Instructions to use onicai/llama_cpp_canister_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

icpp commited on Sep 26, 2024

Commit

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@@ -6,10 +6,12 @@ language:
 # On-chain llama.cpp - Internet Computer
-- Run on-chain (Internet Computer) with [onicai/llama_cpp_canister](https://github.com/onicai/llama_cpp_canister)
-- Run local with [ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
 - Try them out at [ICGPT](https://icgpt.icpp.world/)
 - The models were created with the training procedure outlined in [karpathy/llama2.c](https://github.com/karpathy/llama2.c) and then converted into *.gguf format as described below.

 # 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](https://github.com/onicai/llama_cpp_canister)
+Notes:
 - Try them out at [ICGPT](https://icgpt.icpp.world/)
+- To use on the Internet Computer, follow instructions of [onicai/llama_cpp_canister](https://github.com/onicai/llama_cpp_canister)
+- Run local with [ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
 - The models were created with the training procedure outlined in [karpathy/llama2.c](https://github.com/karpathy/llama2.c) and then converted into *.gguf format as described below.