stage-3c: unsloth QLoRA training scaffold for Qwen3.5

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>

Dockerfile.train

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+# psyc training container — unsloth + Qwen3.5 QLoRA fine-tuning.
+#
+# Build:
+#   docker build -t psyc-trainer -f Dockerfile.train .
+#
+# Run (24 GB GPU, mounts host data/ for datasets + adapter output):
+#   docker run --gpus all --rm \
+#       -v $(pwd)/data:/data \
+#       psyc-trainer \
+#       --dataset /data/datasets/ioc_extraction-v1.jsonl \
+#       --dataset /data/datasets/severity_classification-v1.jsonl \
+#       --dataset /data/datasets/routing_decision-v1.jsonl \
+#       --dataset /data/datasets/tlp_assignment-v1.jsonl \
+#       --output /data/adapters/psyc-v1
+
+FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    HF_HOME=/data/.hf-cache
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        python3.11 python3.11-venv python3-pip \
+        git curl ca-certificates \
+    && rm -rf /var/lib/apt/lists/* \
+    && ln -sf /usr/bin/python3.11 /usr/local/bin/python \
+    && ln -sf /usr/bin/python3.11 /usr/local/bin/python3
+
+RUN python -m pip install --upgrade pip wheel setuptools && \
+    python -m pip install \
+        torch==2.5.1 \
+        --index-url https://download.pytorch.org/whl/cu124
+
+RUN python -m pip install \
+        "unsloth @ git+https://github.com/unslothai/unsloth.git" \
+        transformers>=4.46 \
+        datasets>=3.0 \
+        peft>=0.13 \
+        trl>=0.12 \
+        accelerate>=1.1 \
+        bitsandbytes>=0.44 \
+        sentencepiece \
+        protobuf
+
+WORKDIR /workspace
+COPY scripts/train_qlora.py /workspace/train_qlora.py
+
+ENTRYPOINT ["python", "/workspace/train_qlora.py"]