ruvector/crates/ruvllm-cli

RuvLLM CLI

Command-line interface for RuvLLM inference, optimized for Apple Silicon.

Installation

# From crates.io
cargo install ruvllm-cli

# From source (with Metal acceleration)
cargo install --path . --features metal

Commands

Download Models

Download 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

Model Aliases

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

Quantization Options

Option	Description	Memory Savings
q4k	4-bit quantization (default)	~75%
q8	8-bit quantization	~50%
f16	Half precision	~50%
none	Full precision	0%

List Models

# List all available models
ruvllm list

# List only downloaded models
ruvllm list --downloaded

# Detailed listing with sizes
ruvllm list --long

Model Information

# Show model details
ruvllm info qwen

# Output includes:
# - Model architecture
# - Parameter count
# - Download status
# - Disk usage
# - Supported features

Interactive Chat

# 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 q8

Chat Commands

During 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

Start Server

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

API Endpoints

Endpoint	Method	Description
/v1/chat/completions	POST	Chat completions
/v1/completions	POST	Text completions
/v1/models	GET	List models
/health	GET	Health check

Example Request

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ],
    "max_tokens": 256
  }'

Run Benchmarks

# 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

Benchmark Metrics

• 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

Global Options

# 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/cache

Configuration

Cache Directory

Models are cached in:

• macOS: ~/Library/Caches/ruvllm
• Linux: ~/.cache/ruvllm
• Windows: %LOCALAPPDATA%\ruvllm

Override with --cache-dir or RUVLLM_CACHE_DIR.

Logging

Set log level with RUST_LOG:

RUST_LOG=debug ruvllm chat qwen
RUST_LOG=ruvllm=trace ruvllm serve qwen

Examples

Basic Workflow

# 1. Download a model
ruvllm download qwen

# 2. Verify it's downloaded
ruvllm list --downloaded

# 3. Start chatting
ruvllm chat qwen

Server Deployment

# 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

Performance Testing

# Run comprehensive benchmarks
ruvllm benchmark qwen \
  --warmup 10 \
  --iterations 50 \
  --prompt-length 512 \
  --gen-length 256 \
  --format json > benchmark_results.json

Troubleshooting

Out of Memory

# Use smaller quantization
ruvllm chat qwen --quantization q4k

# Or reduce context length
ruvllm serve qwen --max-context 2048

Slow Download

# Resume interrupted download
ruvllm download qwen

# Force fresh download
ruvllm download qwen --force

Metal Issues (macOS)

Ensure Metal is available:

# Check Metal device
system_profiler SPDisplaysDataType | grep Metal

# Try with CPU fallback
RUVLLM_NO_METAL=1 ruvllm chat qwen

Feature Flags

Build 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"

License

Apache-2.0 / MIT dual license.