135 lines
3.8 KiB
Markdown
135 lines
3.8 KiB
Markdown
# llama-swap
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# Introduction
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llama-swap is an OpenAI API compatible server that gives you complete control over how you use your hardware. It automatically swaps to the configuration of your choice for serving a model. Since [llama.cpp's server](https://github.com/ggerganov/llama.cpp/tree/master/examples/server) can't swap models, let's swap the server instead!
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Features:
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- ✅ Easy to deploy: single binary with no dependencies
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- ✅ Single yaml configuration file
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- ✅ Automatic switching between models
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- ✅ Full control over llama.cpp server settings per model
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- ✅ OpenAI API support (`v1/completions` and `v1/chat/completions`)
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- ✅ Multiple GPU support
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- ✅ Run multiple models at once with `profiles`
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- ✅ Remote log monitoring at `/log`
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- ✅ Automatic unloading of models from GPUs after timeout
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## config.yaml
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llama-swap's configuration is purposefully simple.
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```yaml
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# Seconds to wait for llama.cpp to load and be ready to serve requests
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# Default (and minimum) is 15 seconds
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healthCheckTimeout: 60
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# define valid model values and the upstream server start
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models:
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"llama":
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cmd: llama-server --port 8999 -m Llama-3.2-1B-Instruct-Q4_K_M.gguf
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# where to reach the server started by cmd, make sure the ports match
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proxy: http://127.0.0.1:8999
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# aliases names to use this model for
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aliases:
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- "gpt-4o-mini"
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- "gpt-3.5-turbo"
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# check this path for an HTTP 200 OK before serving requests
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# default: /health to match llama.cpp
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# use "none" to skip endpoint checking, but may cause HTTP errors
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# until the model is ready
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checkEndpoint: /custom-endpoint
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# automatically unload the model after this many seconds
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# ttl values must be a value greater than 0
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# default: 0 = never unload model
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ttl: 60
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"qwen":
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# environment variables to pass to the command
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env:
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- "CUDA_VISIBLE_DEVICES=0"
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# multiline for readability
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cmd: >
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llama-server --port 8999
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--model path/to/Qwen2.5-1.5B-Instruct-Q4_K_M.gguf
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proxy: http://127.0.0.1:8999
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# profiles make it easy to managing multi model (and gpu) configurations.
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#
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# Tips:
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# - each model must be listening on a unique address and port
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# - the model name is in this format: "profile_name:model", like "coding:qwen"
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# - the profile will load and unload all models in the profile at the same time
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profiles:
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coding:
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- "qwen"
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- "llama"
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```
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More [examples](examples/README.md) are available for different use cases.
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## Installation
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1. Create a configuration file, see [config.example.yaml](config.example.yaml)
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1. Download a [release](https://github.com/mostlygeek/llama-swap/releases) appropriate for your OS and architecture.
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* _Note: Windows currently untested._
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1. Run the binary with `llama-swap --config path/to/config.yaml`
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## Monitoring Logs
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Open the `http://<host>/logs` with your browser to get a web interface with streaming logs.
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Of course, CLI access is also supported:
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```
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# sends up to the last 10KB of logs
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curl http://host/logs'
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# streams logs
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curl -Ns 'http://host/logs/stream'
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# stream and filter logs with linux pipes
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curl -Ns http://host/logs/stream | grep 'eval time'
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# skips history and just streams new log entries
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curl -Ns 'http://host/logs/stream?no-history'
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```
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## Systemd Unit Files
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Use this unit file to start llama-swap on boot. This is only tested on Ubuntu.
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`/etc/systemd/system/llama-swap.service`
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```
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[Unit]
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Description=llama-swap
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After=network.target
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[Service]
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User=nobody
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# set this to match your environment
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ExecStart=/path/to/llama-swap --config /path/to/llama-swap.config.yml
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Restart=on-failure
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RestartSec=3
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StartLimitBurst=3
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StartLimitInterval=30
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[Install]
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WantedBy=multi-user.target
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```
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## Building from Source
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1. Install golang for your system
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1. run `make clean all`
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1. binaries will be built into `build/` directory
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