A server implementing the TypeSafe/Jev HTTP API with Qwen3.6-35B-A3B on SGLang.

Each container one B200 with SGLang 0.5.19's Rust frontend, radix caching, and breakable prefill CUDA graphs. A separate Python API process uses FastAPI, uvloop, the Rust-backed HF tokenizer, and pooled asynchronous HTTP connections to SGLang on localhost. CUDA dependencies stay in SGLang's container; uv sync on your laptop installs only the API, deployment tools, and tests.

uv sync
# Only if you haven't authenticated Modal on this machine:
uv run modal setup

# Start a temporary Server, run actual inference checks, then shut it down:
uv run modal run modal_app.py

# Deploy a stable public endpoint:
uv run modal deploy modal_app.py

The deployment prints a https://...us-west.modal.direct URL. It uses a Modal Server, unauthenticated=True, routing_region="us-west", and compute_region=["us-west", "us-central", "us"]. Autoscaling has no explicit container cap and scales to zero after five idle minutes. Set min_containers=1 in modal_app.py to keep a B200 warm. If SGLang exits unexpectedly, the API exits too. The Modal launcher watches the API and exits the container so Modal can replace it, rather than leaving a live HTTP process with a dead inference backend. Normal shutdown disarms both watchers.

Cache warmups also request one unused token probability to avoid SGLang's mixed-logprob batch crash. This keeps warmups and scoring requests batch-compatible without patching SGLang.

The first build imports a large SGLang image. The first GPU start also downloads weights and compiles/captures kernels. Model weights persist in the openjev-huggingface Modal Volume, alongside SGLang's tuning cache and Triton compilation cache. Later starts reuse these files; CUDA graph capture still runs at startup. The Rust frontend receives an explicit local tokenizer directory to avoid remote-name lookup issues with revision-pinned snapshots. A scaled-to-zero Server returns 503 while it starts; the included smoke command retries startup responses.

uv run openjev smoke https://YOUR-SERVER.us-west.modal.direct

The smoke test covers all three answer types, a 64-answer question, basic semantic sanity checks, and rejection of 65 answers. It reports startup wait, inference latency, and cache usage. modal run saves this report as smoke-result.json.

curl "$OPENJEV_URL/v1/systemone" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "jev-latest",
    "state": [
      {"role": "system", "content": "You are a support assistant."},
      {"role": "user", "content": "I was charged twice. Please refund the duplicate."}
    ],
    "questions": {
      "refund": {
        "type": "noul",
        "instructions": "Does the user request a refund?"
      },
      "department": {
        "type": "choice",
        "instructions": "Which department should handle this?",
        "criteria": {"billing": "Payments and refunds", "technical": "Software bugs"}
      },
      "urgency": {
        "type": "score",
        "instructions": "How urgent is the request?",
        "criteria": ["Routine", "Urgent", "Emergency"]
      }
    }
  }'

Or use curl "$OPENJEV_URL/v1/systemone" -H 'Content-Type: application/json' --data-binary @examples/request.json.

state, instructions, and criteria descriptions accept strings, JSON objects, or arrays. Criteria descriptions may also be null, in which case the option label is shown to the model. Structured criteria are serialized as compact JSON. A state that is a list of chat messages, or exactly {"messages": [...]}, is rendered using the model's native chat template. Original roles and message objects are retained; the classification question becomes an additional user turn, even after another user turn. Other structured state is serialized intact into a user message. Objects containing messages plus additional fields are kept intact so metadata isn't silently discarded. Chat state supports text, not image/audio/video content.

jev-latest is a compatibility alias for the configured Qwen model. The public model ID is Qwen/Qwen3.6-35B-A3B; NVIDIA's repository is only the internal weight source. Set OPENJEV_SERVED_MODEL_NAME or openjev serve --served-model-name NAME to override the public ID. It is also passed to SGLang as --served-model-name. No requests go to TypeSafe. The public server is the evaluation API; SGLang's generation and administration routes remain on localhost and aren't forwarded publicly.

1. Validate the schema, answer count, body size, context length, and total token budget.
2. Render the native chat template once, with thinking disabled. Split out a common prefix and independently tokenize each question suffix.
3. Send the common prefix to /generate with max_new_tokens=1, await completion, and discard the sampled token. This warms SGLang's radix cache.
4. Concurrently send prefix + question suffix + assistant header + "Answer:\n" for each question. Every call again has max_new_tokens=1. Request token_ids_logprob for every answer label and logprob_start_len=-1, so there is no need to recompute prompt logprobs. The sampled token itself is ignored.
5. Renormalize the requested label logprobs with stable softmax. Noul returns P(yes), Choice returns the argmax and full distribution, and Score returns sum(level_index * probability) with zero-based levels and a legend.

Options are rendered as A: description, B: description, etc., without JSON wrappers. Choice keys identify response fields and are hidden from the model, except when a description is null: then the option key supplies its meaning, matching Jev's nullable description schema.

This is a prefill plus first-token-readout workload: there is no generated chain of thought and no autoregressive continuation after the first token. There are N+1 one-token calls for N questions, including the cache-warming call. Speculative decoding is not enabled.

Qwen tokenizes 10 and 64 as multiple tokens. Answer labels are therefore A–Z, followed by verified single-token letter combinations (AA, AB, ...). All 64 labels are checked against the actual tokenizer at startup. Your original option names are preserved in the returned distribution. This keeps 64-way classification an exact one-token readout instead of comparing only the first digit of a multi-token number.

Radix reuse is opportunistic, not a pinned per-request KV session. Hybrid Qwen's recurrent state, cache page boundaries, cache pressure, and concurrent requests can reduce hits. The backend uses --mamba-radix-cache-strategy extra_buffer. x-openjev-prefix-tokens exposes the requested common prefix size. When SGLang reports cache counts, x-openjev-cached-tokens sums the branch cache hits. SGLang 0.5.19's Rust frontend omits these counts: the header is absent and smoke reports null, rather than a misleading zero. Scheduler logs still show actual cache hits (verified on the live B200 deployment). Server-Timing separates prompt preparation, the shared prefill, and branch inference.

usage.input_tokens sums SGLang's full prompt counts across the warm-up and all branches, including cached tokens. usage.output_tokens is N+1. These are backend usage counts, not TypeSafe billing estimates or unique tokens actually computed.

Defaults are 64 questions, 2–64 answers per Choice/Score, 2 MiB JSON, 32,768 tokens per branch including its output, 262,144 total submitted input tokens, 16 simultaneous evaluations, and 64 simultaneous backend calls. Invalid requests return 422 before inference; oversized bodies return 413; overload returns 529 with Retry-After. Backend timeouts return 504. Failed or cancelled evaluations cancel sibling requests and attempt to abort them in SGLang.

All settings can be provided as OPENJEV_* environment variables; see src/openjev/config.py. Common settings:

The Modal launch script forwards OPENJEV_PROFILE, OPENJEV_FRONTEND, and OPENJEV_SERVED_MODEL_NAME from the local environment. To customize other remote settings, add them to image.env(...) or use a Modal Secret for keys. The default Modal endpoint intentionally has no auth.

OpenJev defines confidence = 1 - H(probabilities) / log(number_of_options), clamped to [0, 1]. This is zero for a uniform distribution and one for a point mass. Probabilities are conditioned on the supplied options, depend on prompt and label ordering, and are not calibrated estimates of correctness.

uv sync
uv run pytest                         # offline unit + API tests
uv run pytest -m integration          # real tokenizer, small HF download, no GPU
uv run ruff check .
uv run openjev schema                 # no GPU or model download

# Connect to an existing backend; it must have matching model/tokenizer revision,
# selected-token logprobs, radix cache, and a sufficient context length:
uv run openjev serve --connect http://127.0.0.1:30000

# On a B200 host/container with SGLang 0.5.19 installed in another environment:
uv run openjev serve --sglang-python /path/to/sglang/bin/python