# A local Jev backed by DiffusionGemma > Source: > Published: 2026-09-19 13:02:40+00:00 A local, Jev-compatible `POST /v1/systemone` API written in TypeScript for [Bun](https://bun.sh/), backed by DiffusionGemma through an OpenAI-compatible Chat Completions endpoint. The defaults target: - inference server: `http://127.0.0.1:8000` - model: `diffusiongemma-26B-A4B-it-4bit` - LocalJev API: `http://127.0.0.1:8080` [Jev](https://typesafe.ai/) uses a typed decision API rather than an OpenAI chat API. [OpenJev](https://github.com/razorback16/openjev) implements the Jev wire protocol and obtains probabilities with a special one-step DiffusionGemma **structured read**. Its backend depends on unmerged vLLM request extensions such as `diffusion_seed_canvas`, `diffusion_read_only`, and requested token logprobs. The normal oMLX API does not expose those primitives. LocalJev therefore takes the portable approach: 1. translate `state` and typed Jev questions into a classification prompt; 2. ask DiffusionGemma for a JSON probability scalar/vector; 3. validate the complete result and retry malformed output; 4. normalize vectors and calculate Jev-compatible choices, expected scores, and entropy-based confidence; 5. return the normal Jev response shape. This is wire-compatible, but not mathematically equivalent to OpenJev's logit read. The probabilities are generated/self-reported by the model rather than read directly from its logits. Evaluate their calibration on your own workload before relying on them for consequential decisions. Requires Bun 1.2+ and a running oMLX server. ``` bun install cp .env.example .env $EDITOR .env # replace the upstream API-key placeholder bun run start ``` Bun loads `.env` automatically. Alternatively, set the key in your shell before starting the server: ``` # fish set -gx LOCALJEV_UPSTREAM_API_KEY 'your-local-omlx-key' # bash/zsh export LOCALJEV_UPSTREAM_API_KEY='your-local-omlx-key' ``` LocalJev listens on `http://127.0.0.1:8080`. Check that the configured model is available: ``` curl http://127.0.0.1:8080/ready ``` Make a decision: ``` curl http://127.0.0.1:8080/v1/systemone \ -H 'Content-Type: application/json' \ -d '{ "model": "jev-latest", "state": "Hi, I have been trying to connect Stripe but keep getting a 403 error.", "questions": { "department": { "type": "choice", "instructions": "Which team should handle this?", "criteria": { "billing": "Payment or subscription issues", "technical": "Bugs or integration problems", "sales": "Pricing or account questions" } }, "frustration": { "type": "score", "instructions": "How frustrated does the customer appear?", "criteria": ["Calm", "Frustrated but civil", "Very angry"] }, "urgent": { "type": "noul", "instructions": "Does this require an immediate response?" } } }' ``` The SDK requires an API-key value. LocalJev accepts any value unless `LOCALJEV_API_KEY` is configured. Set the SDK environment for your shell: ``` # fish set -gx TYPESAFE_BASE_URL http://127.0.0.1:8080 set -gx TYPESAFE_API_KEY local # bash/zsh export TYPESAFE_BASE_URL=http://127.0.0.1:8080 export TYPESAFE_API_KEY=local python from typesafe_sdk import TypeSafeClient client = TypeSafeClient() response = client.system_one( "I was charged twice this month.", { "billing": { "type": "noul", "instructions": "Is this a billing issue?", } }, ) print(response.nouls["billing"].noul) ``` `jev-latest` and `jev-preview` are accepted aliases so SDK defaults work unchanged. | Variable | Default | Purpose | |---|---|---| | `LOCALJEV_UPSTREAM` | `http://127.0.0.1:8000` | OpenAI-compatible base URL, with or without `/v1` | | `LOCALJEV_UPSTREAM_API_KEY` | empty | Bearer key sent to the inference server | | `LOCALJEV_UPSTREAM_MODEL` | `diffusiongemma-26B-A4B-it-4bit` | Upstream model identifier | | `LOCALJEV_API_KEY` | empty | Optional Bearer key required from LocalJev clients | | `LOCALJEV_HOST` | `127.0.0.1` | Listen address | | `LOCALJEV_PORT` | `8080` | Listen port | | `LOCALJEV_TIMEOUT` | `180` | Upstream timeout in seconds | | `LOCALJEV_MAX_INFLIGHT` | `2` | Concurrent calls admitted upstream | | `LOCALJEV_MAX_QUEUE` | `64` | Waiting decisions before HTTP 529 | | `LOCALJEV_MALFORMED_RETRIES` | `2` | Corrective retries for invalid model JSON | | `LOCALJEV_MAX_OUTPUT_TOKENS` | `2048` | Per-completion output ceiling | | `LOCALJEV_QUESTIONS_PER_CALL` | `16` | Chunking limit per model call | | `LOCALJEV_OUTCOMES_PER_CALL` | `128` | Choice/score outcomes per model call | Bun automatically loads `.env`, so you can also copy `.env.example`, replace its placeholder, and run the server. ``` bun install bun test bun run typecheck bun run smoke # live call to the configured inference server ``` The repeatable bake-off uses public gold labels for news categorization (AG News), yes/no reading comprehension (BoolQ), and five-level sentiment (SST-5). It runs the same LocalJev engine against five installed models, comparing quality, calibration, retries, and full-decision latency at two actual input lengths. ``` # Quick integration check (30 requests, not a meaningful quality sample) bun run eval --out eval/runs/pilot --limit 3 # 5 models × 120 labeled examples × 2 input lengths = 1,200 requests bun run eval --out eval/runs/my-bakeoff # Regenerate a completed or partial report without running inference bun run eval:report eval/runs/my-bakeoff ``` Requires oMLX and the upstream key in `.env`; no running LocalJev HTTP server or Python is needed. See [the evaluation guide](https://github.com/githubnext/localjev/blob/main/docs/evaluation.md) for pinned data sources, methodology, configuration, resuming runs, and limitations. The [first completed bake-off](https://github.com/githubnext/localjev/blob/main/docs/evaluation-results-2026-09-18.md) includes 1,200 requests on an M5 Max. Gemma 4 26B-A4B and Qwen3.6 were the strongest overall candidates in this small screening sample; the report includes per-task results, latency, context effects, and caveats rather than claiming a definitive winner. Not currently for this model. As of September 18, 2026, DiffusionGemma support is still tracked as open in both [`lmstudio-ai/mlx-engine#336`](https://github.com/lmstudio-ai/mlx-engine/issues/336) and [`lmstudio-ai/lmstudio-bug-tracker#2037`](https://github.com/lmstudio-ai/lmstudio-bug-tracker/issues/2037). The reported MLX backend fails to load `diffusion_gemma`, while the normal llama.cpp backend reports an unknown architecture. oMLX already loads and serves your exact checkpoint successfully, so it is the better runner for this Mac today. Even after LM Studio adds ordinary generation support, changing runners alone will not make the result OpenJev-equivalent. The runner must expose seeded diffusion canvases, read-only denoising, and selected-token logits/logprobs. If LM Studio only provides standard Chat Completions, LocalJev can use it by changing `LOCALJEV_UPSTREAM`, but the probability path remains prompted/self-reported. For direct model probabilities, the best paths are: 1. add the structured-read primitives to oMLX's DiffusionGemma lane and consume them here; or 2. run OpenJev's patched vLLM backend on a supported NVIDIA machine.