Deploy Kev on Modal

scripts/kev_serve.py is a single self-contained Modal app. modal deploy on it builds an image with the kev package at a pinned commit, loads a Kev checkpoint from the Hugging Face Hub on a GPU sized for it, and serves TypeSafe's System One protocol (POST /v1/systemone, GET /v1/models) at https://<workspace>--kev-api.modal.run. It scales to zero when idle. The file is short on purpose: if the user's case does not fit, read it and edit it.

1. Check the prerequisites

python3 -c "import modal" 2>/dev/null || pip install modal
modal profile current 2>/dev/null || modal setup      # opens a browser to sign in; the user must do this step
modal skills install -y                               # optional: Modal's own agent skill + docs (into .agents/, or -g for home)

If modal setup is needed, tell the user and wait; do not try to authenticate for them. modal skills install gives you Modal's official skill and documentation, which helps with anything beyond this file: GPUs, secrets, volumes, logs, billing.

2. Pick the model

Model time is the latency_ms the API returns (median of 20 requests, measured in the Kev repo: runs/serve-*, runs/grouping-4b-h100 and runs/fused-27b-*). A new state is the normal call, since every ticket is a new state; a repeated state is served from a prefix cache. The very first cold start of an account also downloads the weights and compiles kernels (1-2 minutes); both are cached on the kev-hf-cache volume afterwards. Other GPUs work with KEV_GPU but are worse picks: an L4 runs out of compute on Kev-4B, and an A100 is slower than an L40S here and costs more. Kev-27B is compute-bound under load: a B200 serves ~57 mixed requests/s at 64 concurrent clients (H200 ~40, H100 ~36) for about the same cost per request, with the lowest latency. Its first cold start downloads 55 GB of weights (several minutes).

A warm container costs the GPU's hourly rate only while it is up; after five idle minutes it scales to zero. KEV_MIN_CONTAINERS=1 keeps one warm (no cold starts, pays the hourly rate all the time). @revision pins a checkpoint revision (jaredpalmer/kev-4b@v7-base).

3. Deploy

Always set an API key unless the user explicitly wants a public URL; without one, anyone with the URL can spend their GPU time.

curl -LO https://raw.githubusercontent.com/jaredpalmer/kev/main/skills/kev-deploy/scripts/kev_serve.py   # or use the skill's copy
export KEV_API_KEY=$(openssl rand -hex 24)                # save it: it is the endpoint's bearer token
KEV_MODEL=jaredpalmer/kev-4b modal deploy kev_serve.py    # prints the URL

Settings are read at deploy time; redeploying with other values replaces the model behind the same URL. KEV_APP_NAME=kev-9b gives a second, independent endpoint (https://<workspace>--kev-9b-api.modal.run). KEV_GPU=H100 overrides the GPU list (comma-separated). KEV_REGION=us (or us-east, eu, ...) pins where the container runs: without it Modal takes the first region with a free GPU, which can be another continent (an unpinned Kev-4B landed in Frankfurt and added ~150 ms to every round trip from the US). A pinned region costs 1.15-1.75x on Modal; pin it near the callers for latency-sensitive use.

Throughput

A container answers concurrent requests in batches: its model thread takes everything waiting and runs it through shared passes. In-process, Kev-4B on an H100 serves about 95 six-question requests/s (120 on mixed short records); an L40S about 45. Over HTTP the front door matters more than the GPU (Kev-4B, H100, a client in the same region, measured 2026-09-24):

For steady high traffic, keep containers warm (KEV_MIN_CONTAINERS=2 or more) so bursts do not wait for a cold start, and size it at about 32 concurrent requests per container. KEV_FLASH=1 needs KEV_REGION (its proxy is regional) and its URL is printed as https://<workspace>--<app>-kev.<region>.modal.direct.

4. Verify

The first request after a deploy or an idle period waits for the cold start. Modal answers a request that waits longer than 150 s with an HTTP 303 to a result URL, so warm the endpoint with a redirect-following call first:

curl -sL --max-time 900 $KEV_URL/v1/models -H "authorization: Bearer $KEV_API_KEY"   # returns once the model is loaded

curl -s $KEV_URL/v1/systemone -H "authorization: Bearer $KEV_API_KEY" -H 'content-type: application/json' -d '{
  "state": "Order 4411 arrived late and the box was crushed. Two charges appear on my card.", "model": "kev-latest",
  "questions": {"team": {"type": "choice", "instructions": "Which team should handle this?",
                         "criteria": {"returns": "Exchanges, refunds", "shipping": "Delivery, delays", "billing": "Charges, payments"}},
                "urgent": {"type": "noul", "instructions": "Does this need urgent human attention?"}}}'
curl -s $KEV_URL/v1/models -H "authorization: Bearer $KEV_API_KEY"       # served checkpoint, base, temperature

Expect per-question probabilities (calibrated by the checkpoint's own temperature), choice / noul / score, and latency_ms, the model time: tens of milliseconds warm (table above). The round trip adds the network and Modal's proxy, about 80-100 ms from a client in the US to a us-east container over a kept-alive connection, more with a new TLS connection per request, so reuse one HTTP client. The first request of a new shape (question set, state length) runs without a CUDA graph, about 2-4x slower; the server captures one in the background and later requests use it. /v1/models reports the served checkpoint, its temperature and the number of captured graphs. A request without the key must return 401.

5. Wire it in

The protocol is TypeSafe's, so only the base URL and key change:

client = TypeSafeClient(api_key=KEV_API_KEY, base_url=KEV_URL, model="kev-latest")   # was: TypeSafeClient(api_key=TYPESAFE_KEY)

const r = await fetch(`${KEV_URL}/v1/systemone`, { method: "POST",
  headers: { "content-type": "application/json", authorization: `Bearer ${KEV_API_KEY}` },
  body: JSON.stringify({ state, model: "kev-latest", questions }) });   // question type "noul", not "boolean"

Kev answers typed questions about a state (Choice over named options, Noul yes/no, Score over ordered levels) without generating text. It was trained on public classification, policy and rule data; on the user's own domain, measure it on a few hundred labelled examples before relying on it, and if it falls short, fine-tune it with the kev-finetune skill.

6. Take it down

modal app stop kev            # or the KEV_APP_NAME used; the URL stops working immediately
modal volume delete kev-hf-cache   # optional: the cached weights (shared with kev-finetune; next deploy re-downloads)

Troubleshooting

• First request returns nothing or a 303: the cold start is still running (weights download on the very first start, or Modal is still finding a GPU; modal app logs kev says "waiting to be scheduled"). Follow redirects (curl -L), use a longer client timeout, or deploy with KEV_MIN_CONTAINERS=1.
• CUDA out of memory on start: the GPU is too small for that checkpoint (Kev-9B needs ~18 GB, Kev-4B ~10 GB); use the table above or KEV_GPU=H100.
• Slow round trips with fast latency_ms: the container is far from the caller or every request opens a new connection; set KEV_REGION and reuse the HTTP client.
• 401 with the right key: the key is fixed at deploy time; redeploy with the same KEV_API_KEY exported.
• Logs: modal app logs kev shows the load line (serving <model> on <GPU> ... ready in Ns) and every request.