hanzo-edge (workspace)
+-- edge-core/              # Core inference runtime (library)
|   +-- lib.rs              # Public API: Model, InferenceSession, SamplingParams
|   +-- model.rs            # Model trait, GGUF loading, HF Hub download
|   +-- session.rs          # Autoregressive generation + streaming iterator
|   +-- sampling.rs         # Temperature, top-k, top-p, repeat penalty
|   +-- tokenizer.rs        # HF tokenizer wrapper with EOS detection
+-- edge-cli/               # CLI binary
|   +-- main.rs             # Clap-based CLI with 4 subcommands
|   +-- loader.rs           # HF Hub download with progress bars
|   +-- cmd/
|       +-- run.rs          # Streaming inference to stdout
|       +-- serve.rs        # Local HTTP server on /v1/* (Axum)
|       +-- bench.rs        # TTFT, throughput, memory benchmarking
|       +-- info.rs         # Model metadata inspection
+-- edge-wasm/              # WebAssembly module
    +-- lib.rs              # WASM bindings: EdgeModel, generate, generate_stream

# Install via curl
curl -sSL https://edge.hanzo.ai/install.sh | sh

# Or via cargo
cargo install hanzo-edge

# Run inference with streaming output
hanzo-edge run --model zenlm/zen3-nano --prompt "Hello!"

# Start the local API server on /v1/*
hanzo-edge serve --model zenlm/zen3-nano --port 8080

# Model info (architecture, params, quantization, context length)
hanzo-edge info --model zenlm/zen3-nano

# Benchmark (TTFT, tokens/sec, memory, averaged over N iterations)
hanzo-edge bench --model zenlm/zen3-nano --prompt "Hello" \
    --max-tokens 128 -n 5

# Run a Zen model
hanzo-edge run --model zenlm/zen3-nano --prompt "Write a haiku" \
    --max-tokens 128 --temperature 0.7 --top-p 0.9

hanzo-edge serve --model zenlm/zen3-nano --port 8080

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zen3-nano",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

use hanzo_edge_core::{load_model, InferenceSession, SamplingParams, ModelConfig};

// Load from HuggingFace Hub (auto-downloads and caches)
let config = ModelConfig {
    model_id: "zenlm/zen3-nano".to_string(),
    model_file: Some("zen3-nano.Q4_K_M.gguf".to_string()),
    ..Default::default()
};
let (mut model, tokenizer) = load_model(&config)?;

// Generate
let params = SamplingParams {
    temperature: 0.7,
    top_p: 0.9,
    top_k: 40,
    max_tokens: 256,
    repeat_penalty: 1.1,
    repeat_last_n: 64,
};
let mut session = InferenceSession::new(&mut *model, &tokenizer, params);
let output = session.generate("Explain quantum computing")?;
println!("{}", output.text);

// Streaming
let stream = session.generate_stream("Write a haiku")?;
for token_result in stream {
    print!("{}", token_result?);
}

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="local")

# Chat completions (streaming)
stream = client.chat.completions.create(
    model="zen3-nano",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

# Text completions
response = client.completions.create(
    model="zen3-nano",
    prompt="The quick brown fox",
    max_tokens=64
)
print(response.choices[0].text)

import init, { EdgeModel, get_version, get_device_info } from 'hanzo-edge-wasm';

await init();
console.log(`Hanzo Edge v${get_version()} [${get_device_info()}]`);

// Load model and tokenizer as ArrayBuffers
const modelBytes = await fetch('model.gguf').then(r => r.arrayBuffer());
const tokenizerBytes = await fetch('tokenizer.json').then(r => r.arrayBuffer());

const model = new EdgeModel(
    new Uint8Array(modelBytes),
    new Uint8Array(tokenizerBytes)
);

// Synchronous generation
const output = model.generate("Hello!", 256, 0.7);
console.log(output);

// Streaming (token-by-token callback)
model.generate_stream("Write a poem", 256, 0.7, (token) => {
    process.stdout.write(token);
});

// Reset KV cache between conversations
model.reset();

git clone https://github.com/hanzoai/edge
cd edge

# Build CLI (CPU)
cargo build --release -p hanzo-edge

# Build CLI (Metal, Apple Silicon)
cargo build --release -p hanzo-edge --features metal

# Build CLI (CUDA)
cargo build --release -p hanzo-edge --features cuda

# Build WASM
cd edge-wasm && cargo build --target wasm32-unknown-unknown --release
wasm-bindgen target/wasm32-unknown-unknown/release/edge_wasm.wasm \
    --out-dir pkg --target web

# Run tests
cargo test --workspace