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Qwen3 0.6B fails to load after flutter_edge_ai 2.3 / litertlm 1.13 bump

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Évaluation

Difficulté
4/5
Temps estimé
3-5 jours
Accessibilité débutants
18/100
Type d'issue
Bug
Clarté
Plutôt claire
Activité
Active
Stack technique
android, dart
Domaine
machine-learning, mobile

Piste de recherche

Start with the failing path in the LiteRT-LM client at src/ffi/litert_lm_client.dart (initialize, around line 956) and LiteRtLmEngine.createModel in litert_lm_engine.dart (line 167), then reproduce with integration_test/llm_stability_e2e_test.dart, which is listed in all_tests.dart. The open question is whether Qwen3-0.6B needs re-conversion for LiteRT-LM 0.18 or a different load setting, and the issue still lacks those probe results. Done means either the model loads on the bumped packages or AIModelId.qwen25_05b is gated behind a load check with a fallback to Gemma.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Description

bug P1-High

Summary

After bumping to flutter_edge_ai 2.3.2 / flutter_edge_ai_litertlm 1.13.2 (LiteRT-LM 0.18.0, see #257), Qwen3 0.6B (Qwen3-0.6B.litertlm) no longer loads on device. The same file loads and answers on our current packages. Gemma 4 E2B is unaffected.

Error

BackendInitException: all FFI backends failed. Last attempted cpu. Attempts:
  gpu: Exception: Failed to create engine. Model may be invalid: .../app_flutter/Qwen3-0.6B.litertlm;
  cpu: Exception: Failed to create engine. Model may be invalid: .../app_flutter/Qwen3-0.6B.litertlm
#0 LiteRtLmFfiClient.initialize (flutter_edge_ai_litertlm/src/ffi/litert_lm_client.dart:956)
#1 LiteRtLmEngine.createModel (litert_lm_engine.dart:167)

Logged just before: [LiteRtLmFfi] litert_lm_engine_create took 91ms, then [LiteRtLmEngine] cpu backend failed, no candidates are left.

Evidence it is the package, not our code or the file

Result
Old: flutter_gemma 1.8.3 + _litertlm 1.6.3 (native 0.16.0), same phone, same file loads, replies (<think> ... )
New: flutter_edge_ai 2.3.2 + _litertlm 1.13.2 (native 0.18.0-e) Failed to create engine. Model may be invalid on GPU and CPU
File on device 614,236,160 bytes, identical size to the pushed fixture
Gemma 4 E2B (gemma-4-E2B-it.litertlm) on the new packages all cases pass

Device: vivo 1933, Android 11 (API 30), arm64. The native bundle downloaded and verified its checksum (native-v0.18.0-e), so it is not a corrupt download.

How it was found

New device test integration_test/llm_stability_e2e_test.dart (runs every model fixture). Before it, every baseline and post-bump device test ran on Gemma, so the Qwen regression was invisible.

Impact

Qwen3 0.6B is in our local catalog (AIModelId.qwen25_05b). On the bumped packages it cannot be activated, so anyone who picked it would get no local model.

Open

  • Probe in progress: does a different maxTokens (256 / 1024 / 4096) or an explicit CPU backend load it? Results will be added here.
  • Whether the file needs re-converting for LiteRT-LM 0.18 (the upstream "Model may be invalid" text suggests a format check). I could not confirm without upstream.

Proposed handling

  1. Report upstream (flutter_edge_ai) with the table above.
  2. Until fixed, do not ship the bump with Qwen3 0.6B in the catalog, or gate it behind a load check with a clear message and fall back to Gemma.
  3. Keep llm_stability_e2e_test in all_tests.dart so this is caught on every device run.

Related: #257 (the bump), #88 (device tests).

Langage dominant
Dart
Étoiles
2
Forks
2
Merge moyen
18 h 23 min
PR mergées (30 j)
40

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