BUILD ON AVENIA

curl -N -X POST http://localhost:8788/v1/train \
  -H 'content-type: application/json' \
  -H 'accept: text/event-stream' \
  -H 'x-avenia-wallet: 0xYOURWALLET' \
  -d '{
    "model": "aven-qwen-0.5b",
    "dataset": [{"prompt": "hi", "completion": "hello there"}],
    "hyperparams": { "epochs": 3, "loraRank": 16 },
    "stream": true
  }'

{
  "jobId": "job_7c3",
  "model": "aven-qwen-0.5b",
  "backend": "Qwen/Qwen2.5-0.5B-Instruct",
  "datasetUrl": "http://localhost:8788/v1/dataset/ds_a1f",
  "datasetRows": 3,
  "hyperparams": {
    "epochs": 3, "loraRank": 16,
    "loraAlpha": 32, "learningRate": 0.0002,
    "batchSize": 2
  }
}

// The stream is a POST, so use fetch + a stream reader (not EventSource,
// which is GET-only). This is exactly what the live console does.
const res = await fetch("/v1/train", {
  method: "POST",
  headers: { "content-type": "application/json", accept: "text/event-stream" },
  body: JSON.stringify({
    model: "aven-qwen-0.5b",
    dataset: [{ prompt: "hi", completion: "hello there" }],
    hyperparams: { epochs: 3, loraRank: 16 },
    stream: true,
  }),
})

const reader = res.body.getReader()
const dec = new TextDecoder()
let buf = ""
for (;;) {
  const { value, done } = await reader.read()
  if (done) break
  buf += dec.decode(value, { stream: true })
  const blocks = buf.split("\n\n")
  buf = blocks.pop() ?? ""
  for (const block of blocks) {
    const line = block.split("\n").find((l) => l.startsWith("data:"))
    if (!line) continue
    const ev = JSON.parse(line.slice(5).trim()) // TrainEvent
    console.log(ev.type, ev)
  }
}

// For a GET SSE endpoint you would instead use:
//   const es = new EventSource(url); es.onmessage = (e) => JSON.parse(e.data)

import json, requests

with requests.post(
    "http://localhost:8788/v1/train",
    headers={"content-type": "application/json", "accept": "text/event-stream"},
    json={
        "model": "aven-qwen-0.5b",
        "dataset": [{"prompt": "hi", "completion": "hello there"}],
        "hyperparams": {"epochs": 3, "loraRank": 16},
        "stream": True,
    },
    stream=True,
) as r:
    for line in r.iter_lines():
        if line and line.startswith(b"data:"):
            ev = json.loads(line[5:].strip())  # TrainEvent
            print(ev["type"], ev)

# mock runtime - no GPU, deterministic loss curve (dev / CI default)
AVENIA_RUNTIME=mock npm run worker -- start --models aven-qwen-0.5b

# real GPU - install the trainer sidecar first, then serve
pip install -r packages/worker/trainer/requirements.txt
npm run worker -- start \
  --runtime python \
  --models aven-qwen-0.5b,aven-llama-1b,aven-llama-3b \
  --account acct_you \
  --label marketplace

# clone + install the workspace (coordinator, worker, cli, web)
git clone <repo> avenia && cd avenia && npm install

# boot the coordinator (HTTP + WS on :8788) with a mock worker
npm run dev

# point the web app at a hosted coordinator instead of localhost
COORDINATOR_HTTP_URL=https://coord.example.com npm run web