Set up Kahn1 on this machine and prove it works with one real request.

Kahn1 (https://github.com/Okura66/kahn1, MIT code) is an open-source decision engine: a fine-tuned open model that answers typed questions (choice, score, noul) about a text by reading the probabilities of option tokens. It comes in two sizes, and both run on a GPU (vLLM) or a CPU (transformers): Kahn1 4B (Okura66/Kahn1-Qwen3.5-4B, Apache 2.0 weights, 8.4 GB) and Kahn1 3B (Okura66/Kahn1-Qwen2.5-3B, weights under the Qwen Research License, a research licence: see its terms, 6.17 GB). The 3B is smaller and faster (median 36 ms against 89 ms on one GPU); the 4B is much stronger on hard judgment calls. Use the 4B unless I ask for the 3B. It is served by a FastAPI app, `sysone`. Guide: https://kahn1.com/get-started/

Rules: work step by step and show each command before you run it. Ask me before using sudo, installing system packages, or changing anything outside the project folder. If a step fails, show the exact error, explain the likely cause and propose a fix before going on. Never invent output: if the model could not run, say so.

1. Inspect the machine and tell me what you find: OS (Linux, macOS, Windows, WSL2), Python version (3.11+ needed), free RAM and disk space, and whether an NVIDIA GPU is usable (`nvidia-smi`). Then pick the backend and say why:
   - GPU (vLLM): only on Linux or WSL2 with an NVIDIA GPU and CUDA. Kahn1 4B needs 12 GB of VRAM or more (it ran on 16 GB), Kahn1 3B 8 GB or more.
   - CPU (transformers) otherwise: a few seconds per question. In float32, about 13 GB of free RAM for the 3B, about 17 GB for the 4B.
   The download is 8.4 GB for the 4B, 6.17 GB for the 3B. If the chosen size does not fit this machine, tell me before going on.

2. Install uv if it is missing (https://docs.astral.sh/uv/). If this folder is not already a clone of the repository, clone it:
   git clone https://github.com/Okura66/kahn1 && cd kahn1
   uv venv --python 3.11
   GPU: uv pip install -e ".[gpu]"
   CPU: uv pip install -e ".[cpu]" --extra-index-url https://download.pytorch.org/whl/cpu
   The model's tokenizer needs transformers 5 or newer. Check with
   uv run python -c "import transformers; print(transformers.__version__)"
   and run `uv pip install -U transformers` if it prints 4.x.

3. Start the server in the background and keep its log:
   GPU: SYSONE_MODEL=Okura66/Kahn1-Qwen3.5-4B uv run sysone serve --port 8000
   CPU: SYSONE_BACKEND=cpu SYSONE_MODEL=Okura66/Kahn1-Qwen3.5-4B uv run sysone serve --port 8000
   For the 3B, set SYSONE_MODEL=Okura66/Kahn1-Qwen2.5-3B instead, on either backend. The engine picks each model's prompt format by itself.
   On Windows PowerShell, set each variable first with $env:NAME = "value". Poll GET http://127.0.0.1:8000/health until it returns {"status": "ok"}.

4. POST this JSON to http://127.0.0.1:8000/v1/evaluate/jev (write it to a file and send it with curl -d @file, or use Python). The first call loads the model and can take minutes:
   {"state": "Hi, I was charged twice for order #48213. I want a refund, otherwise I will dispute the charge with my bank.",
    "schema": {
      "intent": {"type": "choice", "instructions": "What is the customer's main request?",
                 "criteria": {"refund": "the customer wants their money back", "delivery": "question about a delivery", "account": "cannot access their account"}},
      "urgency": {"type": "score", "instructions": "How urgent is this message?", "criteria": ["low", "medium", "high", "critical"]},
      "churn_risk": {"type": "noul", "instructions": "The customer threatens to leave the service or to escalate."}},
    "n_permutations": 1}
   Show me the JSON answer and check it: intent has a choice and probabilities that sum to 1, urgency has a level and a score, churn_risk is a number between 0 and 1. Report latency_ms, and send the same request a second time to show the warm latency.

5. Finish with a short summary: backend used, install path, how to stop and restart the server, and where to go next (https://kahn1.com/get-started/ for the API, the response format, tuning and calibration on my own data).