…AT-701 Bug B)

Implements `contracts/cuda-q4k-frozen-teacher-v1.yaml` (landed in PR #1863).

Before this fix, `apr distill --backend cuda` with a Q4K teacher (e.g. the
MODEL-1 teacher `paiml/qwen2.5-coder-7b-apache-q4k-v1`) was killed by the
Linux OOM killer at the first training step. Even with PR #1863's allocator
fix, the legacy `CudaTrainerTeacher` dequantizes Q4K weights to F32 at GPU
upload (4 GB on disk → 28 GB F32), and that inflation plus student grads +
Adam + activations exceeded the OOM threshold on Grace Blackwell GB10.

## What this PR adds

`crates/apr-cli/src/commands/distill_q4k_teacher.rs`:

* `RealizarQ4KTeacher` — wraps realizar's `OwnedQuantizedModelCuda` (the
  same inference-time path validated by `apr run`). Weights live on the
  GPU in their native Q4K format; forward GEMM uses Q4K-native CUDA
  kernels. No F32 dequantization at upload, no gradient/optimizer state.
* Implements `entrenar_distill::teacher_provider::TeacherLogitsProvider`:
  `logits_for_batch` delegates per-element to `cuda_model.forward_cuda`.

`crates/apr-cli/src/commands/distill.rs`:

* `run_cuda_backend` now inspects the teacher .apr's tensor dtype
  histogram. If any tensor is Q4K or Q6K, route to `RealizarQ4KTeacher`.
  F32/F16/BF16 teachers continue to use `CudaTrainerTeacher` (the dequant
  path is harmless for those types).

`Cargo.toml`:

* Adds `aprender-train-common` (`entrenar_common`) as an optional dep
  on apr-cli's `training` feature to surface `EntrenarError::Internal`
  in the new teacher impl.

## Verification on gx10 (Grace Blackwell GB10, sm_121)

Captured in `evidence/distill-7b-teacher-loadtest-gx10/launch-after-fix-b.log`:

* `[PMAT-701] Q4K/Q6K teacher detected → RealizarQ4KTeacher` — dispatch fires.
* `[PMAT-701] RealizarQ4KTeacher: pre-uploaded ... MB to GPU (Q4K-native, no F32 dequant)` — teacher staged.
* `✓ 24 transformer blocks uploaded to GPU` + `✓ GPU training state allocated (LM head: 544.5 MB)` — student loaded.
* `✓ Fused gradient clipping: 1506 partials (5.9 KB)` — training state ready.

Process then ran for 15 minutes of stable training at **~36 GB system
memory** (well under the 122 GB MemAvailable ceiling), terminated by the
test's `timeout 900` SIGTERM. **No OOM-kill, no `[PMAT-333] Dequantizing`
log, no `Killed` log.** Before this fix, the run SIGKILL'd within seconds
of `Fused gradient clipping` due to the F32 dequant memory pressure.

## Falsifier mapping (`cuda-q4k-frozen-teacher-v1.yaml`)

* FT-Q4K-TEACHER-001 PASS: no `[PMAT-333] Dequantizing` line in the log.
* FT-Q4K-TEACHER-002 partial: 36 GB total includes student F32 + grads + Adam;
  teacher contribution is dominated by Q4K blocks, not F32 inflation.
* FT-Q4K-TEACHER-005 partial: process completes >1 training step without
  OOM-kill. (Full 1-epoch completion deferred — teacher forward via realizar
  is slow enough that 31 steps exceeds the test's 15-minute timeout.
  Throughput tuning is separate work; the contract's "no OOM" invariant
  is satisfied.)

## Practical impact

Phase 4 distillation dispatch can now select the MODEL-1 7B teacher
(`paiml/qwen2.5-coder-7b-apache-q4k-v1`) on GB10 without the smoke-mode
TEACHER=STUDENT=0.5B workaround. Combined with PR #1863, the cuda distill
backend on Grace Blackwell now matches the practical expectations of
"128 GB unified memory means I can train with a real teacher."

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>