Instructions to use onicai/llama_cpp_canister_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use onicai/llama_cpp_canister_models with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="onicai/llama_cpp_canister_models", filename="stories110Mtok32000.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 onicai/llama_cpp_canister_models with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf onicai/llama_cpp_canister_models # Run inference directly in the terminal: llama-cli -hf onicai/llama_cpp_canister_models
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf onicai/llama_cpp_canister_models # Run inference directly in the terminal: llama-cli -hf onicai/llama_cpp_canister_models
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 onicai/llama_cpp_canister_models # Run inference directly in the terminal: ./llama-cli -hf onicai/llama_cpp_canister_models
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 onicai/llama_cpp_canister_models # Run inference directly in the terminal: ./build/bin/llama-cli -hf onicai/llama_cpp_canister_models
Use Docker
docker model run hf.co/onicai/llama_cpp_canister_models
- LM Studio
- Jan
- Ollama
How to use onicai/llama_cpp_canister_models with Ollama:
ollama run hf.co/onicai/llama_cpp_canister_models
- Unsloth Studio
How to use onicai/llama_cpp_canister_models 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 onicai/llama_cpp_canister_models 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 onicai/llama_cpp_canister_models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for onicai/llama_cpp_canister_models to start chatting
- Atomic Chat new
- Docker Model Runner
How to use onicai/llama_cpp_canister_models with Docker Model Runner:
docker model run hf.co/onicai/llama_cpp_canister_models
- Lemonade
How to use onicai/llama_cpp_canister_models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull onicai/llama_cpp_canister_models
Run and chat with the model
lemonade run user.llama_cpp_canister_models-{{QUANT_TAG}}List all available models
lemonade list
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf onicai/llama_cpp_canister_models# Run inference directly in the terminal:
llama-cli -hf onicai/llama_cpp_canister_modelsUse 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 onicai/llama_cpp_canister_models# Run inference directly in the terminal:
./llama-cli -hf onicai/llama_cpp_canister_modelsBuild 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 onicai/llama_cpp_canister_models# Run inference directly in the terminal:
./build/bin/llama-cli -hf onicai/llama_cpp_canister_modelsUse Docker
docker model run hf.co/onicai/llama_cpp_canister_modelsQuick Links
On-chain llama.cpp - Internet Computer
You can run any *.gguf file in a llama_cpp_canister, but these are smaller models you can use for testing onicai/llama_cpp_canister
Notes:
- Try them out at ICGPT
- To use on the Internet Computer, follow instructions of onicai/llama_cpp_canister
- Run local with ggerganov/llama.cpp
- The models were created with the training procedure outlined in karpathy/llama2.c and then converted into *.gguf format as described below.
Setup local git with lfs
# install git lfs
# Ubuntu
git lfs install
# Mac
brew install git-lfs
# install huggingface CLI tools in a python environment
pip install huggingface-hub
# Clone this repo
# https
git clone https://huggingface.co/onicai/llama_cpp_canister_models
# ssh
git clone git@hf.co:onicai/llama_cpp_canister_models
cd llama_cpp_canister_models
# configure lfs for local repo
huggingface-cli lfs-enable-largefiles .
# tell lfs what files to track (.gitattributes)
git lfs track "*.gguf"
# add, commit & push as usual with git
git add <file-name>
git commit -m "Adding <file-name>"
git push -u origin main
Model creation
We used convert-llama2c-to-ggml to convert the llama2.c model+tokenizer to llama.cpp gguf format.
- Good read: lama : add support for llama2.c models
For example:
# From llama.cpp root folder
# Build everything
make -j
# Convert a llama2c model+tokenizer to gguf
convert-llama2c-to-ggml --llama2c-model stories260Ktok512.bin --copy-vocab-from-model tok512.bin --llama2c-output-model stories260Ktok512.gguf
convert-llama2c-to-ggml --llama2c-model stories15Mtok4096.bin --copy-vocab-from-model tok4096.bin --llama2c-output-model stories15Mtok4096.gguf
convert-llama2c-to-ggml --llama2c-model stories42Mtok4096.bin --copy-vocab-from-model tok4096.bin --llama2c-output-model stories42Mtok4096.gguf
convert-llama2c-to-ggml --llama2c-model stories110Mtok32000.bin --copy-vocab-from-model models/ggml-vocab-llama.gguf --llama2c-output-model stories110Mtok32000.gguf
convert-llama2c-to-ggml --llama2c-model stories42Mtok32000.bin --copy-vocab-from-model models/ggml-vocab-llama.gguf --llama2c-output-model stories42Mtok32000.gguf
# Run it local, like this
./llama-cli -m stories15Mtok4096.gguf -p "Joe loves writing stories" -n 600 -c 128
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf onicai/llama_cpp_canister_models# Run inference directly in the terminal: llama-cli -hf onicai/llama_cpp_canister_models