Instructions to use minkdank/LLAMA-JSON-data-extration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use minkdank/LLAMA-JSON-data-extration with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="minkdank/LLAMA-JSON-data-extration", filename="Llama-3.2-3B-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 minkdank/LLAMA-JSON-data-extration with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf minkdank/LLAMA-JSON-data-extration:Q4_K_M # Run inference directly in the terminal: llama-cli -hf minkdank/LLAMA-JSON-data-extration:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf minkdank/LLAMA-JSON-data-extration:Q4_K_M # Run inference directly in the terminal: llama-cli -hf minkdank/LLAMA-JSON-data-extration: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 minkdank/LLAMA-JSON-data-extration:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf minkdank/LLAMA-JSON-data-extration: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 minkdank/LLAMA-JSON-data-extration:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf minkdank/LLAMA-JSON-data-extration:Q4_K_M
Use Docker
docker model run hf.co/minkdank/LLAMA-JSON-data-extration:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use minkdank/LLAMA-JSON-data-extration with Ollama:
ollama run hf.co/minkdank/LLAMA-JSON-data-extration:Q4_K_M
- Unsloth Studio
How to use minkdank/LLAMA-JSON-data-extration 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 minkdank/LLAMA-JSON-data-extration 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 minkdank/LLAMA-JSON-data-extration to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for minkdank/LLAMA-JSON-data-extration to start chatting
- Docker Model Runner
How to use minkdank/LLAMA-JSON-data-extration with Docker Model Runner:
docker model run hf.co/minkdank/LLAMA-JSON-data-extration:Q4_K_M
- Lemonade
How to use minkdank/LLAMA-JSON-data-extration with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull minkdank/LLAMA-JSON-data-extration:Q4_K_M
Run and chat with the model
lemonade run user.LLAMA-JSON-data-extration-Q4_K_M
List all available models
lemonade list
Add README
Browse files
README.md
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base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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# LLAMA-JSON-data-extration - GGUF
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This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
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**Example usage**:
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- For text only LLMs: **llama-cli** **--hf** repo_id/model_name **-p** "why is the sky blue?"
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- For multimodal models: **llama-mtmd-cli** **-m** model_name.gguf **--mmproj** mmproj_file.gguf
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## Available Model files:
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- `llama-3.2-3b-instruct.Q4_K_M.gguf`
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## Ollama
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An Ollama Modelfile is included for easy deployment.
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