jevjam

Official registry Other

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

What can you do with jevjam?

  • One process, two doors. The HTTP API and MCP share one queue and one resident
  • Sleeps when idle. After JEVJAM_IDLE_TIMEOUT seconds (300 by default) every
  • Fits the card it finds. clef-flash loads in BF16, 8-bit, 4-bit, or split across
  • 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

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

OCI package ghcr.io/beremaran/jevjam:0.3.3
Claude Code
claude mcp add jevjam -e 'JEVJAM_API_KEY=<your jevjam_api_key>' -e 'JEVJAM_IDLE_TIMEOUT=300' -e 'HF_TOKEN=<your hf_token>' -- docker run -i --rm ghcr.io/beremaran/jevjam:0.3.3
JSON configuration (Claude Desktop, Cursor, VS Code)
{
    "mcpServers": {
        "jevjam": {
            "command": "docker",
            "args": [
                "run",
                "-i",
                "--rm",
                "ghcr.io/beremaran/jevjam:0.3.3"
            ],
            "env": {
                "JEVJAM_API_KEY": "<your jevjam_api_key>",
                "JEVJAM_IDLE_TIMEOUT": "300",
                "HF_TOKEN": "<your hf_token>"
            }
        }
    }
}

Environment variables

  • JEVJAM_API_KEY secret When set, /mcp and /v1/systemone need 'Authorization: Bearer <key>'
  • JEVJAM_IDLE_TIMEOUT Seconds without inference before the resident checkpoint is freed; 0 keeps it
  • HF_TOKEN secret Optional Hugging Face token for higher download rate limits

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. jevjam
  2. Models
  3. Quick start
  4. Features
  5. Docs
  6. Moving from laya-docker
  7. Contributing
  8. 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.

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:

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:

{
  "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:

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.

Frequently asked questions

What is jevjam?

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

How do I install jevjam?

It runs on your machine. In Claude Code: claude mcp add jevjam -e 'JEVJAM_API_KEY=<your jevjam_api_key>' -e 'JEVJAM_IDLE_TIMEOUT=300' -e 'HF_TOKEN=<your hf_token>' -- docker run -i --rm ghcr.io/beremaran/jevjam:0.3.3. For other clients, paste the JSON configuration from this page into their MCP settings.

Does jevjam need an API key?

Yes. It expects JEVJAM_API_KEY, HF_TOKEN, which you set in its configuration.

Is jevjam open source?

Yes. Its code is on GitHub under the Apache-2.0 license.

Who maintains jevjam?

It is published by beremaran. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.