Instructions to use codewithdark/Llama-3.2-3B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use codewithdark/Llama-3.2-3B-4bit with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="codewithdark/Llama-3.2-3B-4bit", filename="Llama-3.2-3B.Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use codewithdark/Llama-3.2-3B-4bit 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 codewithdark/Llama-3.2-3B-4bit:Q4_K_M # Run inference directly in the terminal: llama cli -hf codewithdark/Llama-3.2-3B-4bit:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf codewithdark/Llama-3.2-3B-4bit:Q4_K_M # Run inference directly in the terminal: llama cli -hf codewithdark/Llama-3.2-3B-4bit:Q4_K_M
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 codewithdark/Llama-3.2-3B-4bit:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf codewithdark/Llama-3.2-3B-4bit:Q4_K_M
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 codewithdark/Llama-3.2-3B-4bit:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf codewithdark/Llama-3.2-3B-4bit:Q4_K_M
Use Docker
docker model run hf.co/codewithdark/Llama-3.2-3B-4bit:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use codewithdark/Llama-3.2-3B-4bit with Ollama:
ollama run hf.co/codewithdark/Llama-3.2-3B-4bit:Q4_K_M
- Unsloth Studio
How to use codewithdark/Llama-3.2-3B-4bit 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 codewithdark/Llama-3.2-3B-4bit 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 codewithdark/Llama-3.2-3B-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for codewithdark/Llama-3.2-3B-4bit to start chatting
- Atomic Chat new
- Docker Model Runner
How to use codewithdark/Llama-3.2-3B-4bit with Docker Model Runner:
docker model run hf.co/codewithdark/Llama-3.2-3B-4bit:Q4_K_M
- Lemonade
How to use codewithdark/Llama-3.2-3B-4bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull codewithdark/Llama-3.2-3B-4bit:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3.2-3B-4bit-Q4_K_M
List all available models
lemonade list
Upload model via QuantLLM
Browse files- .gitattributes +1 -0
- Llama-3.2-3B.Q4_K_M.gguf +3 -0
- README.md +4 -1
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Llama-3.2-3B.Q4_K_M.gguf
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README.md
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---
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base_model:
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library_name: quantllm
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tags:
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- quantllm
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## Hyperparameters
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The following hyperparameters were used during training:
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---
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base_model: Llama-3.2-3B
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library_name: quantllm
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tags:
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- quantllm
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## Hyperparameters
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The following hyperparameters were used during training:
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- **format**: gguf
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- **base_model**: Llama-3.2-3B
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