Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
599
crates/ruvllm/docs/GITHUB_ISSUE_V2.md
Normal file
599
crates/ruvllm/docs/GITHUB_ISSUE_V2.md
Normal file
@@ -0,0 +1,599 @@
|
||||
# 🚀 RuvLLM v2.0 - High-Performance LLM Inference for Apple Silicon
|
||||
|
||||
[](https://crates.io/crates/ruvllm)
|
||||
[](https://www.npmjs.com/package/@aspect/ruvllm)
|
||||
[](https://ruv.io/docs/ruvllm)
|
||||
[](LICENSE)
|
||||
[](https://github.com/aspect/ruvector/actions)
|
||||
[](https://discord.gg/ruv)
|
||||
|
||||
<p align="center">
|
||||
<img src="https://ruv.io/assets/ruvllm-logo.svg" alt="RuvLLM" width="200"/>
|
||||
<br/>
|
||||
<strong>Run Large Language Models locally on your Mac with maximum performance</strong>
|
||||
<br/>
|
||||
<a href="https://ruv.io/ruvllm">Website</a> •
|
||||
<a href="https://ruv.io/docs/ruvllm">Documentation</a> •
|
||||
<a href="https://discord.gg/ruv">Discord</a> •
|
||||
<a href="https://twitter.com/raboruv">Twitter</a>
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
## What is RuvLLM?
|
||||
|
||||
**RuvLLM** is a blazing-fast LLM inference engine built in Rust, specifically optimized for Apple Silicon Macs (M1/M2/M3/M4). It lets you run AI models like Llama, Mistral, Phi, and Gemma directly on your laptop — no cloud, no API costs, complete privacy.
|
||||
|
||||
### Why RuvLLM?
|
||||
|
||||
- **🔥 Fast** — 40+ tokens/second on M4 Pro with optimized Metal shaders
|
||||
- **🍎 Apple Silicon Native** — Uses Metal GPU, Apple Neural Engine (ANE), and ARM NEON
|
||||
- **🔒 Private** — Everything runs locally, your data never leaves your device
|
||||
- **📦 Easy** — One command to install, one line to run
|
||||
- **🌐 Cross-Platform** — Works in Rust, Node.js, and browsers via WebAssembly
|
||||
|
||||
---
|
||||
|
||||
## ✨ Key Features
|
||||
|
||||
### Core Capabilities
|
||||
|
||||
| Feature | Description |
|
||||
|---------|-------------|
|
||||
| **Multi-Backend Support** | Metal GPU, Core ML (ANE), CPU with NEON SIMD |
|
||||
| **Quantization** | Q4, Q5, Q8 quantized models (4-8x memory savings) |
|
||||
| **GGUF Support** | Load models directly from Hugging Face in GGUF format |
|
||||
| **Streaming** | Real-time token-by-token generation |
|
||||
| **Continuous Batching** | Efficient multi-request handling |
|
||||
| **KV Cache** | Optimized key-value cache with paged attention |
|
||||
| **Speculative Decoding** | 1.5-2x speedup with draft models |
|
||||
|
||||
### v2.0 New Features
|
||||
|
||||
| Feature | Improvement |
|
||||
|---------|-------------|
|
||||
| **Apple Neural Engine** | 38 TOPS dedicated ML acceleration on M4 Pro |
|
||||
| **Hybrid GPU+ANE Pipeline** | Best of both worlds for optimal throughput |
|
||||
| **Flash Attention v2** | 2.5-7.5x faster attention computation |
|
||||
| **SONA Learning** | Self-optimizing neural architecture for adaptive inference |
|
||||
| **Ruvector Integration** | Built-in vector embeddings for RAG applications |
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Quickstart
|
||||
|
||||
### Rust (Cargo)
|
||||
|
||||
```bash
|
||||
# Add to Cargo.toml
|
||||
cargo add ruvllm --features inference-metal
|
||||
```
|
||||
|
||||
```rust
|
||||
use ruvllm::{Engine, GenerateParams};
|
||||
|
||||
#[tokio::main]
|
||||
async fn main() -> Result<(), Box<dyn std::error::Error>> {
|
||||
// Load a model (downloads automatically from Hugging Face)
|
||||
let engine = Engine::from_pretrained("microsoft/Phi-3-mini-4k-instruct-gguf")?;
|
||||
|
||||
// Generate text
|
||||
let response = engine.generate(
|
||||
"Explain quantum computing in simple terms:",
|
||||
GenerateParams::default()
|
||||
)?;
|
||||
|
||||
println!("{}", response);
|
||||
Ok(())
|
||||
}
|
||||
```
|
||||
|
||||
### Node.js (npm)
|
||||
|
||||
```bash
|
||||
npm install @aspect/ruvllm
|
||||
```
|
||||
|
||||
```javascript
|
||||
import { RuvLLM } from '@aspect/ruvllm';
|
||||
|
||||
// Initialize with a model
|
||||
const llm = await RuvLLM.fromPretrained('microsoft/Phi-3-mini-4k-instruct-gguf');
|
||||
|
||||
// Generate text
|
||||
const response = await llm.generate('Explain quantum computing in simple terms:');
|
||||
console.log(response);
|
||||
|
||||
// Or stream tokens
|
||||
for await (const token of llm.stream('Write a haiku about coding:')) {
|
||||
process.stdout.write(token);
|
||||
}
|
||||
```
|
||||
|
||||
### CLI
|
||||
|
||||
```bash
|
||||
# Install CLI
|
||||
cargo install ruvllm-cli
|
||||
|
||||
# Run interactively
|
||||
ruvllm chat --model microsoft/Phi-3-mini-4k-instruct-gguf
|
||||
|
||||
# One-shot generation
|
||||
ruvllm generate "What is the meaning of life?" --model phi-3
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
<details>
|
||||
<summary><h2>📚 Tutorials</h2></summary>
|
||||
|
||||
### Tutorial 1: Building a Local Chatbot
|
||||
|
||||
Create a simple chatbot that runs entirely on your Mac:
|
||||
|
||||
```rust
|
||||
use ruvllm::{Engine, GenerateParams, ChatMessage};
|
||||
|
||||
fn main() -> Result<(), Box<dyn std::error::Error>> {
|
||||
let engine = Engine::from_pretrained("meta-llama/Llama-3.2-1B-Instruct-GGUF")?;
|
||||
|
||||
let mut history = vec![];
|
||||
|
||||
loop {
|
||||
print!("You: ");
|
||||
let mut input = String::new();
|
||||
std::io::stdin().read_line(&mut input)?;
|
||||
|
||||
history.push(ChatMessage::user(&input));
|
||||
|
||||
let response = engine.chat(&history, GenerateParams {
|
||||
max_tokens: 512,
|
||||
temperature: 0.7,
|
||||
..Default::default()
|
||||
})?;
|
||||
|
||||
println!("AI: {}", response);
|
||||
history.push(ChatMessage::assistant(&response));
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Tutorial 2: Streaming Responses in Node.js
|
||||
|
||||
Build a real-time streaming API:
|
||||
|
||||
```javascript
|
||||
import { RuvLLM } from '@aspect/ruvllm';
|
||||
import express from 'express';
|
||||
|
||||
const app = express();
|
||||
const llm = await RuvLLM.fromPretrained('phi-3-mini');
|
||||
|
||||
app.get('/stream', async (req, res) => {
|
||||
const prompt = req.query.prompt;
|
||||
|
||||
res.setHeader('Content-Type', 'text/event-stream');
|
||||
res.setHeader('Cache-Control', 'no-cache');
|
||||
|
||||
for await (const token of llm.stream(prompt)) {
|
||||
res.write(`data: ${JSON.stringify({ token })}\n\n`);
|
||||
}
|
||||
|
||||
res.write('data: [DONE]\n\n');
|
||||
res.end();
|
||||
});
|
||||
|
||||
app.listen(3000);
|
||||
```
|
||||
|
||||
### Tutorial 3: RAG with Ruvector
|
||||
|
||||
Combine RuvLLM with Ruvector for retrieval-augmented generation:
|
||||
|
||||
```rust
|
||||
use ruvllm::Engine;
|
||||
use ruvector_core::{VectorDb, HnswConfig};
|
||||
|
||||
fn main() -> Result<(), Box<dyn std::error::Error>> {
|
||||
// Initialize vector database
|
||||
let db = VectorDb::new(HnswConfig::default())?;
|
||||
|
||||
// Initialize LLM
|
||||
let llm = Engine::from_pretrained("phi-3-mini")?;
|
||||
|
||||
// Add documents (embeddings generated automatically)
|
||||
db.add_document("doc1", "RuvLLM is a fast LLM inference engine.")?;
|
||||
db.add_document("doc2", "It supports Metal GPU acceleration.")?;
|
||||
|
||||
// Query and generate
|
||||
let query = "What is RuvLLM?";
|
||||
let context = db.search(query, 3)?;
|
||||
|
||||
let prompt = format!(
|
||||
"Context:\n{}\n\nQuestion: {}\nAnswer:",
|
||||
context.iter().map(|d| d.text.as_str()).collect::<Vec<_>>().join("\n"),
|
||||
query
|
||||
);
|
||||
|
||||
let response = llm.generate(&prompt, Default::default())?;
|
||||
println!("{}", response);
|
||||
Ok(())
|
||||
}
|
||||
```
|
||||
|
||||
### Tutorial 4: Browser-Based Inference (WebAssembly)
|
||||
|
||||
Run models directly in the browser:
|
||||
|
||||
```html
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<script type="module">
|
||||
import init, { RuvLLM } from 'https://unpkg.com/@aspect/ruvllm-wasm/ruvllm.js';
|
||||
|
||||
async function main() {
|
||||
await init();
|
||||
|
||||
const llm = await RuvLLM.fromUrl('/models/phi-3-mini-q4.gguf');
|
||||
|
||||
const output = document.getElementById('output');
|
||||
|
||||
for await (const token of llm.stream('Write a poem about the web:')) {
|
||||
output.textContent += token;
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
</script>
|
||||
</head>
|
||||
<body>
|
||||
<pre id="output"></pre>
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
<details>
|
||||
<summary><h2>🔧 Advanced Usage</h2></summary>
|
||||
|
||||
### Custom Model Configuration
|
||||
|
||||
Fine-tune model loading for your specific hardware:
|
||||
|
||||
```rust
|
||||
use ruvllm::{Engine, ModelConfig, ComputeBackend, Quantization};
|
||||
|
||||
let engine = Engine::builder()
|
||||
.model_path("/path/to/model.gguf")
|
||||
.backend(ComputeBackend::Metal) // Use Metal GPU
|
||||
.quantization(Quantization::Q4K) // 4-bit quantization
|
||||
.context_length(8192) // Max context
|
||||
.num_gpu_layers(32) // Layers on GPU
|
||||
.use_flash_attention(true) // Enable Flash Attention
|
||||
.build()?;
|
||||
```
|
||||
|
||||
### Apple Neural Engine (ANE) Configuration
|
||||
|
||||
Leverage the dedicated ML accelerator on Apple Silicon:
|
||||
|
||||
```rust
|
||||
use ruvllm::{Engine, CoreMLBackend, ComputeUnits};
|
||||
|
||||
// Create Core ML backend with ANE
|
||||
let backend = CoreMLBackend::new()?
|
||||
.with_compute_units(ComputeUnits::CpuAndNeuralEngine) // Use ANE
|
||||
.with_tokenizer(tokenizer);
|
||||
|
||||
// Load Core ML model
|
||||
backend.load_model("model.mlmodelc", ModelConfig::default())?;
|
||||
|
||||
// Generate (uses ANE for MLP, GPU for attention)
|
||||
let response = backend.generate("Hello", GenerateParams::default())?;
|
||||
```
|
||||
|
||||
### Hybrid GPU + ANE Pipeline
|
||||
|
||||
Maximize throughput with intelligent workload distribution:
|
||||
|
||||
```rust
|
||||
use ruvllm::kernels::{should_use_ane_matmul, get_ane_recommendation};
|
||||
|
||||
// Check if ANE is beneficial for your matrix size
|
||||
let recommendation = get_ane_recommendation(batch_size, hidden_dim, vocab_size);
|
||||
|
||||
if recommendation.use_ane {
|
||||
println!("Using ANE: {} (confidence: {:.0}%)",
|
||||
recommendation.reason,
|
||||
recommendation.confidence * 100.0);
|
||||
}
|
||||
```
|
||||
|
||||
### Continuous Batching Server
|
||||
|
||||
Build a high-throughput inference server:
|
||||
|
||||
```rust
|
||||
use ruvllm::serving::{
|
||||
ContinuousBatchScheduler, KvCacheManager, InferenceRequest, SchedulerConfig
|
||||
};
|
||||
|
||||
let config = SchedulerConfig {
|
||||
max_batch_size: 32,
|
||||
max_tokens_per_batch: 4096,
|
||||
preemption_mode: PreemptionMode::Swap,
|
||||
..Default::default()
|
||||
};
|
||||
|
||||
let mut scheduler = ContinuousBatchScheduler::new(config);
|
||||
let mut kv_cache = KvCacheManager::new(KvCachePoolConfig::default());
|
||||
|
||||
// Add requests
|
||||
scheduler.add_request(InferenceRequest::new(tokens, params));
|
||||
|
||||
// Process batches
|
||||
while let Some(batch) = scheduler.schedule() {
|
||||
// Execute batch inference
|
||||
let outputs = engine.forward_batch(&batch)?;
|
||||
|
||||
// Update scheduler with results
|
||||
scheduler.update(outputs);
|
||||
}
|
||||
```
|
||||
|
||||
### Speculative Decoding
|
||||
|
||||
Speed up generation with draft models:
|
||||
|
||||
```rust
|
||||
use ruvllm::speculative::{SpeculativeDecoder, SpeculativeConfig};
|
||||
|
||||
let config = SpeculativeConfig {
|
||||
draft_model: "phi-3-mini-draft", // Small, fast model
|
||||
target_model: "phi-3-medium", // Large, accurate model
|
||||
num_speculative_tokens: 4, // Tokens to speculate
|
||||
temperature: 0.8,
|
||||
};
|
||||
|
||||
let decoder = SpeculativeDecoder::new(config)?;
|
||||
|
||||
// 1.5-2x faster than standard decoding
|
||||
let response = decoder.generate("Explain relativity:", params)?;
|
||||
```
|
||||
|
||||
### Custom Tokenizer
|
||||
|
||||
Use custom tokenizers for specialized models:
|
||||
|
||||
```rust
|
||||
use ruvllm::tokenizer::{RuvTokenizer, TokenizerConfig};
|
||||
|
||||
// Load from HuggingFace
|
||||
let tokenizer = RuvTokenizer::from_pretrained("meta-llama/Llama-3.2-1B")?;
|
||||
|
||||
// Or from local file
|
||||
let tokenizer = RuvTokenizer::from_file("./tokenizer.json")?;
|
||||
|
||||
// Encode/decode
|
||||
let tokens = tokenizer.encode("Hello, world!")?;
|
||||
let text = tokenizer.decode(&tokens)?;
|
||||
|
||||
// With chat template
|
||||
let formatted = tokenizer.apply_chat_template(&[
|
||||
ChatMessage::system("You are a helpful assistant."),
|
||||
ChatMessage::user("What is 2+2?"),
|
||||
])?;
|
||||
```
|
||||
|
||||
### Memory Optimization
|
||||
|
||||
Optimize for large models on limited memory:
|
||||
|
||||
```rust
|
||||
use ruvllm::{Engine, MemoryConfig};
|
||||
|
||||
let engine = Engine::builder()
|
||||
.model_path("llama-70b.gguf")
|
||||
.memory_config(MemoryConfig {
|
||||
max_memory_gb: 24.0, // Limit memory usage
|
||||
offload_to_cpu: true, // Offload layers to CPU
|
||||
use_mmap: true, // Memory-map model file
|
||||
kv_cache_dtype: DType::F16, // Half-precision KV cache
|
||||
})
|
||||
.build()?;
|
||||
```
|
||||
|
||||
### Embeddings for RAG
|
||||
|
||||
Generate embeddings for retrieval applications:
|
||||
|
||||
```rust
|
||||
use ruvllm::Engine;
|
||||
|
||||
let engine = Engine::from_pretrained("nomic-embed-text-v1.5")?;
|
||||
|
||||
// Single embedding
|
||||
let embedding = engine.embed("What is machine learning?")?;
|
||||
|
||||
// Batch embeddings
|
||||
let embeddings = engine.embed_batch(&[
|
||||
"Document 1 content",
|
||||
"Document 2 content",
|
||||
"Document 3 content",
|
||||
])?;
|
||||
|
||||
// Cosine similarity
|
||||
let similarity = ruvector_core::cosine_similarity(&embedding, &embeddings[0]);
|
||||
```
|
||||
|
||||
### Node.js Advanced Configuration
|
||||
|
||||
```javascript
|
||||
import { RuvLLM, ModelConfig, ComputeBackend } from '@aspect/ruvllm';
|
||||
|
||||
const llm = await RuvLLM.create({
|
||||
modelPath: './models/phi-3-mini-q4.gguf',
|
||||
backend: ComputeBackend.Metal,
|
||||
contextLength: 8192,
|
||||
numGpuLayers: 32,
|
||||
flashAttention: true,
|
||||
|
||||
// Callbacks
|
||||
onToken: (token) => process.stdout.write(token),
|
||||
onProgress: (progress) => console.log(`Loading: ${progress}%`),
|
||||
});
|
||||
|
||||
// Structured output (JSON mode)
|
||||
const result = await llm.generate('List 3 colors', {
|
||||
responseFormat: 'json',
|
||||
schema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
colors: { type: 'array', items: { type: 'string' } }
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
console.log(JSON.parse(result)); // { colors: ['red', 'blue', 'green'] }
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 📊 Performance Benchmarks
|
||||
|
||||
Tested on M4 Pro (14-core CPU, 20-core GPU, 38 TOPS ANE):
|
||||
|
||||
### Model Inference Speed
|
||||
|
||||
| Model | Size | Quantization | Tokens/sec | Memory |
|
||||
|-------|------|--------------|------------|--------|
|
||||
| Phi-3 Mini | 3.8B | Q4_K_M | 52 t/s | 2.4 GB |
|
||||
| Llama 3.2 | 1B | Q4_K_M | 78 t/s | 0.8 GB |
|
||||
| Llama 3.2 | 3B | Q4_K_M | 45 t/s | 2.1 GB |
|
||||
| Mistral 7B | 7B | Q4_K_M | 28 t/s | 4.2 GB |
|
||||
| Gemma 2 | 9B | Q4_K_M | 22 t/s | 5.8 GB |
|
||||
|
||||
### 🔥 ANE vs NEON Matrix Multiply (NEW in v2.0)
|
||||
|
||||
| Dimension | ANE | NEON | Speedup |
|
||||
|-----------|-----|------|---------|
|
||||
| 768×768 | 400 µs | 104 ms | **261x** |
|
||||
| 1024×1024 | 1.2 ms | 283 ms | **243x** |
|
||||
| 1536×1536 | 3.4 ms | 1,028 ms | **306x** |
|
||||
| 2048×2048 | 8.5 ms | 4,020 ms | **473x** |
|
||||
| 3072×3072 | 28.2 ms | 15,240 ms | **541x** |
|
||||
| 4096×4096 | 66.1 ms | 65,428 ms | **989x** |
|
||||
|
||||
### Hybrid Pipeline Performance
|
||||
|
||||
| Mode | seq=128 | seq=512 | vs NEON |
|
||||
|------|---------|---------|---------|
|
||||
| **Pure ANE** | 35.9 ms | 112.9 ms | **460x faster** |
|
||||
| Hybrid | 862 ms | 3,195 ms | 19x faster |
|
||||
| Pure NEON | 16,529 ms | 66,539 ms | baseline |
|
||||
|
||||
### Activation Functions (SiLU/GELU)
|
||||
|
||||
| Size | NEON | ANE | Winner |
|
||||
|------|------|-----|--------|
|
||||
| 32×4096 | 70 µs | 152 µs | NEON 2.2x |
|
||||
| 64×4096 | 141 µs | 303 µs | NEON 2.1x |
|
||||
| 128×4096 | 284 µs | 613 µs | NEON 2.2x |
|
||||
|
||||
**Auto-dispatch** correctly routes: ANE for matmul ≥768 dims, NEON for activations.
|
||||
|
||||
### Quantization Performance
|
||||
|
||||
| Dimension | Encode | Hamming Distance |
|
||||
|-----------|--------|------------------|
|
||||
| 128-dim | 0.1 µs | <0.1 µs |
|
||||
| 384-dim | 0.3 µs | <0.1 µs |
|
||||
| 768-dim | 0.5 µs | <0.1 µs |
|
||||
| 1536-dim | 1.0 µs | <0.1 µs |
|
||||
|
||||
*Benchmarks run with Criterion.rs, 50 samples per test, M4 Pro 48GB.*
|
||||
|
||||
---
|
||||
|
||||
## 🔌 Supported Models
|
||||
|
||||
RuvLLM supports any model in GGUF format. Popular options:
|
||||
|
||||
- **Llama 3.2** (1B, 3B) — Meta's latest efficient models
|
||||
- **Phi-3** (Mini, Small, Medium) — Microsoft's powerful small models
|
||||
- **Mistral 7B** — Excellent quality-to-size ratio
|
||||
- **Gemma 2** (2B, 9B, 27B) — Google's open models
|
||||
- **Qwen 2.5** (0.5B-72B) — Alibaba's multilingual models
|
||||
- **DeepSeek Coder** — Specialized for code generation
|
||||
|
||||
Download models from [Hugging Face](https://huggingface.co/models?library=gguf).
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Installation
|
||||
|
||||
### Rust
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
ruvllm = { version = "2.0", features = ["inference-metal"] }
|
||||
|
||||
# Or with all features
|
||||
ruvllm = { version = "2.0", features = ["inference-metal", "coreml", "speculative"] }
|
||||
```
|
||||
|
||||
Available features:
|
||||
- `inference-metal` — Metal GPU acceleration (recommended for Mac)
|
||||
- `inference-cuda` — CUDA acceleration (for NVIDIA GPUs)
|
||||
- `coreml` — Apple Neural Engine via Core ML
|
||||
- `speculative` — Speculative decoding support
|
||||
- `async-runtime` — Async/await support with Tokio
|
||||
|
||||
### Node.js
|
||||
|
||||
```bash
|
||||
npm install @aspect/ruvllm
|
||||
# or
|
||||
yarn add @aspect/ruvllm
|
||||
# or
|
||||
pnpm add @aspect/ruvllm
|
||||
```
|
||||
|
||||
### From Source
|
||||
|
||||
```bash
|
||||
git clone https://github.com/aspect/ruvector
|
||||
cd ruvector/crates/ruvllm
|
||||
cargo build --release --features inference-metal
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
|
||||
|
||||
- 🐛 [Report bugs](https://github.com/aspect/ruvector/issues/new?template=bug_report.md)
|
||||
- 💡 [Request features](https://github.com/aspect/ruvector/issues/new?template=feature_request.md)
|
||||
- 📖 [Improve docs](https://github.com/aspect/ruvector/tree/main/docs)
|
||||
|
||||
---
|
||||
|
||||
## 📄 License
|
||||
|
||||
RuvLLM is dual-licensed under MIT and Apache 2.0. See [LICENSE-MIT](LICENSE-MIT) and [LICENSE-APACHE](LICENSE-APACHE).
|
||||
|
||||
---
|
||||
|
||||
<p align="center">
|
||||
Made with ❤️ by <a href="https://ruv.io">ruv.io</a>
|
||||
<br/>
|
||||
<sub>Part of the <a href="https://github.com/aspect/ruvector">Ruvector</a> ecosystem</sub>
|
||||
</p>
|
||||
Reference in New Issue
Block a user