初始化项目，由ModelHub XC社区提供模型

Model: jiamingshan/AHA-L2A-Qwen3-1.7B-repro
Source: Original Platform

recipe/scripts/train_one.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+if [[ $# -lt 1 || $# -gt 2 ]]; then
+  echo "Usage: $0 aha|l2a_style [GPU_ID]" >&2
+  exit 2
+fi
+
+ARM="$1"
+GPU="${2:-0}"
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+RECIPE="$(cd "$SCRIPT_DIR/.." && pwd)"
+REPO="$(cd "$RECIPE/.." && pwd)"
+VANILLA_DIR="${VANILLA_DIR:-$REPO}"
+DATA_DIR="${DATA_DIR:-$RECIPE/data/am_distilled_long_mix}"
+OUTPUT_ROOT="${OUTPUT_ROOT:-$REPO/outputs}"
+
+case "$ARM" in
+  aha) GRANULARITY=token_kv_head ;;
+  l2a_style) GRANULARITY=token ;;
+  *) echo "Unknown arm: $ARM (expected aha or l2a_style)" >&2; exit 2 ;;
+esac
+
+ARM_OUT="$OUTPUT_ROOT/$ARM"
+HOTSTART="$ARM_OUT/hotstart"
+STAGE1="$ARM_OUT/stage1"
+STAGE2="$ARM_OUT/stage2"
+LOG_DIR="$ARM_OUT/logs"
+mkdir -p "$LOG_DIR"
+
+export CUDA_VISIBLE_DEVICES="$GPU"
+export PYTHONUNBUFFERED=1
+export TOKENIZERS_PARALLELISM=false
+export PYTORCH_CUDA_ALLOC_CONF="${PYTORCH_CUDA_ALLOC_CONF:-expandable_segments:True}"
+
+if [[ ! -f "$VANILLA_DIR/model.safetensors" ]]; then
+  echo "Missing tuned-vanilla checkpoint: $VANILLA_DIR/model.safetensors" >&2
+  exit 2
+fi
+
+if [[ ! -f "$HOTSTART/config.json" ]]; then
+  python "$SCRIPT_DIR/export_tuned_vanilla_aha_hotstart.py" \
+    --vanilla-path "$VANILLA_DIR" \
+    --output-path "$HOTSTART" \
+    --window-size 128 \
+    --local-kind sink_recent \
+    --gate-init-full-prob 0.90 \
+    --router-granularity "$GRANULARITY" \
+    2>&1 | tee "$LOG_DIR/hotstart.log"
+fi
+
+if [[ ! -f "$STAGE1/checkpoint-300/config.json" ]]; then
+  AHA_TRAIN_GATE_HARD_THRESHOLD=0.50 \
+  python "$RECIPE/dynamic_duo_train.py" \
+    --aha_checkpoint "$HOTSTART" \
+    --model_path "$VANILLA_DIR" \
+    --output_dir "$STAGE1" \
+    --data_source am_distilled \
+    --am_dataset_path "$DATA_DIR" \
+    --am_dataset_split train \
+    --am_label_mode full \
+    --max_length 8192 \
+    --num_steps 300 \
+    --warmup_ratio 0.10 \
+    --lr 3e-5 \
+    --reg_weight 0.1 \
+    --ce_weight 0.0 \
+    --batch_size 1 \
+    --grad_accum 1 \
+    --save_steps 100 \
+    --log_steps 10 \
+    --seed 42 \
+    --dtype bfloat16 \
+    --attn_impl sdpa \
+    --aha_local_kind sink_recent \
+    --router_granularity "$GRANULARITY" \
+    2>&1 | tee "$LOG_DIR/stage1.log"
+fi
+
+if [[ ! -f "$STAGE2/checkpoint-75/config.json" ]]; then
+  MODEL_PATH="$VANILLA_DIR" \
+  AHA_CHECKPOINT_PATH="$STAGE1/checkpoint-300" \
+  DATASET_PATH="$DATA_DIR" \
+  DATASET_SPLIT=train \
+  OUTPUT_DIR="$STAGE2" \
+  AHA_MODE=dynamic \
+  AHA_ROUTER_GRANULARITY="$GRANULARITY" \
+  AHA_LOCAL_KIND=sink_recent \
+  AHA_CE_WEIGHT=1.0 \
+  AHA_DISTILL_WEIGHT=0.5 \
+  AHA_REG_WEIGHT=0.01 \
+  AHA_TRAIN_GATE_HARD_THRESHOLD=0.58 \
+  GROUPED_LR=1 \
+  GATE_ONLY=0 \
+  LEARNING_RATE=3e-6 \
+  GATE_LEARNING_RATE=3e-6 \
+  BACKBONE_LEARNING_RATE=3e-7 \
+  LR_SCHEDULER_TYPE=constant_with_warmup \
+  WARMUP_RATIO=0.10 \
+  WEIGHT_DECAY=0.0 \
+  MAX_SEQ_LENGTH=8192 \
+  PER_DEVICE_TRAIN_BATCH_SIZE=1 \
+  GRADIENT_ACCUMULATION_STEPS=1 \
+  MAX_STEPS=75 \
+  LOGGING_STEPS=5 \
+  SAVE_STEPS=25 \
+  SAVE_TOTAL_LIMIT=3 \
+  FREEZE_EMBEDDINGS_LM_HEAD=1 \
+  REPORT_TO=none \
+  SEED=47 \
+  python "$RECIPE/sft.py" \
+    2>&1 | tee "$LOG_DIR/stage2.log"
+fi
+
+python - "$STAGE2/checkpoint-25" "$GRANULARITY" <<'PY'
+import json
+import sys
+from pathlib import Path
+
+checkpoint = Path(sys.argv[1])
+expected = sys.argv[2]
+config = json.loads((checkpoint / "config.json").read_text())
+actual = config.get("aha_router_granularity", "token_kv_head")
+if actual != expected:
+    raise RuntimeError(f"checkpoint granularity {actual!r} != {expected!r}")
+print(f"selected_checkpoint={checkpoint} router_granularity={actual}")
+PY