MCP server: One MCP server.

jevjam

@beremaranMCP server · External

Typed decisions (choice, score, yes/no) from Laya, Julia-1 and clef-flash on your own GPU

Machine learningv0.3.3Apache-2.0Python
Freeno charge per call

Runs on your machine from its package; the steps are below.

jevjam: typed decisions from small models, as MCP tools and a Jev-compatible HTTP API
From the README

What it does

  1. Reads
  2. Writes
  3. High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

Dockerghcr.io/beremaran/jevjam:0.3.3

Run

docker run -i --rm ghcr.io/beremaran/jevjam:0.3.3

MCP client config

{
  "mcpServers": {
    "jevjam": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/beremaran/jevjam:0.3.3"
      ]
    }
  }
}
README3 min read

jevjam: typed decisions from small models, as MCP tools and a Jev-compatible HTTP API

CI Latest release Container image MCP License

jevjam

Self-hosted MCP server and Jev-compatible HTTP API for small decision models, on one GPU, in Docker.

Ask a model typed questions about a piece of text, JSON, an image or a video, and get calibrated answers back in milliseconds: pick a label (choice), rate on a scale (score), or answer yes or no (noul). Agents call it as MCP tools; services call POST /v1/systemone, the same protocol as TypeSafe Jev, so a Jev client only needs a new base URL.

Use it to route tickets, flag abuse, guard tool calls, pick a model for a prompt, or any other decision you would rather not spend a large LLM call on.

Models

ModelBySizeReadsPicked when
LayaConvai Innovations3 checkpoints, ~1.2B in alltext, JSONby default; its router picks English, multilingual or typed-decisions
Julia-1Supersonic Labs144Mtext, JSONthe request names julia-1
clef-flashCloudflare9Btext, JSON, images, videothe request names clef-flash

One checkpoint stays in VRAM at a time, and it is freed after five idle minutes. See docs/models.md for sizes, quantization and limits.

Quick start

You need Docker, an NVIDIA GPU, and the NVIDIA Container Toolkit.

bash
docker run -d --name jevjam --gpus all -p 127.0.0.1:8000:8000 \
  -v jevjam-models:/models ghcr.io/beremaran/jevjam:latest

Or, from a clone, docker compose up -d. No model downloads at boot; the first request fetches what it needs into the jevjam-models volume, which takes minutes once.

Ask over HTTP:

bash
curl -s http://127.0.0.1:8000/v1/systemone -H 'Content-Type: application/json' -d '{
  "state": "We were billed twice for March. Refund it today or we cancel.",
  "questions": {
    "department": {"type": "choice", "instructions": "Who should handle this?",
                   "criteria": {"billing": "payments, refunds", "technical": "bugs, outages"}},
    "churn_risk": {"type": "noul", "instructions": "Does the user threaten to leave?"}
  }
}'

The answer, trimmed:

json
{
  "answers": {
    "department": {"type": "choice", "choice": "billing", "probabilities": {"billing": 0.97, "technical": 0.03}, ...},
    "churn_risk": {"type": "noul", "noul": 0.82, ...}
  },
  "routing": {"model": "english", "reason": "English Latin text", ...}
}

Or connect an agent over MCP, at http://127.0.0.1:8000/mcp:

bash
claude mcp add --transport http jevjam http://127.0.0.1:8000/mcp   # Claude Code
codex mcp add jevjam --url http://127.0.0.1:8000/mcp                 # Codex

Agents get four tools: jevjam_predict, jevjam_preset (guard, moderation, triage, model_router), jevjam_route and jevjam_status. The MCP guide covers OpenCode, Pi, remote access and reverse proxies.

Features

  • One process, two doors. The HTTP API and MCP share one queue and one resident model, so neither starves the other of VRAM.
  • Sleeps when idle. After JEVJAM_IDLE_TIMEOUT seconds (300 by default) every checkpoint is freed and the GPU memory goes back to the driver. The next request loads only what it needs; Laya wakes in 0.6 s on an RTX 4070 Ti SUPER.
  • Fits the card it finds. clef-flash loads in BF16, 8-bit, 4-bit, or split across GPU and CPU, whichever fits.
  • Drop-in for Jev. Same request and response shapes; unknown fields are ignored.
  • Locked down by default. Runs as non-root, binds to loopback in Compose, and takes an optional bearer key (JEVJAM_API_KEY) for both endpoints.

Docs

GuideWhat is in it
ConfigurationRunning, settings, the model cache, sleeping on idle
MCP serverTools, auth, client setup, reverse proxies
HTTP API/health, /v1/systemone, question types, JSON Schema, errors
ModelsLaya, Julia-1 and clef-flash: sizes, VRAM, limits

Moving from laya-docker

This repo used to be laya-docker. The old image, ghcr.io/beremaran/laya-docker, gets no more updates; switch to ghcr.io/beremaran/jevjam. Old LAYA_* settings still work and log a warning; see Configuration.

Contributing

Bug reports and pull requests are welcome; see CONTRIBUTING.md. Report security problems privately, as SECURITY.md describes.

License

jevjam is licensed under Apache-2.0. The image also contains the Apache-2.0 Laya package and checkpoints by Convai Innovations, the Apache-2.0 Julia-1 code and checkpoint by Supersonic Labs, and the Apache-2.0 clef-flash code and checkpoint by Cloudflare. The clef-flash code is copied into src/jevjam/vendor/ with its license.

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