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spec(ship-two-models): v2.94.0 — §49 MODEL-2 strategy pivot: from-scratch → pretrained-init#1461

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spec(ship-two-models): v2.94.0 — §49 MODEL-2 strategy pivot: from-scratch → pretrained-init#1461
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@noahgift noahgift commented May 4, 2026

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Summary

  • Spec v2.93.0 → v2.94.0.
  • §49 amendment pivots MODEL-2 strategy from "370M from-scratch on 565M tokens" (math doesn't work) to "pretrained 0.5B-class fine-tuned on 565M tokens" (industry standard).
  • Live evidence: 500-step run hits val_loss=9.7255, ~identical to §24's 80K-step val_loss=9.7507. Ceiling is corpus-bound, not step-bound.
  • Pre-conditions verified: Qwen2.5-Coder-0.5B-Instruct in HF cache, apr convert works (290 tensors → 942 MiB APR file).

Why now

Operator asked "why aren't we training models?" after 11 SHIP-007 cascade PRs without ship-% movement. Re-diagnosed MODEL-2: §34's "capacity-limited" framing is wrong — it's data-limited. SmolLM-360M (similar params) needed 1T tokens to hit val_loss ~2.9; MODEL-2 saw 565M (~1800× less). From-scratch math doesn't reach val_loss=3.0 at this corpus scale.

Industry precedent

Model Init
Qwen2.5-Coder-0.5B ← Qwen2.5 base
StableCode-3B ← StableLM
DeepSeek-Coder-1.3B ← DeepSeek-LLM
StarCoder2-3B from-scratch on 3.3T tokens
SmolLM-360M from-scratch on 1T tokens

Nobody trains 0.5B from scratch for production code-LMs at <1T tokens because the math doesn't work.

Implementation gap (next PR)

apr pretrain lacks --init <model.apr> flag. §49.6 step 4 calls out ~50 LOC fix. Not in this commit.

Test plan

  • Live evidence captured: 500-step from-scratch ≈ 80K-step from-scratch
  • Qwen2.5-Coder-0.5B-Instruct converted to APR (5.1 sec, 290 tensors)
  • Pre-conditions documented in evidence/model-2-strategy-pivot-2026-05-04/findings.md
  • CI green
  • Auto-merge

Plain ship %

  • MODEL-2: 57% (unchanged) — stays here until first fine-tune produces val_loss < 9.38 evidence
  • MODEL-1: 91% (unchanged)

🤖 Generated with Claude Code

…tch → pretrained-init

After operator asked "why aren't we training models?" mid-session — re-diagnosed MODEL-2 from spec §34's "capacity-limited at val_loss=9.38" framing to the empirically-correct "data-limited" diagnosis. Pivots strategy from "MODEL-2 = 370M from-scratch" to "MODEL-2 = pretrained 0.5B-class fine-tuned" (industry standard for production small code-LMs).

## Live evidence (this session, 2026-05-04)

500-step `apr pretrain --mode from-scratch --device cuda` smoke on RTX 4090:
```
Run Result: OK CONVERGED  final val_loss=9.7255 after 5 epoch(s)
```

Vs §24 memory's 80K-step run on the same 4× corpus: val_loss=9.7507. **Within 0.026 across 160× difference in step count.** Ceiling is corpus-bound, not step-bound.

## Why "from-scratch" is the methodology defect

SmolLM-360M (similar 360M param count) hits val_loss ~2.9 — but trained on **1T tokens**. MODEL-2 saw 565M, ~1800× less. The 370M-from-scratch math doesn't reach val_loss=3.0 at this corpus scale.

Industry: StableCode← StableLM, Qwen2.5-Coder ← Qwen2.5, DeepSeek-Coder ← DeepSeek-LLM. **Nobody trains 0.5B from scratch for production code-LMs.**

## Pre-conditions verified

- ✅ Qwen2.5-Coder-0.5B-Instruct in HF cache (950 MB, model.safetensors + config + tokenizer)
- ✅ `apr convert <safetensors> --quantize fp16` → APR file: 290 tensors, 942 MiB, 5.1 sec
- ✅ APR file at `/mnt/nvme-raid0/models/qwen2.5-coder-0.5b-instruct-fp16.apr`
- ✅ RTX 4090 + cuBLAS + custom PTX backward (sm_89, no Blackwell JIT bug)
- ✅ codeparrot+CSN-Python tokenized corpus (565M tokens)

## Implementation gap (next PR)

`apr pretrain` lacks `--init <model.apr>` flag. §49.6 step 4 calls out the ~50 LOC fix. Not in this commit.

## Net effects

- Spec v2.93.0 → **v2.94.0**.
- §34's "capacity-limited" framing RETIRED in favor of §49.1's data-limited diagnosis.
- §36.2's "distillation is the only path" REFINED — pretrained-init is load-bearing; distillation is multiplicative on top.
- **MODEL-2 ship %**: stays **57%** until first fine-tune produces val_loss < 9.38 evidence.
- **MODEL-1 ship %**: unchanged at 91%.

## Five whys (paraphrased from §49.5)

1. **Why now?** Operator pivot. Cascade was real but didn't move ship %.
2. **Why is "from-scratch" wrong?** Math: 370M × 565M tokens ≠ SmolLM territory.
3. **Why Qwen2.5-Coder-0.5B-Instruct?** Already cached, code-pretrained, similar params, permissive license.
4. **Why retain target val_loss=3.0?** Right product target (~exp(3) = ~20 perplexity = good code completion).
5. **Why isn't this just a rename?** Different init = qualitatively different artifact (good code completion vs near-random tokens).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
@noahgift noahgift enabled auto-merge (squash) May 4, 2026 07:58
@noahgift noahgift merged commit ff689bd into main May 4, 2026
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@noahgift noahgift deleted the spec/v2-94-model-2-pretrained-init-pivot branch May 4, 2026 08:22
noahgift added a commit that referenced this pull request May 4, 2026
…MODEL-2 pivot (#1470)

Authors `contracts/apr-pretrain-from-init-v1.yaml` v1.0.0 PROPOSED, the
contract layer driving §49 step 4 (the wire-up PR for `apr pretrain
--init <model.apr>`).

§49 (spec v2.94.0, 2026-05-04) retired the from-scratch MODEL-2
strategy after 80K-step LR-budget falsification (§24.8) confirmed
the corpus-bottleneck floor at val_loss=9.75. The pivot is to
fine-tune a Qwen2.5-class pretrained checkpoint that has already
paid the 1T-token data tax. This contract pins the new flag's
semantics:

  - flag absent → existing pretrain behavior (no regression on §24/§25)
  - flag present → load weights from APR file, train with that init
  - flag composes with --mode {finetune, from-scratch}
  - missing/corrupted/shape-mismatched APR → fail-fast non-zero exit
  - load-bearing claim: init_loss(step=0) ≤ 6.0 < from_scratch_loss(step=0)
  - drift-prevention: clap field + unit test + integration test in same PR

10 falsifiers (FALSIFY-APR-PRETRAIN-INIT-001..010), 6 proof obligations,
2 kani harnesses. pv validate exits 0 with 0 errors / 0 warnings.

The contract is INDEPENDENT of the SHIP-007 / Qwen2-0.5B `apr run`
gibberish defect (memory entry 2026-05-04). Training forward path uses
a different loader and dispatch surface than `apr run` inference; the
init flag's correctness is provable independently. If the gibberish
defect also infects the training forward path, FALSIFY-APR-PRETRAIN-
INIT-006 (init_loss <= 6.0) will catch it as a side effect.

Five Whys:
  1. Why a contract before the impl? `pv validate` rejects schema
     drift; the falsifier list pins what the wire-up PR must satisfy.
     Contract-first prevents the wire-up shipping with silent gaps.
  2. Why now? Spec §49 was authored 2026-05-04 (PR #1461) and roadmap
     step 3 is "author this contract". Step 4 (wire-up) blocks on it.
  3. Why scope to PROPOSED? No impl exists yet. Promotion to
     ACTIVE/FUNCTIONAL/DISCHARGED gated on the wire-up PR landing
     and the 10 falsifiers compile-binding (PARTIAL_ALGORITHM_LEVEL).
  4. Why 10 falsifiers? Each error class is a distinct silent-failure
     mode. The §24 retrospective showed `--synthetic` defaulted
     silently to `true` for months — a contract pinning "no silent
     fallback" must enumerate ALL silent paths so CI catches each.
  5. Why not block on the SHIP-007 / 0.5B gibberish? The two are
     orthogonal. SHIP-007 is `apr run` inference; this contract is
     `apr pretrain` training-forward. Separating keeps the wire-up
     PR small and lets §49 progress while SHIP-007 stays under
     operator-gated investigation.

Plain ship-% update:
  - MODEL-1: unchanged at 91% (SHIP-007 cascade infrastructure track)
  - MODEL-2: unchanged at 57% — first ship-% movement gated on §49
    step 5 (LIVE 500-step fine-tune producing val_loss < 9.38)

Refs:
  - SPEC-SHIP-TWO-001 §49 — MODEL-2 strategy pivot (#1461)
  - contracts/training-loop-pretrain-v1.yaml v1.5.0 ACTIVE (parent)
  - feedback_cli_subcommand_three_surface_drift.md
  - feedback_no_guessing.md
  - memory:project_qwen2_0_5b_is_ship_007_manifestation.md (orthogonal)

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
noahgift added a commit that referenced this pull request May 4, 2026
Implements `apr pretrain --init <PATH>` per the contract authored in
PR #1470 (`apr-pretrain-from-init-v1` v1.0.0 PROPOSED). This is §49
step 4 of the MODEL-2 pretrained-init pivot.

Spec §49 (v2.94.0, 2026-05-04, PR #1461) retired the from-scratch
MODEL-2 strategy after §24.8's 80K-step LR-budget falsification
confirmed val_loss=9.75 as a corpus-bottleneck floor on the 565M-token
corpus. Fine-tuning a Qwen2.5-class pretrained checkpoint (which has
already paid the 1T-token data tax) is the load-bearing path. This PR
adds the flag that loads weights from an APR file as the initial
weights for the pretrain optimizer.

What this PR adds:

  1. Clap field `init: Option<PathBuf>` on the `Pretrain` variant
     in `extended_commands.rs:635`. Optional — absence preserves
     existing pretrain behavior (no §24/§25 regression).
  2. Plumbing through `dispatch_analysis.rs:346` to
     `commands::pretrain::run` (new `init: Option<&Path>` param).
  3. New helper `validate_init_apr_path()` in `pretrain.rs`:
       a. Open file → FALSIFY-003 (missing → exit non-zero)
       b. Read 4 magic bytes → FALSIFY-004 (read fails → exit non-zero)
       c. Compare magic bytes vs APR\\0 / APRN → FALSIFY-004
       d. If valid magic → return "not yet wired" error pointing at
          §49 step 5 (no silent random-init fallback)
  4. 7 new unit tests in `pretrain::tests`:
       - pretrain_init_flag_absent_parses_to_none (FALSIFY-001/002)
       - pretrain_init_flag_parses_path           (FALSIFY-001)
       - pretrain_init_missing_file_errors        (FALSIFY-003)
       - pretrain_init_bad_magic_errors           (FALSIFY-004)
       - pretrain_init_empty_file_errors          (FALSIFY-004 edge)
       - pretrain_init_valid_apr_rejected_until_step5 (partial-state guard)
       - pretrain_init_v1_magic_aprn_recognised   (v1 magic acceptance)
  5. Contract status bump: PROPOSED → PARTIAL_ALGORITHM_LEVEL via
     v1.0.0 → v1.1.0 metadata update + changelog entry.

Test results (cargo test -p apr-cli --lib commands::pretrain::):
    21 passed; 0 failed; 0 ignored

Step 5 follow-up scope (~150 LOC):

  - Architecture matching: read APR header, compare vocab/hidden/
    layers/heads against pretrain target → discharges FALSIFY-005
  - Actual weight load: read tensor shards, materialize into
    optimizer's initial state → discharges FALSIFY-006/009/010
  - LIVE 500-step fine-tune on Qwen2.5-Coder-0.5B-Instruct.apr →
    DISCHARGED (val_loss < 9.38)

Five Whys:

  1. Why a small partial-state PR instead of full step 4+5? §49
     step 4 was scoped at ~50 LOC for "wire the flag"; step 5
     does the full weight load. Splitting keeps each PR small,
     reviewable, and lets CI catch silent-fallback regressions
     between the two steps.

  2. Why have validate_init_apr_path() reject EVERY valid APR right
     now? Honors the contract's no-silent-fallback invariant. If we
     accepted valid APRs and silently used random init while step 5
     is open, an operator could ship a "fine-tune" run that's
     actually a from-scratch run — exactly the §24 silent-default
     defect class this PR is built to prevent.

  3. Why a custom error message naming "§49 step 5" instead of just
     "not implemented"? Operators tracing a failure to the source
     PR can find the next-step contract obligations by grep'ing the
     spec; "not implemented" gives them no thread to pull. The
     error message IS the breadcrumb to the next-cycle work.

  4. Why bump the contract from PROPOSED to PARTIAL_ALGORITHM_LEVEL
     in the same PR? Atomicity: the contract describes the flag's
     algorithm at the LEVEL we have impl evidence for. PROPOSED
     means "no impl"; PARTIAL means "compile-bound + algorithm-bound
     at sub-falsifier granularity". This PR delivers exactly that.
     Leaving the contract at PROPOSED while the impl is on main
     creates a drift between status and reality.

  5. Why not implement step 5 in the same PR? The MappedAprModel
     architecture extraction is a deeper plumbing question (header
     reading, GGUF qtype decoding, optimizer state initialization)
     that warrants its own commit + review. Going small + atomic
     is the Toyota Way single-piece flow.

Plain ship-% update:

  - MODEL-1: unchanged at 91% (SHIP-007 cascade infrastructure track)
  - MODEL-2: unchanged at 57% — first ship-% movement gated on §49
    step 5 weight-load impl + LIVE 500-step fine-tune (FALSIFY-006)

Refs:

  - SPEC-SHIP-TWO-001 §49 — MODEL-2 strategy pivot (#1461)
  - contracts/apr-pretrain-from-init-v1.yaml v1.0.0 → v1.1.0
  - PR #1470 — contract authoring (merged)
  - feedback_cli_subcommand_three_surface_drift.md (3-surface rule)
  - feedback_no_guessing.md
  - memory:project_qwen2_0_5b_is_ship_007_manifestation.md (orthogonal)

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
noahgift added a commit that referenced this pull request May 4, 2026
…oupling finding (#1472)

Adds §50 documenting the architecture-mismatch finding caught after §49.6
steps 3+4 landed (PR #1470 contract + PR #1471 wire-up). The remaining
§49.6 step 5 was scoped at "0 LOC, just run apr pretrain --init" — that
assumption is empirically wrong.

Empirical finding (§50.1):
  pretrain_real.rs:38-46 HARDCODES Llama370MConfig::* for every
  architectural constant. Qwen2.5-Coder-0.5B-Instruct has different
  shape across the board:

    Param            | Llama370M | Qwen2.5-Coder-0.5B
    -----------------|-----------|--------------------
    hidden_size      | 1024      | 896
    num_attention_heads | 16     | 14
    num_kv_heads     | 4 (GQA-4:1) | 2 (GQA-7:1)
    intermediate_size | 2816    | 4864
    vocab_size       | 50_257    | 151_936
    rope_theta       | 10_000    | 1_000_000

  Every tensor mismatches. Loading Qwen2.5 weights into a Llama370M-
  shaped optimizer is a category error.

Three options surfaced (§50.3):
  A: Find/build a Llama-shaped 0.5B pretrained checkpoint
     (~5K LOC + multi-week training; recreates §24/§25 corpus problem)
  B: Make trainer architecture-polymorphic
     (~200-400 LOC; preserves §24/§25 falsification; recommended)
  C: Replace Llama370MConfig with Qwen2_5_Coder_0_5B_Config outright
     (~300 LOC; deletes a working falsification path)

Recommendation (§50.5): Option B — preserves §24/§25 falsification
evidence, exercises TransformerConfig's designed polymorphism, binds
each new component (qwen2_0_5b constructor, GQA-7:1 attention, Qwen
tokenizer surface) to its own falsifier.

Re-scoped roadmap (§50.4) — 8 sub-steps replacing original step 5:
  5a. Author apr-pretrain-arch-polymorphic-v1.yaml contract  (~80 LOC)
  5b. TransformerConfig::qwen2_0_5b() constructor           (~40 LOC)
  5c. Extract arch from init APR file metadata              (~80 LOC)
  5d. Qwen tokenizer-vocab compatibility check              (~30 LOC)
  5e. GQA-7:1 attention forward-pass verification           (~50 LOC)
  5f. Wire actual weight load                              (~120 LOC)
  5g. LIVE 500-step smoke fine-tune (operator dispatch)        0 LOC
  5h. Stamp + publish as MODEL-2 v2                         (~10 LOC)

  Total: ~410 LOC + 1 LIVE training run.

Five Whys (§50.6):
  1. Why didn't §49 catch this? §49 was authored from strategy/
     data-budget reasoning; the 0-LOC step-5 cost implicitly
     assumed polymorphism. Live source inspection (this section's
     empirical move) revealed pretrain_real.rs:38-46 predates the
     assumption.
  2. Why catch this NOW and not in step 5 implementation? Per
     feedback_no_guessing.md: read live source before forming
     implementation plan. Surfacing the mismatch BEFORE writing
     200 LOC of weight-load code that fails at runtime is the
     cheapest place to pay cost-of-defect. The §50-prior wrong-
     premise PRs (#1466/#1467/#1468 closed) on the SHIP-007 / 0.5B
     gibberish track were the same defect class.
  3. Why option B over A or C? Preserves §24/§25 falsification
     evidence (we KEEP knowing from-scratch fails at 9.75; we just
     don't ship it as MODEL-2). Exercises the polymorphism
     TransformerConfig was designed for. Each new component becomes
     its own falsifier rather than a hidden coupling.
  4. Why is FALSIFY-005 the right place to fail-fast? PR #1470
     already pinned "Architecture mismatch is FAIL-FAST, not silent-
     truncate". Step 4 (PR #1471) doesn't enforce arch matching yet
     — returns "not yet wired" before getting there. So FALSIFY-005
     is currently UNBOUND but its discharge gate is well-defined:
     read APR header, compare against pretrain target, error with
     names of mismatched fields.
  5. Why isn't this a "punt"? A punt would say "blocked, await
     operator". This amendment names three options with LOC
     estimates, recommends one with reasoning, gives a concrete 8-
     step roadmap with falsifier discharge mapped to each sub-step.
     The work IS shippable; it's just bigger than 0 LOC.

Plain ship-% update:
  - MODEL-1: unchanged at 91% (SHIP-007 cascade infrastructure track)
  - MODEL-2: unchanged at 57% — first ship-% movement gated on §50.4
    step 5g (LIVE 500-step fine-tune producing val_loss < 9.38).
    Sub-steps 5a-5f can each individually move 1% with falsifier
    discharge (architecture-polymorphic infrastructure shipped ==
    evidence that the §49 path is REACHABLE, not just theoretical).

Refs:
  - §49 — MODEL-2 strategy pivot (PR #1461)
  - PR #1470 — apr-pretrain-from-init-v1 v1.0.0 PROPOSED contract
  - PR #1471 — apr pretrain --init clap field + magic-byte validate
  - feedback_no_guessing.md — read source before forming hypothesis
  - feedback_fix_root_cause_never_route_around.md

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
noahgift added a commit that referenced this pull request May 4, 2026
… 5b + DEFECT FIX (#1474)

§50.4 step 5b authored a contract assuming `qwen2_0_5b()` did not
exist. Live source inspection during impl revealed the constructor
ALREADY EXISTS at `transformer/config.rs:156`. Reading the HF config
byte-for-byte (per `feedback_no_guessing.md`) revealed a real defect:

  HF config (Qwen2.5-Coder-0.5B-Instruct):  tie_word_embeddings: true
  Existing code (qwen2_0_5b):                tie_word_embeddings: false

Fix: 1 LOC change `false → true`. Per Qwen scaling-law convention
verified against the HF cache:

  - Qwen2.5-Coder-0.5B: tie=true   (HF cache 2026-05-04 ✓)
  - Qwen2.5-Coder-1.5B: tie=true   (HF cache 2026-05-04 ✓; inherits
                                    via `..Self::qwen2_0_5b()` spread)
  - Qwen2.5-Coder-7B:   tie=false  (HF cache 2026-05-04 ✓; explicit
                                    in qwen2_7b())

Why the defect matters: tied vs untied embeddings is a load-bearing
architectural property. With tie=false (current bug), if an operator
fine-tunes from a Qwen2.5-0.5B init checkpoint, the lm_head will be
allocated as a separate tensor that doesn't get loaded (because the
APR file only contains the embed_tokens tensor — they share weights).
The result: lm_head random-initialized and untrained, producing
silent gibberish at val time. This is exactly the §49 / §50 failure
class the contract was authored to prevent.

What this PR adds:

  1. Fix `tie_word_embeddings: false → true` in `qwen2_0_5b()` at
     `transformer/config.rs:156-174`
  2. Add docstring noting the empirical verification + HF cache path
     + Qwen scaling-law quirk
  3. Add 3 new unit tests in `transformer::config::tests`:
       - `qwen2_0_5b_matches_hf_config_2026_05_04` (FALSIFY-001 byte-
         identity verification — 11 fields)
       - `qwen2_1_5b_inherits_tie_word_embeddings_from_0_5b` (drift-
         prevention; catches future spread-split refactors)
       - `qwen2_7b_does_not_tie_embeddings` (drift-prevention; pins
         the 7B Qwen scaling-law quirk against silent flips)

Test results (cargo test -p aprender-train --lib transformer::config::tests::qwen2):
    3 passed; 0 failed; 0 ignored

Discharges FALSIFY-APR-PRETRAIN-ARCH-001 in PR #1473's contract.

Five Whys:

  1. Why was the constructor already there but with the wrong tie
     setting? Likely authored before the spec-§49 use case became the
     load-bearing target. The constants for `qwen2_0_5b` were correct
     for inference, but tie_word_embeddings is mostly a training-
     pipeline concern — it determines whether lm_head is a separate
     trainable parameter or shares with embed_tokens.

  2. Why didn't pmat query / cargo test catch this earlier? Existing
     tests pinned shape (hidden, layers, heads, etc.) but no test
     verified `tie_word_embeddings`. This PR adds the missing
     drift-prevention test that catches the defect class.

  3. Why fix this in the same PR as the test (not a separate fix)?
     Toyota Way: the test IS the discharge mechanism for FALSIFY-001.
     A test that passed against the (defective) status quo would be a
     liar. Fixing first + testing second guarantees the test pins
     correct behavior, not whatever happened to be in the code.

  4. Why also pin qwen2_1_5b (inheritance) and qwen2_7b (anti-spread)?
     Those are drift-prevention. The spread-inheritance pattern
     `..Self::qwen2_0_5b()` is fragile — a future refactor could
     split the inheritance chain and silently flip tie_word_embeddings
     back to false on 1.5B. Test catches that. Similarly, an over-
     enthusiastic refactor could homogenize 7B with 0.5B (incorrectly
     setting 7B's tie=true). Test catches that too.

  5. Why §50.4 step 5b was overscoped at 40 LOC: §50 was authored
     under the assumption that the constructor didn't exist. Live
     source inspection (per `feedback_no_guessing.md`) revealed the
     foundation was already there, just with one defect. This is the
     same lesson as §50 itself — read source before authoring scope.
     The contract from PR #1473 is still valid; only the LOC estimate
     in §50.4's table was wrong.

Plain ship-% update:
  - MODEL-1: unchanged at 91% (SHIP-007 cascade infrastructure track)
  - MODEL-2: unchanged at 57% — first ship-% movement gated on §50.4
    step 5g (LIVE 500-step fine-tune producing val_loss < 9.38)

Refs:
  - SPEC-SHIP-TWO-001 §49 — MODEL-2 strategy pivot (#1461)
  - SPEC-SHIP-TWO-001 §50 — architecture-coupling finding (#1472, in flight)
  - PR #1470 — apr-pretrain-from-init-v1 contract (merged)
  - PR #1471 — apr pretrain --init wire-up (merged)
  - PR #1473 — apr-pretrain-arch-polymorphic-v1 contract (in flight)
  - HF config: ~/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-{0.5B,1.5B,7B}-Instruct/.../config.json
  - feedback_no_guessing.md — read source before forming hypothesis

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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