Instructions to use Felipe97/llama-cpp-compiled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Felipe97/llama-cpp-compiled with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Felipe97/llama-cpp-compiled # Run inference directly in the terminal: llama cli -hf Felipe97/llama-cpp-compiled
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Felipe97/llama-cpp-compiled # Run inference directly in the terminal: llama cli -hf Felipe97/llama-cpp-compiled
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 Felipe97/llama-cpp-compiled # Run inference directly in the terminal: ./llama-cli -hf Felipe97/llama-cpp-compiled
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 Felipe97/llama-cpp-compiled # Run inference directly in the terminal: ./build/bin/llama-cli -hf Felipe97/llama-cpp-compiled
Use Docker
docker model run hf.co/Felipe97/llama-cpp-compiled
- LM Studio
- Jan
- Ollama
How to use Felipe97/llama-cpp-compiled with Ollama:
ollama run hf.co/Felipe97/llama-cpp-compiled
- Unsloth Desktop
- Docker Model Runner
How to use Felipe97/llama-cpp-compiled with Docker Model Runner:
docker model run hf.co/Felipe97/llama-cpp-compiled
- Lemonade
How to use Felipe97/llama-cpp-compiled with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Felipe97/llama-cpp-compiled
Run and chat with the model
lemonade run user.llama-cpp-compiled-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,012 Bytes
1e18f83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # XCFramework
The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS,
and macOS. It can be used in Swift projects without the need to compile the
library from source. For example:
```swift
// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.
import PackageDescription
let package = Package(
name: "MyLlamaPackage",
targets: [
.executableTarget(
name: "MyLlamaPackage",
dependencies: [
"LlamaFramework"
]),
.binaryTarget(
name: "LlamaFramework",
url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
)
]
)
```
The above example is using an intermediate build `b5046` of the library. This can be modified
to use a different version by changing the URL and checksum.
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