Leonther/sentinel-qwen38-27b-q4

Sentinel — Qwen3.8-27B UD-Q4_K_M (System One decision model)

Open, self-hosted System One decision model: send it structured questions about a state and it returns calibrated probability distributions instead of generated text. Wire-compatible with TypeSafe's Jev /v1/systemone.

Benchmark (JevBench public panel, 231 decisions):

System	Accuracy	JevBench Score
Sentinel (this model, local)	87.9%	77.0
Jev (TypeSafe, hosted)	86.6%	75.4

Calibration: ECE 0.049 on hard tier at T=1.2 (fitted, see usage). One decision = one forward pass — output_tokens is always 0, nothing is ever sampled.

GitHub (server + agent skill + case study): https://github.com/clawdbot58-pixel/sentinel

What it is (and is not)

This GGUF is a decision engine, not a chat model. It is served behind a small FastAPI proxy that reads next-token logprobs over answer-option letters and returns probabilities. It never writes prose. Use it from code (routing, ranking, verification, game agents) — optionally with an LLM as the orchestrator that writes the questions.

	LLM	Sentinel
Output	prose to parse	typed probabilities, schema-valid
Failure	hallucination	visible uncertainty (confidence/abstain)
Latency	seconds	0.4–1.5s (one forward pass)

Setup

Hardware used for validation: one AMD Radeon AI PRO R9700 (32GB, RDNA4), llama.cpp Vulkan (coopmat2) build. Any llama.cpp-supported GPU/CPU with ≥18GB unified memory works for Q4_K_M; the readout only needs logprobs.

1. Get llama.cpp

Any recent build works. For RDNA4 the repo used the coopmat2 GCN4 flags:

git clone https://github.com/ggml-org/llama.cpp
cmake -B build-vk -DGGML_VULKAN=ON -DGGML_VULKAN_COOPMAT2_GCN4=ON -DLLAMA_CURL=OFF
cmake --build build-vk --config Release -j

2. Start the backend

./build-vk/bin/llama-server -m Qwen3.8-27B-UD-Q4_K_M.gguf \
  --port 8912 -ngl 999 -c 8192 --jinja

8k context covers decision prompts up to ~3.5k tokens comfortably (p95 ~3s; ~1000 tok/s prefill on the R9700).

3. Run the Sentinel proxy (the /v1/systemone API)

git clone https://github.com/clawdbot58-pixel/sentinel
pip install fastapi uvicorn transformers
python sentinel/server/sentinel_server.py
# → POST /v1/systemone on http://127.0.0.1:8915

The proxy (see sentinel/server/sentinel_server.py in the GitHub repo) builds the chat prompt, requests top-200 logprobs at the final position, and temperature-normalizes (T=1.2 — this is the JevBench-fitted calibration; keep it) over the option letters.

Usage

curl -s http://127.0.0.1:8915/v1/systemone \
  -H 'Content-Type: application/json' \
  -d '{
    "state": "Help! My payouts have been failing for 3 days.",
    "model": "sentinel",
    "questions": {
      "is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"}
    }
  }'

{
  "model": "sentinel-0.1-qwen38-27b",
  "answers": {"is_urgent": {"type": "noul", "noul": 0.94,
    "probabilities": {"no": 0.06, "yes": 0.94},
    "confidence": 0.88, "abstain": false}},
  "usage": {"input_tokens": 131, "output_tokens": 0}
}

The three question types (Jev wire format):

Type	Returns	Use
choice	choice + full probabilities over your options	one-of-N selection, ranking
noul	noul = P(yes)	conditions, gates, verification
score	probabilities over ordered levels 1..N	graded rating, comparable ranking

Responses add confidence (top1−top2 gap) and abstain (declines to commit below a safety floor) — route on them.

Agent skill

The repo ships a skill (skill/SKILL.md) that teaches Claude Code, pi, opencode, or any SKILL.md agent to use this API — the three primitives, the response contract, patterns (route / select / gate / rerank / fan-out), and operations. Install:

cp -r skill ~/.claude/skills/sentinel      # or ~/.pi/agent/skills/sentinel

Case study: LLM + Sentinel beat Snake (fill the map)

An autonomous agent (muse-spark-1.3 via opencode), using only the skill, built a Snake agent where Sentinel makes 100% of the moves and filled the entire 36-cell board (263 decisions, untuned seed):

Pattern: code computes per-move facts (death flags, BFS distances, free space, tail reachability), Sentinel ranks the moves each tick, an LLM wrote the instruction and iterated on telemetry. Full writeup in the repo (docs/case-study.md).

Notes

• Quantization: unsloth UD-Q4_K_M dynamic (imatrix-tuned). It beat Q5_K_XL, Q6_0_ROCMFPX and Q4_0_ROCMFP4 on the same benchmark — don't assume more bits help; the ladder was measured (see repo docs).
• License: MIT (model weights: Qwen3.8 series license via unsloth GGUF; skill adapted from typesafe-ai/skills, MIT).
• Trademarks: TypeSafe and Jev belong to TypeSafe AI; Sentinel is an independent, interoperable open implementation.