# cyberprev-seat — hotdogs/Qwen3.8-27B-abliterated-cyber-preview, fv-ml1 GPU 0, :8025.
# Copy to .env on the host at /opt/docker/compose/cyberprev-seat/.env.

# ── Image ───────────────────────────────────────────────────────────────────
# Same pinned nightly mog-sec and sentinel-r3 run. Not :latest — pin it, so a seat
# restart cannot silently change the engine under a measured configuration.
CYBER_IMAGE=vllm/vllm-openai:nightly-e9d1398d9edfd90fcc1cf783805240e3effec013
API_KEY=replace-me

# ── Placement ───────────────────────────────────────────────────────────────
# GPU 0, co-resident with mog-sec. Took over :8025 from the retired sentinel-r3 seat.
CYBER_GPU_ID=0
CYBER_PORT=8025
CYBER_CONTAINER_NAME=vllm-cyberprev

# ── Model ───────────────────────────────────────────────────────────────────
# In-house mixed quant, compressed-tensors (NOT modelopt_fp4).
# ⚠ Quantized from /tank/aimodels/cyberprev-bf16-FIXED, not the raw download. The raw
# hotdogs export ships 850/1199 body tensors with a TRIPLED `language_model.` prefix
# (upstream bug) -> quant of the raw source is gibberish that passes every structural
# check. cyberprev-bf16-fixed is the name-repaired source (verified tensor-for-tensor
# against qwen38-27b-uncensored-bf16). See docs/pfi/model-quantization-playbook.md.
CYBER_MODEL=/tank/aimodels/cyberprev-nvfp4-mixed-v2
CYBER_QUANT=compressed-tensors
# ⚠ Its OWN name. Do not reuse `sentinel-r3` — that seat is retired and its gateway
# aliases are deliberately left to 404 rather than repointed at different weights.
CYBER_SERVED_NAME=cyberprev-27b
CYBER_SERVED_NAME_THINK=cyberprev-27b-thinking

# ── Memory ──────────────────────────────────────────────────────────────────
# 0.40 of the card, sharing GPU 0 with mog-sec. KV pinned in bytes (8 GiB) so the
# figure is reproducible regardless of what else is resident at start time.
CYBER_GPU_MEM_UTIL=0.40
CYBER_KV_CACHE_MEMORY=8589934592

# ⚠ 163840, NOT native 262144. Inherited from mog-sec, which crashed five times
# learning that the KV pool's capacity and the card's processing depth are different
# numbers. Raising this requires a deep-prefill probe with a NON-REPEATING prompt on
# THIS checkpoint — a repeated prompt hashes to cached blocks and never prefills deep.
CYBER_MAX_MODEL_LEN=163840
CYBER_MAX_NUM_SEQS=16
CYBER_MAX_NUM_BATCHED_TOKENS=4096
CYBER_KV_CACHE_DTYPE=fp8

# ── Speculative decoding ────────────────────────────────────────────────────
# One var, whole JSON — the two shapes are not interchangeable (dflash needs a
# "model", MTP must not have one).
#   DFlash2 : {"method": "dflash", "model": "/drafter", "num_speculative_tokens": 7}
#   MTP     : {"method": "qwen3_5_mtp", "num_speculative_tokens": 3}
# dflash k=7 is the default, carried from the sentinel measurement (2.40 vs 2.18).
# ⚠ Re-measure on THIS body before trusting it: cyberprev is ABLITERATED, and
# abliteration is precisely what can desync an MTP head. Gate MTP on a measured
# acceptance of >=~40%, never on an assumption.
CYBER_SPEC_CONFIG={"method": "dflash", "model": "/drafter", "num_speculative_tokens": 7}

CYBER_REASONING_PARSER=qwen3
CYBER_REASONING_EFFORT=medium
CYBER_ALLOC_CONF=