Instructions to use hekod19045/llama-cuda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hekod19045/llama-cuda with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hekod19045/llama-cuda",
	filename="models/ggml-vocab-aquila.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 hekod19045/llama-cuda with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hekod19045/llama-cuda
# Run inference directly in the terminal:
llama-cli -hf hekod19045/llama-cuda

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hekod19045/llama-cuda
# Run inference directly in the terminal:
llama-cli -hf hekod19045/llama-cuda

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 hekod19045/llama-cuda
# Run inference directly in the terminal:
./llama-cli -hf hekod19045/llama-cuda

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 hekod19045/llama-cuda
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hekod19045/llama-cuda

Use Docker

docker model run hf.co/hekod19045/llama-cuda

LM Studio
Jan
Ollama
How to use hekod19045/llama-cuda with Ollama:
```
ollama run hf.co/hekod19045/llama-cuda
```

Unsloth Studio

How to use hekod19045/llama-cuda 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 hekod19045/llama-cuda 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 hekod19045/llama-cuda to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hekod19045/llama-cuda to start chatting

Docker Model Runner
How to use hekod19045/llama-cuda with Docker Model Runner:
```
docker model run hf.co/hekod19045/llama-cuda
```

Lemonade

How to use hekod19045/llama-cuda with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hekod19045/llama-cuda

Run and chat with the model

lemonade run user.llama-cuda-{{QUANT_TAG}}

List all available models

lemonade list

llama-cuda / docs /install.md

hekod19045

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Install pre-built version of llama.cpp

Homebrew

On Mac and Linux, the homebrew package manager can be used via

brew install llama.cpp

The formula is automatically updated with new llama.cpp releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668

MacPorts

sudo port install llama.cpp

Nix

On Mac and Linux, the Nix package manager can be used via

nix profile install nixpkgs#llama-cpp

For flake enabled installs.

nix-env --file '<nixpkgs>' --install --attr llama-cpp

For non-flake enabled installs.

This expression is automatically updated within the nixpkgs repo.

Flox

On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via

flox install llama-cpp

Flox follows the nixpkgs build of llama.cpp.