scripts/serve_model.py — FastAPI in the CUDA container, loads base Qwen3.5-4B
+ a psyc adapter once and serves POST /infer. Lets the cockpit (no torch in
its venv) put a real fine-tuned model behind a Worker Mesh bot over HTTP.
Dockerfile.train gains a fastapi + uvicorn layer.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ioc_extraction ExampleBuilder now embeds every IOC into the advisory text so
the extraction task is answerable from the input (v1 asked the model to
"extract" a URL that was never given). /train page distinguishes trained /
training… / not-started, and renders a per-step loss bar chart. Dockerfile no
longer bakes the training script — scripts/ is mounted at run time so edits
take effect without a 21 GB rebuild (this is why psyc-v2's loss capture was
silently skipped on its first run).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Training and eval now run clean on the unsloth 2026.5.2 / transformers v5 /
torch 2.10 stack. Fixes: pytorch/pytorch base image (sidesteps the nvidia/cuda
apt-signature failure and the torch download), correct base-model slug
unsloth/Qwen3.5-4B, TRL SFTConfig API. Adds scripts/eval_adapter.py — runs
dataset rows through base+adapter with structured (transformers-v5) message
content and Qwen3.5 thinking-mode stripping.
First v1 adapter: loss 2.10 -> 0.32 over 3 epochs. Eval surfaced an ill-posed
ioc_extraction dataset (output URL not present in input) — to be fixed in the
ExampleBuilder before the next training run.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Dockerfile.train builds a CUDA 12.4 + unsloth container that consumes the
Trainline JSONL datasets and emits a LoRA adapter at data/adapters/<run>/final.
Defaults target a 24 GB GPU (Qwen3.5-4B-Instruct-bnb-4bit, r=16, bf16, 3 epochs,
effective batch 8). README documents the build + run workflow.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
