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docs/integration/hub_integration.md
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docs/integration/hub_integration.md
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# HuggingFace Hub Integration for RuvLTRA
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This document describes the HuggingFace Hub integration for publishing and downloading RuvLTRA models.
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## Overview
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The `ruvllm::hub` module provides comprehensive functionality for:
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1. **Model Download**: Pull GGUF files from HuggingFace Hub with progress tracking and resume support
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2. **Model Upload**: Push models to HuggingFace Hub with automatic model card generation
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3. **Model Registry**: Pre-configured RuvLTRA model collection with hardware requirements
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4. **Progress Tracking**: Visual progress bars with ETA and speed indicators
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5. **Checksum Verification**: SHA256 validation for downloaded files
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## Module Structure
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```
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crates/ruvllm/src/hub/
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├── mod.rs # Main module with exports and common types
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├── download.rs # Model download functionality
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├── upload.rs # Model upload functionality
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├── registry.rs # RuvLTRA model registry
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├── model_card.rs # HuggingFace model card generation
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└── progress.rs # Progress tracking utilities
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```
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## Model Registry
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The registry includes pre-configured RuvLTRA models:
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### Base Models
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| Model ID | Size | Params | Quantization | Use Case |
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|----------|------|--------|--------------|----------|
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| `ruvltra-small` | 662MB | 0.5B | Q4_K_M | Edge devices, includes SONA weights |
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| `ruvltra-small-q8` | 1.3GB | 0.5B | Q8_0 | High quality, small model |
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| `ruvltra-medium` | 2.1GB | 3B | Q4_K_M | General purpose, extended context |
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| `ruvltra-medium-q8` | 4.2GB | 3B | Q8_0 | High quality, balanced model |
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### LoRA Adapters
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| Adapter ID | Size | Base Model | Purpose |
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|------------|------|------------|---------|
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| `ruvltra-small-coder` | 50MB | ruvltra-small | Code completion specialization |
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## Usage
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### 1. Model Download
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#### Using the CLI Example
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```bash
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# Download RuvLTRA Small
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cargo run -p ruvllm --example hub_cli -- pull ruvltra-small
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# Download to custom directory
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cargo run -p ruvllm --example hub_cli -- pull ruvltra-medium --output ./models
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# List all available models
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cargo run -p ruvllm --example hub_cli -- list
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# Show detailed model info
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cargo run -p ruvllm --example hub_cli -- info ruvltra-small
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```
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#### Using the API
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```rust
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use ruvllm::hub::{ModelDownloader, RuvLtraRegistry};
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// Download by model ID
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let downloader = ModelDownloader::new();
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let path = downloader.download_by_id("ruvltra-small")?;
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// Or download with custom config
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let registry = RuvLtraRegistry::new();
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let model_info = registry.get("ruvltra-small").unwrap();
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let config = DownloadConfig {
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cache_dir: PathBuf::from("./models"),
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resume: true,
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show_progress: true,
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verify_checksum: true,
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..Default::default()
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};
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let downloader = ModelDownloader::with_config(config);
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let path = downloader.download(model_info, None)?;
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```
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### 2. Model Upload
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#### Using the CLI Example
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```bash
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# Upload a custom model (requires HF_TOKEN)
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export HF_TOKEN=your_huggingface_token
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cargo run -p ruvllm --example hub_cli -- push \
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--model ./my-ruvltra-custom.gguf \
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--repo username/my-ruvltra-custom \
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--description "My custom RuvLTRA model" \
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--params 0.5 \
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--architecture llama \
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--context 4096 \
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--quant Q4_K_M
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```
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#### Using the API
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```rust
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use ruvllm::hub::{ModelUploader, ModelMetadata, UploadConfig};
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// Create metadata
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let metadata = ModelMetadata {
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name: "My RuvLTRA Model".to_string(),
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description: Some("A custom RuvLTRA variant".to_string()),
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architecture: "llama".to_string(),
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params_b: 0.5,
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context_length: 4096,
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quantization: Some("Q4_K_M".to_string()),
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license: Some("MIT".to_string()),
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datasets: vec!["custom-dataset".to_string()],
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tags: vec!["ruvltra".to_string(), "custom".to_string()],
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};
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// Configure uploader
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let config = UploadConfig::new(hf_token)
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.private(false)
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.commit_message("Upload custom RuvLTRA model");
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let uploader = ModelUploader::with_config(config);
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let url = uploader.upload(
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"./my-model.gguf",
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"username/my-ruvltra-custom",
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Some(metadata),
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)?;
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println!("Model uploaded to: {}", url);
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```
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### 3. Model Registry
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```rust
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use ruvllm::hub::{RuvLtraRegistry, ModelSize};
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let registry = RuvLtraRegistry::new();
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// Get a specific model
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let model = registry.get("ruvltra-small").unwrap();
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println!("Model: {}", model.name);
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println!("Size: {} MB", model.size_bytes / (1024 * 1024));
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// List all models
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for model in registry.list_all() {
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println!("{}: {}", model.id, model.description);
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}
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// List by size category
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for model in registry.list_by_size(ModelSize::Small) {
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println!("Small model: {}", model.id);
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}
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// Get adapters for a base model
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for adapter in registry.list_adapters("ruvltra-small") {
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println!("Adapter: {}", adapter.id);
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}
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// Recommend model based on available RAM
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let model = registry.recommend_for_ram(4.0).unwrap();
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println!("Recommended for 4GB RAM: {}", model.id);
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```
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### 4. Model Card Generation
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```rust
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use ruvllm::hub::{
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ModelCardBuilder, TaskType, Framework, License
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};
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let card = ModelCardBuilder::new("RuvLTRA Custom")
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.description("A custom RuvLTRA variant")
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.task(TaskType::TextGeneration)
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.framework(Framework::Gguf)
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.architecture("llama")
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.parameters(500_000_000)
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.context_length(4096)
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.license(License::Mit)
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.add_dataset("training-data", Some("Custom dataset".to_string()))
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.add_metric("perplexity", 5.2, Some("test-set".to_string()))
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.add_tag("ruvltra")
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.add_tag("custom")
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.build();
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// Generate markdown for HuggingFace
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let markdown = card.to_markdown();
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```
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### 5. Progress Tracking
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```rust
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use ruvllm::hub::{ProgressBar, ProgressStyle};
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let mut pb = ProgressBar::new(total_bytes)
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.with_style(ProgressStyle::Detailed)
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.with_width(50);
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// Update progress
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pb.update(downloaded_bytes);
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// Finish
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pb.finish();
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```
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## Hardware Requirements
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Each model in the registry includes hardware requirements:
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```rust
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let model = registry.get("ruvltra-small").unwrap();
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println!("Minimum RAM: {:.1} GB", model.hardware.min_ram_gb);
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println!("Recommended RAM: {:.1} GB", model.hardware.recommended_ram_gb);
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println!("Apple Neural Engine: {}", model.hardware.supports_ane);
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println!("Metal GPU: {}", model.hardware.supports_metal);
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println!("CUDA: {}", model.hardware.supports_cuda);
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```
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## Environment Variables
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- `HF_TOKEN`: HuggingFace API token (required for uploads and private repos)
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- `HUGGING_FACE_HUB_TOKEN`: Alternative name for HF token
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- `RUVLLM_MODELS_DIR`: Default cache directory for downloaded models
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## Dependencies
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The hub integration requires:
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- `curl` or `wget` for downloads (uses system tools for efficiency)
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- `huggingface-cli` for uploads (install with `pip install huggingface_hub[cli]`)
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- SHA256 for checksum verification (built-in via `sha2` crate)
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## Features
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### Download Features
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- ✅ Resume interrupted downloads
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- ✅ Progress bar with ETA
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- ✅ SHA256 checksum verification
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- ✅ Automatic retry on failure
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- ✅ HuggingFace token authentication
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- ✅ Cache directory management
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### Upload Features
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- ✅ Automatic repository creation
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- ✅ Model card generation
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- ✅ Public/private repository support
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- ✅ SONA weights upload
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- ✅ Custom metadata
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- ✅ Commit message customization
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### Registry Features
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- ✅ Pre-configured model catalog
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- ✅ Hardware requirement tracking
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- ✅ Quantization level support
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- ✅ LoRA adapter registry
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- ✅ RAM-based recommendations
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- ✅ Download time estimation
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## Error Handling
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All hub operations return `Result<T, HubError>`:
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```rust
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use ruvllm::hub::{HubError, ModelDownloader};
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match downloader.download_by_id("ruvltra-small") {
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Ok(path) => println!("Downloaded to: {}", path.display()),
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Err(HubError::NotFound(id)) => eprintln!("Model {} not found", id),
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Err(HubError::ChecksumMismatch { expected, actual }) => {
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eprintln!("Checksum mismatch: expected {}, got {}", expected, actual);
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}
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Err(HubError::Network(msg)) => eprintln!("Network error: {}", msg),
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Err(e) => eprintln!("Error: {}", e),
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}
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```
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## Testing
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Run the hub integration tests:
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```bash
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# Test model registry
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cargo test -p ruvllm --lib hub::registry
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# Test download (requires network)
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cargo test -p ruvllm --lib hub::download
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# Test model card generation
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cargo test -p ruvllm --lib hub::model_card
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# Run all hub tests
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cargo test -p ruvllm --lib hub
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```
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## Examples
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See the examples for complete usage:
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1. `examples/download_test_model.rs` - Legacy downloader with hub integration
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2. `examples/hub_cli.rs` - Full CLI with pull/push/list/info commands
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## Future Enhancements
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Planned improvements:
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- [ ] Direct API uploads (without huggingface-cli dependency)
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- [ ] Parallel chunk downloads for faster transfers
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- [ ] Delta updates for model weights
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- [ ] Model versioning support
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- [ ] Automatic quantization variant selection
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- [ ] Multi-repo synchronization
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- [ ] Offline model registry cache
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## Contributing
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To add a new model to the registry:
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1. Add model definition to `registry.rs` in `RuvLtraRegistry::new()`
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2. Include hardware requirements
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3. Set checksum after first upload
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4. Update this documentation
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## License
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MIT License - See LICENSE file for details
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