Command-line interface for RuvLLM inference, optimized for Apple Silicon.
# From crates.io
cargo install ruvllm-cli
# From source (with Metal acceleration)
cargo install --path . --features metalDownload models from HuggingFace Hub:
# Download Qwen with Q4K quantization (default)
ruvllm download qwen
# Download with specific quantization
ruvllm download qwen --quantization q8
ruvllm download mistral --quantization f16
# Force re-download
ruvllm download phi --force
# Download specific revision
ruvllm download llama --revision main| Alias | Model ID |
|---|---|
qwen |
Qwen/Qwen2.5-7B-Instruct |
mistral |
mistralai/Mistral-7B-Instruct-v0.3 |
phi |
microsoft/Phi-3-medium-4k-instruct |
llama |
meta-llama/Meta-Llama-3.1-8B-Instruct |
| Option | Description | Memory Savings |
|---|---|---|
q4k |
4-bit quantization (default) | ~75% |
q8 |
8-bit quantization | ~50% |
f16 |
Half precision | ~50% |
none |
Full precision | 0% |
# List all available models
ruvllm list
# List only downloaded models
ruvllm list --downloaded
# Detailed listing with sizes
ruvllm list --long# Show model details
ruvllm info qwen
# Output includes:
# - Model architecture
# - Parameter count
# - Download status
# - Disk usage
# - Supported features# Start chat with default settings
ruvllm chat qwen
# With custom system prompt
ruvllm chat qwen --system "You are a helpful coding assistant."
# Adjust generation parameters
ruvllm chat qwen --temperature 0.5 --max-tokens 1024
# Use specific quantization
ruvllm chat qwen --quantization q8During chat, use these commands:
| Command | Description |
|---|---|
/help |
Show available commands |
/clear |
Clear conversation history |
/system <prompt> |
Change system prompt |
/temp <value> |
Change temperature |
/quit or /exit |
Exit chat |
OpenAI-compatible inference server:
# Start with defaults
ruvllm serve qwen
# Custom host and port
ruvllm serve qwen --host 0.0.0.0 --port 8080
# Configure concurrency
ruvllm serve qwen --max-concurrent 8 --max-context 8192| Endpoint | Method | Description |
|---|---|---|
/v1/chat/completions |
POST | Chat completions |
/v1/completions |
POST | Text completions |
/v1/models |
GET | List models |
/health |
GET | Health check |
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen",
"messages": [
{"role": "user", "content": "Hello!"}
],
"max_tokens": 256
}'# Basic benchmark
ruvllm benchmark qwen
# Configure benchmark
ruvllm benchmark qwen \
--warmup 5 \
--iterations 20 \
--prompt-length 256 \
--gen-length 128
# Output formats
ruvllm benchmark qwen --format json
ruvllm benchmark qwen --format csv- Prefill Latency: Time to process input prompt
- Decode Throughput: Tokens per second during generation
- Time to First Token (TTFT): Latency before first output token
- Memory Usage: Peak GPU/RAM consumption
# Enable verbose logging
ruvllm --verbose <command>
# Disable colored output
ruvllm --no-color <command>
# Custom cache directory
ruvllm --cache-dir /path/to/cache <command>
# Or via environment variable
export RUVLLM_CACHE_DIR=/path/to/cacheModels are cached in:
- macOS:
~/Library/Caches/ruvllm - Linux:
~/.cache/ruvllm - Windows:
%LOCALAPPDATA%\ruvllm
Override with --cache-dir or RUVLLM_CACHE_DIR.
Set log level with RUST_LOG:
RUST_LOG=debug ruvllm chat qwen
RUST_LOG=ruvllm=trace ruvllm serve qwen# 1. Download a model
ruvllm download qwen
# 2. Verify it's downloaded
ruvllm list --downloaded
# 3. Start chatting
ruvllm chat qwen# Download model first
ruvllm download qwen --quantization q4k
# Start server with production settings
ruvllm serve qwen \
--host 0.0.0.0 \
--port 8080 \
--max-concurrent 16 \
--max-context 4096 \
--quantization q4k# Run comprehensive benchmarks
ruvllm benchmark qwen \
--warmup 10 \
--iterations 50 \
--prompt-length 512 \
--gen-length 256 \
--format json > benchmark_results.json# Use smaller quantization
ruvllm chat qwen --quantization q4k
# Or reduce context length
ruvllm serve qwen --max-context 2048# Resume interrupted download
ruvllm download qwen
# Force fresh download
ruvllm download qwen --forceEnsure Metal is available:
# Check Metal device
system_profiler SPDisplaysDataType | grep Metal
# Try with CPU fallback
RUVLLM_NO_METAL=1 ruvllm chat qwenBuild with specific features:
# Metal acceleration (macOS)
cargo install ruvllm-cli --features metal
# CUDA acceleration (NVIDIA)
cargo install ruvllm-cli --features cuda
# Both (if available)
cargo install ruvllm-cli --features "metal,cuda"Apache-2.0 / MIT dual license.