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fix(annotate): try AutoModelForImageTextToText first, fall back to AutoModelForVision2Seq

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Newer transformers versions renamed/removed AutoModelForVision2Seq in
favour of AutoModelForImageTextToText for VL models. Try the new name
first and fall back gracefully so the transformers backend works on
both transformers 4.45-4.5x and 5.x.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Pepijn
2026-04-27 22:31:56 +02:00
parent 69f049b877
commit f583307590
1 changed files with 13 additions and 2 deletions
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src/lerobot/annotations/steerable_pipeline/vlm_client.py
+13 -2
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@@ -166,11 +166,22 @@ def _make_vllm_client(config: VlmConfig) -> VlmClient:
def _make_transformers_client(config: VlmConfig) -> VlmClient: def _make_transformers_client(config: VlmConfig) -> VlmClient:
try: try:
import torch # type: ignore[import-not-found] import torch # type: ignore[import-not-found]
from transformers import AutoModelForVision2Seq, AutoProcessor # type: ignore[import-not-found] import transformers # type: ignore[import-not-found]
from transformers import AutoProcessor # type: ignore[import-not-found]
except ImportError as exc: except ImportError as exc:
raise ImportError("transformers + torch are required for backend='transformers'.") from exc raise ImportError("transformers + torch are required for backend='transformers'.") from exc
auto_cls = (
getattr(transformers, "AutoModelForImageTextToText", None)
or getattr(transformers, "AutoModelForVision2Seq", None)
)
if auto_cls is None:
raise ImportError(
"Neither AutoModelForImageTextToText nor AutoModelForVision2Seq is available in this "
"transformers version. Install transformers>=4.45 (which has AutoModelForImageTextToText) "
"for VL models."
)
processor = AutoProcessor.from_pretrained(config.model_id) processor = AutoProcessor.from_pretrained(config.model_id)
model = AutoModelForVision2Seq.from_pretrained(config.model_id, torch_dtype="auto") model = auto_cls.from_pretrained(config.model_id, torch_dtype="auto")
model.eval() model.eval()
def _gen(batch: Sequence[Sequence[dict[str, Any]]], max_tok: int, temp: float) -> list[str]: def _gen(batch: Sequence[Sequence[dict[str, Any]]], max_tok: int, temp: float) -> list[str]: