Instructions to use kadasterdst/aardcode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kadasterdst/aardcode with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kadasterdst/aardcode", dtype="auto") - llama-cpp-python
How to use kadasterdst/aardcode with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="kadasterdst/aardcode", filename="unsloth.Q8_0.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 kadasterdst/aardcode with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kadasterdst/aardcode:Q8_0 # Run inference directly in the terminal: llama-cli -hf kadasterdst/aardcode:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kadasterdst/aardcode:Q8_0 # Run inference directly in the terminal: llama-cli -hf kadasterdst/aardcode: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 kadasterdst/aardcode:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf kadasterdst/aardcode: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 kadasterdst/aardcode:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kadasterdst/aardcode:Q8_0
Use Docker
docker model run hf.co/kadasterdst/aardcode:Q8_0
- LM Studio
- Jan
- Ollama
How to use kadasterdst/aardcode with Ollama:
ollama run hf.co/kadasterdst/aardcode:Q8_0
- Unsloth Studio
How to use kadasterdst/aardcode 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 kadasterdst/aardcode 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 kadasterdst/aardcode to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kadasterdst/aardcode to start chatting
- Docker Model Runner
How to use kadasterdst/aardcode with Docker Model Runner:
docker model run hf.co/kadasterdst/aardcode:Q8_0
- Lemonade
How to use kadasterdst/aardcode with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kadasterdst/aardcode:Q8_0
Run and chat with the model
lemonade run user.aardcode-Q8_0
List all available models
lemonade list
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| "<|im_start|>", | |
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| "<|object_ref_start|>", | |
| "<|object_ref_end|>", | |
| "<|box_start|>", | |
| "<|box_end|>", | |
| "<|quad_start|>", | |
| "<|quad_end|>", | |
| "<|vision_start|>", | |
| "<|vision_end|>", | |
| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>" | |
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| "errors": "replace", | |
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| "pad_token": "<|vision_pad|>", | |
| "padding_side": "right", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } | |