Issue 45081 is the most actionable overall because it has a specific failure mode, clear trigger (`fix_mistral_regex=True`), and ongoing discussion, making it the best single issue to keep as the representative record.

Issue 45081 is the strongest representative candidate: it is a detailed, concrete bug report with multiple inbound references and an active repro path. Even so, the cluster does not form a true duplicate set.

Both are model-loading failures, but one is a key-mismatch on MobileLLM while the other is a storage deserialization error for float8; different root causes and code paths.

These concern different models and different bugs: torch.compile/SDPA behavior in Bamba versus save/load of custom token IDs in DiaConfig.

One is a Qwen3-MoE sliding_window behavior bug; the other is URL loading for AutoImageProcessor. No shared underlying defect.

Both involve `apply_chat_template`, but the triggers differ materially: batched padding=False crash versus assistant tool-call messages with no content.

These are unrelated loading/dtype issues: gguf tokenizer/model loading versus Blip2 dtype propagation for model and qformer.

Different models, different failure mechanisms: legacy field handling in BEiT versus packed-weight unpacking during accelerate loading for BitNet.

Same model family, but different subsystems: chat-template batching crash versus incorrect video vision_position_ids.

These are separate regressions in different areas: PIL image processor torchvision dependency versus Mistral tokenizer regex patching.

Both are regression-style issues, but they affect different models and different operations: RTDetrV2 inference outputs versus Qwen3.5 save_pretrained checkpoint correctness.

Summary: The cluster is heterogeneous: these issues span unrelated models, processors, tokenizer loading, save/load, dtype, and chat-template bugs. I do not see any soft-edge pair that is the same underlying defect, so no merges are justified.


Issue 44843 is the best single issue to anchor the cluster: it is actionable, regression-focused, and tied to a broadly used API path. The rest do not form a true duplicate set around it.

Issue 44843 is the strongest representative because it has the most discussion and an inbound reference, and it describes a concrete regression in a common tokenizer loading path. The other issues are more isolated model-specific failures or different bug classes.

Both concern MoE-related model behavior, but one is about auxiliary loss normalization and the other about a native-vs-remote implementation divergence. Different bug mechanisms and not mergeable as one fix.

Both involve tokenizer/model loading, but 43452 is about gguf_file breaking from_pretrained while 44843 is an unconditional model_info call in _patch_mistral_regex and offline mode. Different code paths.

These are unrelated: one is a legacy-field load failure in BEiT semantic segmentation, the other is a torch.compile + SDPA failure in Bamba-9B-v2.

Both are runtime failures, but Gemma3 ZeRO-3 loading IndexError and trainer.evaluate() failing after train() are distinct failures in different subsystems.

A CPU offload device mismatch in Bark-small is not the same bug as Perceiver failing on non-default resolution despite interpolate_pos_encoding=True.

Both are tokenizer regressions, but 43295 is about processor.tokenizer/image handling in custom code, while 45356 is codec handling and a misleading warning for Kimi-K2.5. Different fixes.

BEiT legacy-field loading and OmDet-Turbo processor/model resolution mismatch are separate model-specific issues.

Qwen3.5 num_labels propagation and granite_speech strict-config loading are different config-validation bugs with different symptoms and fixes.

Both concern tokenizer special tokens, but BigBird mask-token registration and mDeBERTa add_special_tokens BOS/EOS handling are different model-specific defects.

Summary: These issues are mostly unrelated model- or tokenizer-specific bugs. The soft pairs share broad themes (tokenizers, config loading, model divergence, runtime failures) but not the same concrete underlying code path, so none should be merged as duplicates.


No single issue cleanly represents the cluster because the items span several unrelated bugs; if forced, the most discussion-heavy issue is the broadest candidate, but it is not a true cluster canonical.

Both concern generation behavior, but the failure modes differ: chunked generation with compiled forward vs recurrent state reset with cache/seq_len>1.

The second item is only an isolated reproduction reference; there is no clear evidence it is the same Qwen3 auto-device-map cudaErrorAssert bug.

Different code paths and symptoms: BitNet packed-weight loading vs DiaConfig losing custom token IDs after save/load.

CPU-offload device mismatch in bark-small is unrelated to DiaConfig token ID loss and generation IndexError.

Both mention tied weights in v5, but one is a device-dependent serialization bug and the other is a broader translation-model tied-weights issue; not the same concrete bug.

Both involve config fields, but one is a missing pad_token_id on Llama4Config while the other is AutoConfig with trust_remote_code returning incomplete fields.

Unrelated failures: DAC latent reconstruction mismatch vs Whisper batch_decode ignoring skip_special_tokens.

Both are tokenizer-loading related, but one is an offline/model_info regression in _patch_mistral_regex and the other is a model-specific tokenizer load failure; not clearly the same bug.

Multimodal finetuning with multiple images per prompt is unrelated to Whisper processor batch_decode behavior.

Different subsystems and symptoms: BLIP2 dtype loading remains float32 vs RTDetrV2 output differences in v5.

Summary: This cluster is mostly a grab bag of unrelated issues. A few pairs are thematically close (tied weights, tokenizer loading, config fields), but none look like the same concrete bug/change strongly enough to merge.


There is no global best issue for deduplication because the set is not a coherent duplicate group; each item appears to represent a separate problem.

No single canonical issue stands out: the items cover distinct failure modes and models, with no strong duplicate cluster among them.

Different bugs: BitNet accelerate-loading/unpacked weights vs MLukeTokenizer task-time AttributeError. No shared concrete code path.

DeepSpeed training weight-shape error vs LayoutLMv3 box validation/error-message clarity. Related only by being training/model issues, not the same bug.

SigLIP2 zero probabilities vs processor.tokenizer regression with images/custom model code. Different symptoms and likely different code paths.

Qwen3.5 flash-attention generation bug vs Kimi-K2.5 tokenizer codec/regression warning. Different models and failure mechanisms.

Video-input regression for Qwen VL models vs trainer.evaluate after train. Unrelated areas and no shared underlying defect.

Tokenization regression across versions vs isolated reproduction of another issue; the titles are too generic to पुष्टि same bug, and no concrete overlap is shown.

Void segmentation map processing vs Qwen2-VL multi-GPU training. Different functionality and likely different components.

Missing PyTorch import error vs MacOS bus error with community CLIP model. Environment/import failure is not the same as runtime bus error.

Missing PyTorch import error vs NoneType broadcast error in Trainer. Separate setup/runtime issues with no shared code path.

Summary: These are mostly unrelated issues across different subsystems (training, tokenization, vision processing, environment/import, and model-specific regressions). None of the soft pairs look like the same underlying bug or change, so all soft edges should be rejected.


Issue 43504 is a strong representative because it describes a specific reproducible load failure and is easier to unify than the broader or more model-specific reports. However, the cluster does not appear to contain a true duplicate group overall.

Issue 43504 is the most concrete, self-contained model-loading bug in the set and has a clear failure mode tied to a specific class. The rest are separate bugs in different subsystems or model families.

Different problems: Beit pretrained loading with a legacy field vs. Bark CPU offload device mismatch. Same general area of model loading, but not the same bug.

One is an ImportError for missing PyTorch; the other is a weight reinitialization bug after resizing embeddings. No shared code-path problem.

Both involve generation correctness, but one is about compiled forward chunked generation and the other about Qwen3.5 flash-attention behavior. Too different to be duplicates.

Blip2 dtype propagation on load is unrelated to an MPS attention correctness issue when value head dim differs from query.

Qwen3-MoE sliding_window behavior and MobileLLM key errors are different model-specific loading/runtime issues.

Video multimodal token counting undefined names vs. Qwen2.5-VL rope index scaling for still images. Different code paths and symptoms.

Beit legacy-field loading failure is unrelated to BigBirdTokenizer mask token special-token registration/empty decode output.

Both are from_pretrained-related, but one is gguf_file breaking tokenizer/model loading and the other is a specific ScandiBERT load regression. Not the same underlying bug.

Qwen3.5 output_hidden_states bug and incorrect visual encoder keys on save_pretrained are distinct regressions affecting different parts of the model lifecycle.

Summary: This cluster is mostly a grab bag of unrelated Transformers bugs across different model families and code paths. None of the soft pairs look like true duplicates; they only share broad themes like loading, generation, or attention.


No issue is a strong global representative; the items are too diverse. If forced to anchor one, pick a concrete self-contained bug rather than a broad symptom, but it would not subsume the others.

No safe canonical issue: the cluster mixes unrelated bugs across different models and code paths, so none of the issues can represent the whole set as a duplicate target.

Both are error reports around missing config/tokenizer state, but they affect different models and different failure points; not the same bug.

Both mention dtype/precision, but one is a PyTorch warning during compile while the other is a load-time dtype propagation bug in Blip2; different code paths and fixes.

One is about missing mm_token_type_ids defaults for text-only fine-tuning; the other is multi-GPU inference OOM. Same model family, different problems.

Video-input regression across Qwen VL variants is unrelated to num_labels propagation into text config.

CUDA assert with auto device mapping is a different failure mode from inconsistent chunked generation under compiled forward.

Video-input ValueError in Qwen3OmniMoe and Mistral-3 image preprocessor dtype mismatch are distinct model-specific issues.

Both are missing pad token field regressions, but they concern different config classes/models and are not clearly the same underlying defect.

Trainer _gpu_broadcast_one NoneType error and Qwen2-VL multi-GPU training are too broad to treat as the same concrete bug.

MobileLLM key error and AutoImageProcessor URL loading failure are different loading problems with different surfaces and likely different fixes.

Summary: This cluster is heterogeneous: the soft pairs share only broad surface themes (token/config errors, dtype issues, video/image/model-loading bugs) but do not appear to be the same underlying defect. I would reject all soft edges and avoid collapsing these issues into one canonical duplicate.


No single issue is a good global representative here; the strongest theme is broad Hugging Face model/runtime regressions, but the items are too diverse to collapse into one issue.

No clear canonical issue: the set spans distinct bugs across trainer internals, model config fields, tokenizer/offline behavior, audio chat templates, quantization/offloading, and model-specific architecture regressions.

Different bugs: one is a Trainer GPU broadcast TypeError, the other is TrainerState token-count tracking not updating.

Both involve missing config fields, but they affect different models and different attributes (`pad_token_id` vs `pad_token_idx`), so this is not the same concrete bug.

Both touch rotary settings, but one is a Smollm3 RoPE-layer mismatch and the other is GPTNeoX config reload behavior; distinct failure modes and code paths.

Unrelated: dtype drift in evaluation vs. auxiliary-loss normalization in MoE models.

Completely different areas: SigLIP2 implementation discrepancy vs. an incorrect pipeline error message about translation support.

Different subsystems and symptoms: audio chat-template sampling-rate defaulting vs. a template compilation TypeError.

Different problems: MoE auxiliary-loss normalization vs. offloading behavior with quantization.

Both relate to model loading, but one is a specific model failing to load and the other is an offline-mode break in `_patch_mistral_regex`; not the same bug.

Different issues: Qwen2-VL multi-GPU training vs. an ImportError for `GenerationMixin`.

Both concern training behavior, but one is BF16/FP32 dtype drift and the other is gradient growth under DeepSpeed ZeRO-3; no concrete overlap.

Summary: These issues are mostly unrelated and only share superficial wording around training, configs, or model-loading. None of the soft pairs look like true duplicates of the same concrete bug or change.


issue:43299 is the strongest standalone issue because it is a concrete, version-specific loading regression with a clear model scope and actionable symptom.

No single issue is canonical: the set spans distinct model families, subsystems, and failure modes rather than one shared regression.

Different models and failures: Smollm3 RoPE-layer selection vs Qwen3-Next tied-weights warning. Not the same bug.

Both mention Qwen3/MoE loading, but one is an accelerate+deepspeed conversion issue and the other is a transformers version regression for Qwen3VL. Different concrete code paths.

Qwen3OmniMoe video processing error vs Qwen3.5 flash-attention generation issue. Same broad family only, not the same defect.

Unrelated model families and symptoms: dtype mismatch in inference vs Wav2Vec2 save/tokenization failure.

Both are SigLIP2-related, but one is a broken documentation example and the other is an implementation discrepancy with JAX. Separate issues.

SigLIP2 implementation mismatch vs DeepSpeed ZeRO-3 gradient growth during random-init training. Different subsystems and failure modes.

Multi-GPU training request/issue for Qwen2-VL is not the same as the Trainer API TypeError about num_items_in_batch.

Phi-4 multimodal loading regression vs PEFT minimum-version bump. No shared underlying bug.

Both are serving-related, but one crashes on multimodal continuous batching and the other on KV-cache continuation indexing. Different code paths.

Summary: This cluster is a grab-bag of unrelated Transformers issues across different models and subsystems. None of the soft pairs look like the same underlying bug or change, so no duplicates should be merged here.


issue:43824 is the best standalone issue in this set because it is clearly described, has broad impact, and has the strongest evidence of community traction.

issue:43824 is the most central/high-signal item here, with the most discussion and inbound references, but it should be treated as an anchor issue rather than a true duplicate cluster representative.

Both mention model/runtime failures, but one is an import/export problem for Qwen2.5-VL while the other is a storage-type error for Float8; different code paths and fixes.

Both are v5-related regressions, but one breaks custom model initialization and the other changes lm_head.weight serialization behavior; not the same bug.

Both involve init/config compatibility in v5, but one is a missing config field and the other is an unexpected constructor argument from init_empty_weights; distinct failures.

One is an auxiliary-loss normalization bug in MoE models, the other is a PEFT/local-checkpoint loading issue for GraniteSpeech; unrelated.

Both affect model outputs, but the first is a SigLIP2 AutoModel/pipeline misbehavior and the second is a position-id key warning across many models; too different to unify.

Both are about apply_chat_template, but one is all-zero assistant masks for multimodal inputs while the other is a crash on tool-call messages with no content; different trigger and symptom.

Different subsystems and failure modes: multimodal token counting/video branch bug versus CPU-offload device mismatch in Bark.

One is a MacOS bus error with a community CLIP model; the other is a Qwen2-VL multi-GPU training request. No shared bug.

Both mention multimodal processor/model API friction, but one is a documentation-example mismatch plus quantization failure and the other is a regression around processor.tokenizer access; not the same concrete issue.

Summary: These issues are mostly unrelated regression/bug reports spanning multimodal chat templating, v5 initialization, serialization, and model-specific bugs. No soft pair is close enough to merge as the same underlying fix.


No best issue candidate: the cluster does not form a coherent duplicate group, so choosing any one issue would be arbitrary.

No single issue is a clear canonical representative because the items are largely unrelated and span different products/subsystems with distinct failure modes.

Different bugs: one is generation kwargs handling after sync_gpus, the other is a Qwen3.5 output_hidden_states issue.

Qwen2 tensor-parallel OOM and a PEFT version bump are unrelated.

Tokenizer offset_mapping mismatch is a different problem from missing BOS/EOS with add_special_tokens.

MoE weight conversion under accelerate+deepspeed is unrelated to an unreleased PEFT minimum version.

A janus image-generation test failure does not match a dependency version bump issue.

Video-input validation in Qwen3OmniMoe and Gemma 4 mm_token_type_ids defaults are different code paths and symptoms.

TorchScript GeneratorExp export failure and zero probabilities in siglip2 are unrelated model behaviors.

Embedding dtype drift in evaluate is distinct from a resume_from_checkpoint key mismatch.

MPS attention correctness with mismatched head dims is unrelated to a PyTorch version guard for AuxRequest import.

Liger Kernel not applied with model_init and Qwen3.5 hidden_states handling are separate bugs.

Summary: This cluster is not a duplicate set; it mixes unrelated bugs across different models, tokenizers, generation paths, and environment/version checks. No soft pair looks like the same underlying issue, and there are no PRs to consider.


`issue:43957` has the best combination of scope and specificity among this set: it names the failure mode, version range, and multiple impacted models, making it the strongest standalone bug report here.

`issue:43957` is the broadest and most representative report: a concrete Transformers v5 model-loading regression with multiple affected models and a clear shared code path (`torch.device("meta")` loading).

Different failures: Apple Silicon allocator warmup `TypeError` vs Qwen3.5 flash-attention generation bug.

Unrelated problems: ZeRO-3 gradient instability during training vs a model-loading failure for Phi-4 multimodal.

Both involve loading-related regressions, but one is an import error for a flash-attention helper while the other is a meta-device loading bug; no clear same root cause.

Both mention device mismatches, but the affected models and concrete failure modes differ: CPU offload on bark-small vs a stale GraniteSpeech CI test.

Both are multimodal-related, but they describe different bugs in different model families and different mechanisms (`mm_token_type_ids` defaulting vs rope index scaling).

Completely different areas: MPS attention correctness vs tokenizer codec/regex regression.

Same symptom (`pad_token_id` missing), but different model configs and no evidence they share the exact same underlying change or fix.

Different tokenizer-related failures in different model families; not enough to treat as one bug.

Serialization of tied weights depending on device is unrelated to LoRA merging with extended vocabulary and model collapse.

Summary: This set is mostly a grab-bag of unrelated Transformers bugs across model loading, tokenizer/config regressions, device/offload issues, and attention/serialization problems. I don’t see any pair that is confidently the same underlying bug or change from the available titles alone.


No single issue is a good global representative because the cluster is heterogeneous and not a duplicate set.

No canonical issue: the items describe distinct failures, regressions, and model-specific behaviors rather than one shared bug.

Different problems: Perceiver non-default image size interpolation vs RT-DETR memory not being freed. Different model families and failure modes.

Both involve labels, but one is Trainer metric-gathering trimming the last batch and the other is BeitImageProcessorFast reduce_labels returning only one label. Different code paths and bugs.

Both are import errors, but for different symbols and modules: flash attention helper vs Qwen2.5-VL class export. Not the same missing export.

Regression in custom processor/tokenizer image passing vs OmDet-Turbo processor/model image-size mismatch. Different subsystems and fixes.

OpenAI GPT-OSS perplexity issue vs Qwen3.5 num_labels propagation. Unrelated model behavior and configuration bugs.

SigLIP2 implementation discrepancy vs resume_from_checkpoint key mismatch. Different functionality and no shared code-path problem.

Quantization/offloading behavior vs chunked generation with compiled forward. Both mention runtime behavior, but they are distinct mechanisms and bugs.

ZeRO-3 gradient growth during training vs trainer.evaluate() failing after trainer.train(). Different training issues and root causes.

StableLmConfig missing pad_token_idx after update vs storage type lookup error for Float8_e4m3fnStorage. Different versioning/config and serialization problems.

Summary: This cluster is a loose collection of unrelated issue reports across different models and subsystems. The soft pairs are only superficially similar by wording; none look like the same underlying bug or change.


Issue 44521 is the strongest standalone issue because it is active, detailed, and tied to a specific user-facing regression with clear reproduction context.

Issue 44521 is the most representative cluster member: it is open, has multiple references/discussion, and describes a concrete, reproducible multimodal `apply_chat_template` bug.

Different failures: a missing `pad_token_idx` config field versus a model load failure for a specific repo. No shared code path is evident.

One is about Qwen3.5 label propagation, the other about Gemma EOS token configuration. Different models and different config bugs.

Both involve `apply_chat_template`, but one is a batching crash with `padding=False` while the other is zeroed assistant masks. Related area, not the same concrete bug.

Unrelated topics: DSA indexer activation logic versus removal of `is_torch_fx_available` breaking trust-remote-code models.

ImportError for a Qwen2.5-VL class versus DeepSeek detokenization behavior. Different models and different failure modes.

SigLIP probability output issue and RT-DETR memory cleanup issue are unrelated model/runtime bugs.

Qwen3.5 hidden-state output bug versus unsafe `sys.modules` access in `modeling_utils`. No clear shared underlying defect.

DAC latent/forward mismatch is a codec bug; `_get_feat_extract_output_lengths` in qwen3_omni_moe is a separate feature-extraction helper issue.

Tensor-parallel OOM when loading a model versus inconsistent chunked generation with compiled forward. Different symptoms, different likely causes.

Summary: These are mostly unrelated issue reports that only share broad Transformers themes. I do not see any pair that clearly describes the same underlying bug or change, so all soft edges should be rejected.


issue:43994 is the strongest representative issue overall because it captures the concrete broken behavior most directly and broadly.

issue:43994 is the clearest and broadest SigLIP2 user-facing report, covering incorrect outputs across AutoModel and pipeline usage and likely subsuming the narrower symptom reports.

Different models and failures: FineGrainedFP8Config on Qwen3Moe vs a missing Qwen2.5-VL import; not the same bug.

One is a processor.tokenizer regression, the other is quantization/offload behavior; same general area but not one concrete defect.

Both mention dtype/memory, but they describe different Qwen3 MoE problems with no clear shared code-path.

Processor.tokenizer regression and batched video shape bug are distinct processor issues.

Custom model processor API breakage is unrelated to SigLIP2 incorrect outputs.

Both are tokenizer-related, but for different models and different failure modes.

CPU-offload device mismatch and a Qwen3 Omni MoE helper-function issue are unrelated.

Both are docs issues, but they concern different API references and should not be merged as the same defect.

OmDet-Turbo input-size mismatch and LwDetrImageLoss AMP crash are separate model/runtime bugs.

Both point to the same SigLIP2 wrong-output problem; one reports zero probabilities, the other describes a HF-vs-JAX implementation discrepancy.

Summary: Mostly a mixed bag of unrelated issues; the only plausible duplicate cluster is the SigLIP2 incorrect-output reports, while the rest are separate bugs or docs regressions.


Issue 44462 is the most broadly user-facing and reusable bug report here, but it is not a good duplicate target for the other items.

Issue 44462 is the cleanest concrete regression in the set (AutoTokenizer/tokenizer.json loading) and the most plausible anchor, but the overall cluster is still heterogeneous.

Extended-vocabulary LoRA collapse is a model-merging/output-quality bug; the 2^24 categories error is a categorical feature-limit RuntimeError. Different failure modes and code paths.

AutoTokenizer ignoring tokenizer.json is a loading-resolution bug; removal of is_torch_fx_available breaks trust_remote_code imports. Different subsystems and symptoms.

Loading cjvt/sleng-bert failing and mdeberta-v3-base not adding BOS/EOS with add_special_tokens=True are both tokenizer issues, but not the same concrete bug.

Batched video processor shape mismatch and SigLIP2 HF/JAX discrepancy concern different components and likely different root causes.

A trust_remote_code import break from a removed helper is unrelated to DeepSeek tokenizer producing incorrect results in v5.

Both mention compatibility/import problems, but one is a missing flash-attention helper import and the other is a Torch 2.10 incompatibility report; not the same bug.

Chat template crashes on assistant tool-call messages, while the Qwen2.5-VL report is about incorrect vision_position_ids for video input. Different paths.

BigBirdTokenizer special-token registration causing empty decode is unrelated to a GraniteSpeech CI device-override test failure.

These are both import errors, but they involve different missing symbols (_flash_supports_window_size vs HybridCache) and different affected code paths.

Pegasus v4/v5 output drift and Qwen3_5 recurrent-state reset with cache are distinct model-specific regressions.

Summary: This cluster is mostly a set of soft-similarity false positives: the issues span unrelated tokenizer, video, cache, import, CI, and model-specific regressions. I do not see any pair that should be merged as the same underlying bug.


Different models and bugs: LayoutLMv2 NER/padding crash vs Wav2Vec2 save_pretrained/tokenization failure.

Unrelated problems: batched video shape bug vs tensor-parallel OOM when loading a Qwen2 model.

Docs/example mismatch and quantization failure are unrelated to BeitImageProcessorFast reduce_labels behavior.

Flash Attention 2 test failures and Trainer _gpu_broadcast_one NoneType errors are different code paths.

Embedding dtype regression in eval is unrelated to pipeline translation support/error-message behavior.

torch.export/torch_compilable_check failures are a different issue from Wav2Vec2 save/tokenization problems.

DAC from_latents/forward mismatch is unrelated to the GraniteSpeech stale device override CI failure.

Base_model_prefix loading regression and Qwen3-VL weight-shape mismatch are distinct loading bugs.

Audio chat-template sampling-rate default bug is not the same as batched video output-shape bug.

Gemma 4 multimodal token-type-id defaults and Qwen2.5-VL video position-id issues are separate problems.

Summary: No duplicate cluster emerges here: the issues span unrelated subsystems and failure modes (tokenization, loading, export, CI, docs, audio/video processors). All soft edges should be rejected.


Issue 44493 is the closest to a cross-model regression umbrella, but it still does not subsume the other reports.

Issue 44493 is the broadest-sounding report, but the set is too heterogeneous to support a real canonical duplicate issue.

Different model families and different failure modes: weight tying in Mistral3 vs required multimodal token type ids in Gemma 4.

One is a Python 3.13 import/torch.jit parsing error in DebertaV2; the other is a helper-length calculation bug in qwen3_omni_moe.

DAC forward/latent mismatch is a codec bug, while 44493 is a position-id key regression across many models.

SigLIP2 docs/quantization example issues are documentation and example failures, not the same bug as a position-id mismatch.

Both are version regressions, but one is Pegasus generation output drift and the other is Deepseek tokenizer correctness; different code paths.

Unexpected zero probabilities in SigLIP2 is a model output issue, not the same underlying problem as unexpected position-id keys.

Flash-attention generation failure in Qwen3.5 is unrelated to Gemma 4 multi-GPU CUDA OOM.

Both touch processors, but batched video shape handling and Beit reduce_labels behavior are distinct bugs in different components.

torch.compile SDPA failure in Bamba is unrelated to qwen3_omni_moe feature-length helper behavior.

SwitchTransformers sparse-layer creation on zero layers is a config construction bug, not the same as the qwen3_omni_moe helper issue.

Different subsystems and symptoms: SwitchTransformers config logic vs Bamba torch.compile/SDPA failure.

LayoutLMv3 error-message clarity and Trainer/_gpu_broadcast_one NoneType failure are unrelated issues.

Summary: No convincing duplicate cluster here: the items span unrelated models, processors, configs, and runtime failures. All soft edges should be rejected.


No canonical issue: the items cover distinct bugs across different models and subsystems, and the soft pairs do not share a single concrete failure mode.

Both involve dtype/device-related failures, but they affect different models and different paths (LwDetr loss under AMP/CUDA vs SwitchTransformers/TimmWrapperModel bfloat16 inference).

Both are tokenizer-loading regressions in v5, but one is about decoder type selection and the other about AutoTokenizer ignoring tokenizer.json; different bugs and fixes.

These are unrelated vision/data-processing issues: Qwen2.5-VL multi-image IterableDataset finetuning vs BeitImageProcessorFast reduce_labels behavior.

Different models and symptoms: Pegasus output regression across v4/v5 vs Qwen3.5 generation breaking with flash-attention.

Both are runtime failures, but one is a CPU offload device mismatch for bark-small and the other is a mixed-precision loss crash for LwDetr; not the same code-path bug.

Tokenizer AutoTokenizer loading in v5 is unrelated to the MPS attention correctness issue when value head dim differs from query.

Both concern Gemma 4 processor handling, but one is a mistral_common import failure and the other is a missing _tokenizer attribute in serve; different concrete failures.

Unrelated bugs: SwitchTransformers sparse layer construction vs BigBirdTokenizer mask token registration/empty decode output.

Different models and failure modes: Perceiver image classification at non-default resolution vs Wav2Vec2 save_pretrained/tokenization failure.

These affect different model families and different defects: SwitchTransformers layer creation vs Beit semantic-segmentation preset loading with a legacy field.

Summary: This cluster is a heterogeneous mix of unrelated Transformers bugs (tokenizers, vision models, model config, runtime/device issues, and processor loading). None of the soft pairs look like the same underlying defect, so no duplicate merge is warranted.


No issue is a better representative duplicate target than the others, because none of the soft-linked items describe the same underlying bug.

No clear canonical issue exists here; the items are heterogeneous (config bugs, import errors, tokenizer issues, device-mapping, quantization, and multimodal processor problems).

Different bugs: SwitchTransformers sparse-layer config vs a GraniteSpeech CI device-override failure.

Tokenizer special-token registration bug vs qwen3_omni_moe feature-length helper behavior; unrelated code paths.

Config-layer creation bug and bfloat16 dtype mismatch are separate failure modes.

Flash-attention import error vs model loading failure for a specific repo; no shared underlying defect.

Quantization/offloading behavior vs unexpected position-id keys; different symptoms and likely different causes.

Both involve inference/memory, but one is quantization offloading and the other is multi-GPU OOM; not the same concrete bug.

torch.compile + SDPA issue in Bamba is unrelated to GraniteSpeech CI stale device override behavior.

Llama 3.2 vision model loading regression is not the same as embedding resize/reinitialization with untied embeddings.

Gemma 4 processor import failure from mistral_common is unrelated to protobuf error masking in tokenizers.

Qwen3 auto device-mapping cuda assert and Gemma4 multi-GPU OOM are distinct runtime problems.

Embedding dtype drift in evaluate is unrelated to an undefined video-token counting branch.

Summary: No convincing duplicate pairs: the listed issues span unrelated subsystems and failure modes, so the soft-similarity matches look like false positives.


Both are CI-related failures, but they concern different models and different failure modes: BitNet accelerated loading vs GraniteSpeech stale device override tests.

Unrelated bugs: a Python compatibility issue in Transformers 5.1.0 versus a divergence between native DeepseekV3 and a remote implementation.

Different code paths and symptoms: Flash Attention 2 test failures versus token embedding resizing/reinitialization in post_init.

Different models and failures: a dummy Conv2d in Sam3PixelDecoder versus incorrect outputs for Siglip2 via AutoModel/pipeline.

No shared bug: Sam3PixelDecoder dummy layer versus RT-DETR memory not being released on deletion.

Different subsystems and problems: tokenizer loading failure versus bfloat16 dtype mismatches in inference.

Both involve v5 behavior changes, but one is generation/output discrepancy and the other is serialization/tied-weight inconsistency; not the same concrete bug.

Different failure modes and platforms: Trainer _gpu_broadcast_one TypeError versus MacOS bus error with a community CLIP model.

No concrete overlap: Siglip2 nonsensical outputs versus RT-DETR memory cleanup issues.

Both are quantization-related, but one is a model/config failure with FineGrainedFP8Config and the other is a generic quantized model saving bug.

Summary: No soft pair looks like the same underlying bug or change. The cluster is a set of unrelated issue reports connected only by broad superficial similarity (CI failures, model-specific bugs, quantization, serialization, etc.).


issue:43824 is the best standalone issue in the set because it is specific, reproducible, and appears to have broader impact than the others.

issue:43824 is the strongest representative by signal: it has a clear runtime failure, multiple inbound references, and a concrete import regression in the transformers namespace.

Different bugs: Qwen3 MOE conversion under accelerate/deepspeed vs embedding dtype drift in evaluation.

Both involve eval-time behavior, but one is a dtype change and the other is gradient-checkpointing incompatibility; not the same failure.

Completely unrelated: pipeline top_k output shape change vs Trainer GPU broadcast TypeError.

Both are generation/config issues, but one is eos_token_id mismatch in Gemma-3 and the other is a flash-attention regression in Qwen3.5.

Both are tokenizer-related, but they affect different tokenizers and different concrete failures.

Different regressions: unsupported feature-extractor arg vs video-input regression across Qwen VL variants.

Both mention model implementation parity, but they concern different architectures and different divergence points.

Tokenizer special-token registration bug is unrelated to recurrent-state reset when using cache.

Different subsystems and failures: auto_docstring annotations handling vs Gemma 4 processor import breakage.

Specific Qwen2_5_VL import failure is not the same as a generic transformers dependency issue.

Summary: The candidate pairs are only superficially similar (shared subsystem words like tokenizer, generation, eval, or model-loading), but they describe different concrete bugs and code paths. I would reject all soft edges and treat this cluster as unrelated issues rather than duplicates.


Different problems: OmDet-Turbo processor/model input-size mismatch vs RT-DETR memory not being released after deletion. No shared underlying bug or fix.

Flash Attention 2 test failures are unrelated to image processor handling of void segmentation maps; different subsystem and failure mode.

MacOS bus error on a CLIP model is not the same as SigLIP2 documentation example errors/quantization failure.

LayoutLMv3 box/info error message clarity is unrelated to TrainerState num_input_tokens_seen not updating.

BEiT pretrained preset legacy-field loading bug is different from LwDetrImageLoss crashing under float16 AMP on CUDA.

Trainer NoneType broadcast error and Adafactor+DeepSpeed OOM are distinct issues with different root causes.

Qwen3-VL missing pad_token_id when loading is separate from Qwen3-MoE sliding_window behavior.

Qwen3.5 flash-attention generation failure is unrelated to the 2^24 category-count runtime limit.

Both mention OOM, but one is int4 quantization memory regression in Transformers 5.x and the other is Gemma4 multi-GPU inference memory exhaustion; not the same code-path problem.

Summary: All soft pairs look like false positives: they span different models, error surfaces, and code paths, so there is no single duplicate cluster representative here.


Issue 44186 is a strong representative bug report because it is actionable, well-scoped, and clearly tied to one component; the rest are similarly isolated but not better representatives of the overall set.

Issue 44186 is the most concrete and narrowly scoped report in the set, with a specific crash path in LayoutLMv2Tokenizer and clear reproduction context.

Different components and failures: BeitImageProcessorFast label reduction vs LayoutLMv2Tokenizer crashes on NER/batched padding. Not the same bug.

Both involve audio, but one is about chat template sampling-rate defaults and the other is a feature extractor passing an unsupported center argument. Different code paths and symptoms.

Gradient checkpointing in eval mode is unrelated to resume_from_checkpoint key mismatches.

Tokenizer loading failure and Perceiver image resolution/interpolation failure are distinct issues in different subsystems.

Failing Flash Attention 2 tests is unrelated to an ImportError for GenerationMixin.

Different training/generation paths: generation kwargs update after sync_gpus vs gather_for_metrics dropping tuple labels.

LayoutLMv3 error messaging for insufficient boxes is unrelated to a MacOS bus error with a CLIP model.

Both mention Qwen, but one is a FineGrainedFP8Config failure and the other is a weight-shape mismatch during model loading. Not the same concrete defect.

Both touch label-related code, but one is BeitImageProcessorFast label reduction and the other is LwDetrImageLoss crashing under AMP/CUDA. Different bugs.

torch.compile with SDPA in Bamba-9B-v2 is unrelated to checkpoint resumption key mismatches.

Summary: These issues are mostly unrelated, spanning different models, processors, and failure modes. I do not see any soft pair that looks like the same underlying bug or change.


`issue:45003` is the strongest global issue candidate because it describes a general runtime-safety bug rather than a model-specific symptom.

`issue:45003` is the most cross-cutting and code-centric report: a utility-level unsafe `sys.modules` access can affect many call paths, making it a strong representative issue.

Different bugs: one is CPU-offload device mismatch in a model, the other is a checkpoint key mismatch during resume.

Unrelated subsystems: LayoutLMv3 error messaging vs token-embedding resizing/post-init reinitialization.

Different model/processor bugs: BEiT label reduction vs SIGLIP2 AutoModel/pipeline output corruption.

One is a utility safety bug in `modeling_utils`; the other is a model parity regression for DeepSeekV3MoE.

Both involve model execution behavior, but the concrete failures differ: CLIP hidden states vs attention-kernel branching under `torch.compile`.

Different code paths and symptoms: Qwen recurrent-state reset vs Whisper `batch_decode` token skipping.

Both are config/API discrepancies, but they concern different models and different missing/uneven attributes.

Distinct regressions: video-input handling for Qwen VL variants vs chat-template tokenization with tool-call assistant messages.

Different failure modes: SIGLIP2 inference produces bad outputs, while LASR feature extraction crashes on an unsupported argument.

Separate model-specific problems: Phi-4 multimodal loading failure vs DeepSeekV3MoE implementation divergence.

Summary: No soft pair looks like the same underlying bug or change; they mostly share only broad topic overlap. I would not merge any pair. Among the issues, `modeling_utils unsafely accesses sys.modules[]` is the most broadly applicable code-path bug and the best representative issue here.


Both concern model loading regressions, but one is a weight-shape mismatch for Qwen3-VL-30B and the other is a save_pretrained checkpoint regression for Qwen3.5; different symptoms and paths.

Both mention Transformers 5.0 behavior changes, but one is about custom model initialization and the other about patchtsmixer post_init being disallowed; not the same bug.

One is a missing GenerationMixin cache in chunked_prefill, the other is recurrent state resetting in modular_qwen3_5; different generation/state-management defects.

Both involve loading/training failures, but one is tensor-parallel OOM for Qwen2-57B and the other is a resume_from_checkpoint key mismatch; unrelated root causes.

Flash Attention 2 test failures are unrelated to Qwen2-VL multi-GPU training; no shared concrete bug or fix path.

Both can manifest as OOM, but one is TP load OOM for a specific Qwen model while the other is increased CUDA reserved memory under int4 quantization; different mechanisms and fixes.

Both are import-related, but they involve different missing symbols from different dependencies and different processor-loading failures.

BigBirdTokenizer special-token decode bug is unrelated to resume_from_checkpoint key mismatch; one is tokenizer registration, the other checkpoint key handling.

AdaFactor+DeepSpeed OOM and TrainerState num_input_tokens_seen not updating are different training issues with no shared code-path bug.

Both are model-loading regressions, but one is a specific Llama 3.2 90B Vision breakage in 4.46.2 and the other is a 5.0 base_model_prefix regression; different eras and failure modes.

Summary: These items are a heterogeneous set of unrelated issues. The soft-similarity pairs share broad themes like loading, OOM, or checkpointing, but they describe different failure modes, code paths, or regressions and should not be merged as duplicates.


No best issue for deduplication. The cluster does not converge on one underlying bug; choosing any item would misrepresent the rest as duplicates.

No single canonical issue: the items describe distinct defects in different code paths and models (for example, optimizer OOM, embedding reinit, loading errors, docs mistakes, and video/regression bugs).

Different problems: SigLIP2 implementation discrepancy versus a docs reference to removed `pipeline()`. One is model behavior, the other documentation cleanup.

Unrelated bugs: a `patchtsmixer` post-init API restriction versus a video-input regression affecting Qwen VL models.

Both touch docs/model utilities, but they are different failures: SigLIP2 example mistakes versus `LasrFeatureExtractor` passing an unsupported `center` arg.

Distinct runtime paths: gradient checkpointing in eval mode versus quantization/offloading behavior. Not the same defect.

Different root causes and subsystems: DeepSpeed Adafactor OOM versus token-embedding resize causing output embedding reinitialization.

Separate model-specific reports: ESM2 broken versus a Janus image-generation test failure. No shared underlying bug.

Dataset-loading failure in a training script is unrelated to an unrecognized model/config loading error for Olmo-Hybrid.

Quantization/offloading behavior and Gemma `eos_token_id` config mismatch are different issues with different symptoms and fixes.

One is an Adafactor/DeepSpeed memory issue; the other is a model-loading breakage for Llama 3.2 Vision. Not the same code-path problem.

FSDP CPU-RAM-efficient loading and Qwen3-VL weight-shape mismatch are separate loading/regression bugs, not mergeable as one fix.

Summary: This cluster is heterogeneous: it mixes unrelated bugs across training, loading, quantization, docs, model-specific regressions, and test failures. None of the proposed soft pairs look like the same underlying issue or change, so there is no canonical duplicate target.


43316 is the best single triage anchor because it is concrete, user-facing, and closest to the recurring config-loading/API theme among the items.

Issue 43316 is the broadest and most reusable config/API inconsistency in the set, but it is not a duplicate representative for the others.

Both are config loading/reload inconsistencies, but one loses fields with trust_remote_code and the other resets rotary_pct on reload; different bugs and fixes.

Both involve multimodal preprocessing, but one is a Qwen3OmniMoe video ValueError and the other is a Qwen2_5_VLProcessor batching/padding crash; not the same code path.

Completely different failures: TorchScript parsing on Python 3.13 vs a DeepSpeed ZeRO3 deque underflow.

Both touch config handling, but Gemma3TextConfig API mismatch and granite_speech strict loading are distinct model-specific problems.

Dependency/extra-install packaging issue versus a config API discrepancy; no shared underlying bug.

Gradient checkpointing in eval mode and Qwen3 MoE memory usage are different training/runtime issues.

Label truncation in gather_for_metrics and auxiliary-loss normalization are unrelated evaluation vs training bugs.

DeepSpeed OOM with Adafactor and a CustomTrainer compute_loss signature break are unrelated.

Both are Mistral-family issues, but one is tied weights in generation and the other is a FlashAttention failure; different concrete code paths.

Both are model/config mismatch reports, but one is an API discrepancy in config objects and the other is an image preprocessor dtype mismatch; not the same bug.

Summary: No soft pair looks like a true duplicate; the list is a mix of unrelated bugs, with only a loose config/API theme appearing in a few titles.


No single issue is a good canonical representative for this set because the items are heterogeneous rather than duplicates.

No PR candidate is available, and no merged code change is implied by these issues.

None of the issues form a single duplicate bug cluster; the pairwise similarities are superficial and span unrelated regressions, questions, and API breakages.

No PRs were provided, and there is no clear shared code change to anchor a canonical PR.

Different problems: a dummy Conv2d in Sam3PixelDecoder vs CUDA OOM in Sam3Video; not the same bug or fix.

LoRA merge/model collapse on tied embeddings is unrelated to Gemma 4 mm_token_type_ids being required for text-only fine-tuning.

One is a Llama 3.2 Vision model-loading regression; the other is a missing GenerationMixin import. Same area, different failure.

Pipeline selecting image processors for video classification is unrelated to SAM3VisionEncoderOutput missing fpn_position_embeddings.

A model_max_length question for DeBERTa-V3 is not the same as a missing transformers.onnx module import error.

CLIPVisionModel hidden_states regression and Qwen video-input regression affect different models and code paths.

top_k changing pipeline output nesting is unrelated to AutoModel requiring PyTorch; different symptoms and fixes.

processor.tokenizer/image-to-tokenizer regression is separate from an incorrect translation-task error message in pipeline().

KV-cache continuation indexing crash in transformers serve is unrelated to Gemma4Processor missing _tokenizer.

Summary: All soft pairs are false positives: they share broad transformer/video/processor language, but each pair points to a different subsystem, model family, or failure mode. No duplicate cluster emerges here.


Both mention tokenizer/model config regressions, but one is about processor.tokenizer/image passthrough and the other is an eos_token_id mismatch in Gemma-3; different bugs.

One concerns patchtsmixer post_init being disallowed; the other is about non-persistent buffers being filled with junk in v5. Separate failure modes.

Both are model-loading errors, but gguf_file/AutoTokenizer loading and an unrecognized model_type in config.json are distinct root causes.

CLIPVisionModel hidden_states output and router load-balancing loss are unrelated code paths and symptoms.

Custom model initialization breakage is broader and different from GPTNeoX rotary_pct not persisting on reload.

The first is an isolated repro of another issue; the second is a specific apply_chat_template crash with tool calls and empty content. Not the same bug.

Qwen3-VL weight-shape mismatch and Qwen3-Next tied-weights warning are separate model/config issues.

Both touch multimodal chat/video behavior, but one is all-zero assistant masks from apply_chat_template and the other is incorrect vision_position_ids for qwen2.5-vl video input.

Video input regression in several Qwen VL models and a serving KV-cache indexing crash are different concrete problems.

Summary: These issues are not duplicates of one another; they span unrelated regressions in tokenization/model loading, config serialization, multimodal chat templating, model initialization, and serving paths.


No global best issue: the cluster is too mixed to use any one issue as a representative duplicate target.

No single canonical issue fits this cluster: the items describe different bugs and regressions across unrelated code paths, so forcing one canonical issue would be misleading.

issue:42617 — Not able to run 3d_parallel.py

Different failure modes and scopes: a script execution problem versus tensor-parallel load OOM for a specific model.

issue:43575 — Load `Qwen2-57B-A14B-Instruct` with tp lead to OOM

Both are loading-time issues, but the concrete errors and models differ completely (OOM vs KeyError).

issue:43742 — Key error when loading facebook/MobileLLM-125M

issue:44933 — Nonexistant import from image_utils

An import/export problem is not the same underlying bug as a chat/runtime crash in a model integration.

issue:45362 — Qwen3.5-35B crashes with transformers chat

issue:44792 — Failed test case `test_model_generate_images` for janus model

Different models, different code paths, and different symptoms; one is a test failure for image generation, the other a flash-attention generation bug.

issue:44977 — Qwen3.5 cannot generate normally with flash-attention

issue:43065 — Dummy `nn.Conv2d` in `Sam3PixelDecoder`

These concern different multimodal components and different mismatches; they do not appear to be the same fixable defect.

issue:44610 — [BUG] OmDet-Turbo processor produces 640px inputs but the model expects 224px

issue:41720 — Qwen3 with auto device mapping fails due to cudaErrorAssert on A800

A CUDA assert during device mapping is unrelated to a category-count limit error.

issue:45245 — RuntimeError: number of categories cannot exceed 2^24

issue:44464 — Chunked generation produces inconsistent outputs when using compiled forward

One is a compiled-forward/chunked-generation correctness issue; the other is a configuration mismatch in eos token IDs.

issue:44871 — [Gemma-3] Inconsistent eos_token_id configuration: tokenizer has single value (1) but model.config has list [1, 106]

Both involve large-model loading, but the concrete problem is different: device-mapping CUDA assert versus tensor-parallel OOM.

issue:32090 — [Error] with Trainer: TypeError: Unsupported types (<class 'NoneType'>) passed to `_gpu_broadcast_one`.

A distributed training broadcast TypeError is unrelated to embedding reinitialization after resize.

issue:35141 — resizing token embeddings causes output embedding to be reinitialized in `post_init` when `tie_word_embedding` is False

issue:43827 — Summarization/Translation docs still reference pipeline() after v5 pipeline removals

The first is stale documentation; the second is a runtime/model-behavior bug. They do not share the same underlying change or fix.

issue:43994 — google/siglip2-base-patch16-224 produces nonsensical results with AutoModel and pipeline

Summary: The cluster is heterogeneous and spans unrelated issues in training, model loading, multimodal processing, generation, docs, and device placement. None of the soft-edge pairs look like true duplicates or the same underlying fix.


No global best issue for deduplication purposes; none of the issues subsumes the others or serves as a representative duplicate target.

No single canonical issue fits the cluster; the items describe distinct bugs with different code paths, models, and failure modes.

Different problems: one is about non-persistent buffers becoming junk, the other about GPTNeoXConfig losing rotary_pct on reload.

Unrelated subsystems and failures: metric gathering vs Apple Silicon allocator warmup TypeError.

Both are audio/data-related at a broad level, but the reported bugs are different: a generic data placeholder issue vs _torch_extract_fbank_features() behavior.

Both concern model persistence/changes, but one is about LoRA merging with extended vocab and tied embeddings; the other is about incorrect visual encoder keys in save_pretrained.

Both are loading failures, but for different artifacts and causes: a local dataset in an example script vs a specific multimodal model with latest transformers.

Different concrete bugs: CLIPVisionModel not returning hidden states vs recurrent states resetting with cache and seq_len>1.

Both are import/runtime errors, but one concerns compiled Python files generally while the other is a Python 3.13 torch.jit parsing/IndentationError regression.

Completely different failures: SwitchTransformers sparse layer creation vs DebertaV2Model import parsing on Python 3.13.

Different model/runtime paths: Qwen3OmniMoe video input ValueError vs transformers serve KV-cache continuation indexing crash.

Both are Python-version compatibility issues, but the specific breakages differ: one claims 5.1.0 fails on Python 3.9+, the other is a Python 3.13 torch.jit parsing problem.

Summary: This cluster is heterogeneous: the issues span unrelated bugs in configs, data loading, serving, model serialization, and Python-version-specific failures. None of the soft pairs look like the same underlying problem, so no duplicate merge is warranted.


45372 is the best single representative issue in this set because it is recent, clearly user-facing, and describes a concrete regression with a specific import failure. That said, it is not a duplicate of the others.

There is no strong canonical duplicate here because the items span different code paths and failure modes. If one issue must be used as a representative anchor, 45372 is the most actionable recent regression with concrete impact and reproduction details.

Both are loading-related, but one is an unrecognized model/config problem and the other is a Gemma 4 processor import dependency breakage. Different root causes and fixes.

Both concern persistence/reload behavior, but one is about junk-filled non-persistent buffers and the other is about rotary_pct not round-tripping in config. Distinct bugs.

Different example scripts and different failures: multi-label classification returns empty results vs local dataset loading in image classification. Not the same underlying bug.

PatchTSMixer post_init validation and ModelOutput key assignment are unrelated implementation bugs.

A pipeline processor-selection bug and a notebook syntax error are unrelated.

One is a regression around processor.tokenizer/image passing, the other is resize_token_embeddings not updating Gemma 4 embedding/output weights. Different code paths.

Both are import errors, but one is for a specific Qwen2.5-VL processor class and the other is for the top-level AutoImageProcessor API. Not the same bug.

Mistral3 lm_head tying and Qwen3.5 save_pretrained checkpoint serialization are separate model-specific regressions.

SwitchTransformers sparse-layer construction and local dataset loading in an example script are unrelated.

SigLIP2 docs example errors and image-classification dataset loading are different issues, with different repros and fixes.

Summary: This cluster is heterogeneous: it mixes unrelated import failures, model/config regressions, docs/script problems, and pipeline bugs. None of the soft pairs looks like the same underlying issue, so no duplicate merge is justified.


Both are ImportError reports, but they reference different missing symbols in different modules and likely separate regressions.

One is an eos_token_id config inconsistency; the other is a categorical cardinality runtime limit. Different failure modes and code paths.

Different models and different bugs: zero probabilities in SigLIP2 vs a dummy Conv2d in Sam3 pixel decoder.

Both concern config handling, but one is default-init behavior for None while the other is a missing field being dropped by strict serialization; not the same concrete bug.

Tokenizer crashes on NER/padding versus server KV-cache continuation tensor indexing; unrelated code paths.

The first is specifically about TensorFlow not being included with the torch extra; the second is a vague dependency issue without the same concrete symptom or fix.

A notebook syntax error and an audio feature-extraction bug are unrelated.

Both touch import behavior, but one reports slow imports while the other is about unsafe sys.modules access; not enough to treat as the same bug.

Both mention flex_attention/PyTorch versioning, but they target different problems: compiled flex_attention failing on torch>=2.9 vs a version check for AuxRequest import.

Summary: No soft pair appears to be the same underlying bug or change; the set is a mix of unrelated import errors, model/config issues, tokenizer/server crashes, and performance/compatibility reports. I would not merge any of the candidate pairs.


Both mention serialization/reload behavior, but one is about tied lm_head.weight saving and the other about GPTNeoX rotary_pct being dropped on reload; different code paths and symptoms.

Both are test failures, but they target different models and failures (GraniteSpeech device override CI vs janus image generation). Not the same bug.

Same model family, but one is about saving dequantized Ministral/Devstral models and the other about FlashAttention inference failure in Ministral-3; distinct problems.

Different models and scenarios: GLM-4.7-Flash loading in unit tests vs janus image-generation test failure.

Both are version regressions in Qwen models, but one is Qwen3VL MoE loading and the other is Qwen3.5 save_pretrained checkpoint corruption; not the same concrete bug.

Different behaviors and models: processor.tokenizer/image-handling regression vs t5gemma resize_token_embeddings not updating decoder.embed_tokens.

A notebook syntax error and an AddedToken keyword-argument TypeError are unrelated.

Both involve config loading, but one is missing fields with trust_remote_code and the other is an unrecognized model lacking model_type; different root causes.

Generation cache/chunked_prefill regression is unrelated to ANSI codes emitted by loading_report outside a terminal.

GraniteSpeech CI test flake and Phi-4-multimodal loading failure are different models and failure modes.

Summary: This cluster is not a duplicate set: the issues span unrelated model families and distinct failure modes (loading, saving, config, tests, and notebook/doc problems). No soft edge pair looks like the same underlying bug or change.


One is a Python/version-compatibility failure for Transformers 5.1.0; the other is a model-loading regression for physical-intelligence/fast. Different failure modes and subsystems.

Both involve import errors, but for different symbols and modules: Qwen2.5-VL export vs a non-existent image_utils import. Not the same underlying bug.

SGD optimizer arguments not being applied is unrelated to the Qwen3.5 tie_word_embeddings warning during LoRA fine-tuning.

Both mention Qwen models, but one is a load-time MoE model breakage and the other is a save_pretrained key regression for visual encoder state. Different code paths.

Generic dependency trouble is too broad; these describe different issues, one about Python support and one about a transformers dependency failure.

Unrecognized model/config.json handling is unrelated to PIL image processors incorrectly requiring torchvision.

Load-balancing loss gating with router logits is a model-loss computation bug, while mm_token_type_ids defaults for text-only fine-tuning is a separate input-default issue.

A BeitImageProcessorFast label-reduction bug is unrelated to a documentation mismatch about return_all_scores in TextClassificationPipeline.

Both are tokenizer-related, but one is AutoTokenizer ignoring tokenizer.json and the other is a crash in fix_mistral_regex during Mistral tokenizer patching. Different failures.

Summary: All soft pairs look like false positives: they share broad themes like loading, tokenizers, or Qwen, but each points to a different bug, model family, or code path. No pull requests are present, and there is no single canonical issue among these unrelated reports.


issue:44521 is the strongest standalone issue in the set because it has the most inbound references and discussion activity, and it describes a concrete, reproducible multimodal bug; however, it does not represent the rest of the cluster well enough to serve as a true duplicate canonical.

Both involve multimodal/video behavior, but they target different failures in different code paths: assistant mask generation vs rope/position-id scaling. Not the same underlying bug.

Different models and different defects: Blip2 dtype loading vs Qwen3.5 checkpoint saving. No shared concrete fix.

Docs mismatch for TextClassificationPipeline is unrelated to an unrecognized-model/config loading error for Olmo-Hybrid-Instruct. Different subsystems and symptoms.

Both mention import errors, but one is a specific missing symbol and the other is a broad post-upgrade breakage report. Too generic to treat as the same bug.

Both are model-load failures, but for different models and likely different compatibility issues. Not enough evidence of one shared root cause.

Tokenizer save_pretrained writing the wrong tokenizer_class is not the same as AutoTokenizer ignoring tokenizer.json from a repo. Related area, but different concrete bugs.

A video-input regression for specific Qwen VL models is unrelated to a categories-limit runtime error. Different failure mode and code path.

A vague dependency issue is not the same as a specific MIN_PEFT_VERSION bump to an unreleased PyPI version. No clear duplicate relationship.

Video-classification pipeline processor lookup and a vague 'ADD THE DATA' report do not describe the same bug or change.

Summary: The cluster is heterogeneous: it mixes unrelated bug reports about multimodal chat templates, tokenizer serialization, model loading/import errors, and versioning regressions. None of the soft pairs looks like a safe duplicate merge.


No issue is a good global canonical candidate. If forced, the broadest/highest-level report is the remote_code regression, but it still does not subsume the other bugs.

No single issue cleanly represents the set because the items span distinct root causes and subsystems; treating any one as canonical would incorrectly merge unrelated reports.

Different failures: one is a video-classification processor lookup bug, the other is a broad remote_code regression. Same general area, not the same concrete defect.

Both concern tokenizers, but one is about AutoTokenizer ignoring repository tokenizer.json and the other is Deepseek tokenization correctness in v5; different code paths and likely different fixes.

Unrelated problems: Python version compatibility vs an unreleased PEFT version bump. No shared underlying bug.

Both are config-related, but one is multimodal default sub-config initialization and the other is a missing pad_token_id attribute on Llama4Config; different root causes.

Completely different symptoms and subsystems: SigLIP2 zero probabilities vs SAM3Video CUDA OOM.

Both involve loading models from config, but one is missing model_type and the other is strict config handling for granite_speech; not the same bug.

Processor/tokenizer regression vs Deepseek tokenizer output bug. They touch tokenizers but not the same concrete failure.

Different models and load failures (Qwen3VL MoE vs GLM-4.7-Flash). Similar class of issue, not the same underlying change.

Both mention docs, but one is a SigLIP2 example/model mismatch with quantization failure and the other is TextClassificationPipeline return_all_scores docs drift. Different topics and fixes.

Summary: This set is not a true duplicate cluster; it contains several unrelated Transformers issues that only share broad thematic similarity (tokenizers, model loading, configs, docs, regressions). None of the soft edges look like the same underlying bug/change.


Issue 44291 is the strongest representative overall because it is narrowly scoped, reproducible, and clearly tied to a specific regression rather than a vague or model-specific failure.

Issue 44291 is the most concrete and triage-ready report: it has a clear exception, a version boundary (transformers >= 5.0.0rc0), and a specific loading path (`init_empty_weights`) that makes it a good anchor if one issue must represent this loose set.

Both involve attention backends, but one is a Ministral-3 FlashAttention failure in Transformers v5 RC and the other is a compiled flex_attention failure on torch >= 2.9. Different backend, version trigger, and code path.

Tokenizer loading in `AutoTokenizer.from_pretrained` is unrelated to the ESM2 workflow breakage; these point to different components and symptoms.

A video processor shape bug and a vague dependency issue are not the same underlying problem; there is no shared concrete code path.

Notebook syntax error is unrelated to the `init_empty_weights` TypeError when loading a model in v5 RC.

Both are quantization-adjacent, but one is about saving a quantized model and the other about loading a dequantized model with `device_map=auto` and CPU/GPU offload; different operations and failure modes.

These are different model-specific failures: Bamba-9B-v2 with torch.compile/SDPA versus Phi-4 multimodal loading on latest transformers.

`ADD THE DATA` is not a technical bug report and does not match the chat-template tool-call crash.

Dummy Conv2d in Sam3PixelDecoder and Perceiver image classification interpolation are different model implementations and different bug classes.

A missing `rms_norm_eps` precision/config issue is unrelated to the `torch.split()` return-value bug in GlmMoeDsaIndexer; only superficial ML terminology overlaps.

Summary: This set does not look like a single duplicate cluster. The soft-similarity pairs mostly share only broad transformer/model-loading themes, but they involve different models, code paths, and failure modes, so none should be merged as duplicates.


`issue:44521` is the best representative because it is a specific regression with clear reproduction and the strongest evidence/activity among the issues.

`issue:44521` is the most central and actionable issue in the set: it is concrete, open, well-discussed, and has the most inbound references.

Both involve `apply_chat_template`, but one is about audio sampling-rate defaults and the other about all-zero assistant masks for multimodal inputs; different bugs and code paths.

Both touch weights/embeddings, but one is inconsistent `lm_head.weight` serialization and the other is `resize_token_embeddings` not updating Gemma-4 embedding/output layers; not the same defect.

Both are v5 tokenization/model-output regressions, but CamemBERT masked-LM predictions and DeepSeek tokenizer output are separate model-specific issues.

`Different tokenization...` and `ADD THE DATA` do not describe the same bug or change; no meaningful overlap.

`GenerationMixin` cache missing during `chunked_prefill` and CLIPVision hidden-states being `None` are unrelated code paths and symptoms.

One is a model-specific Flash Attention 2 support error, the other is a generic Flash Attention 2 issue; too broad and not the same concrete fix.

`processor.save_pretrained(...) missing files` and a slowdown when filtering data after upgrade are unrelated problems.

Missing files in `processor.save_pretrained` is a packaging/persistence bug; `gemma-4 zero3 from_pretrained` is a different loading/configuration issue.

TensorFlow omitted from the `torch` extra and Python 3.9/3.10 compatibility are different installation/runtime problems.

Summary: This cluster is mostly heterogeneous; the soft pairs share surface terms but do not look like the same underlying bug/change. No PRs are present. The strongest single issue candidate is the open `apply_chat_template` multimodal bug, but it is not a duplicate of the other items.


No single issue cleanly represents the cluster because the items span unrelated failure modes and subsystems.

No pull requests are present in this cluster.

Docs about return_all_scores behavior do not match a SigLIP2 model/pipeline correctness issue.

A processor.tokenizer regression is unrelated to a Sam3Video CUDA OOM report.

One is a missing model import; the other is a Python version compatibility problem.

Import latency is not the same as a video model out-of-memory failure.

A missing SAM3 attribute and a notebook syntax error are unrelated issues.

Missing Llama4Config.pad_token_id is a different code-path than glm-4v-9b loading failure.

A storage-object lookup error is not the same as Qwen3.5 chat crashing.

torch.compile attention-kernel divergence is unrelated to ANSI code emission in loading_report.

Video-classification image-processor lookup and _torch_extract_fbank_features() involve different pipeline paths.

An AutoImageProcessor import error is not the same as a package Python-version support issue.

Pipeline documentation mismatch and slow import behavior are distinct problems.

Summary: This cluster is heterogeneous: import errors, model-loading issues, pipeline regressions, docs bugs, kernel divergence, and notebook/runtime errors. None of the soft pairs look like the same underlying bug or a plausible single PR fix.


45356 is the best representative issue because it is narrowly scoped, reproducible, and describes a specific regression rather than a vague symptom or unrelated maintenance issue.

Issue 45356 is the strongest self-contained bug report in the group: it has a clear regression window, a specific affected model family, and a concrete tokenizer failure mode.

Tokenizer_config class mismatch and Gemma-4 zero3 from_pretrained are different failures affecting different code paths.

AutoTokenizer ignoring tokenizer.json is not the same bug as the Kimi-K2.5 codec/regex regression.

MLukeTokenizer AttributeError and slow data filtering after upgrade are unrelated problems.

ANSI codes in loading_report and a wrong docstring for position_ids are unrelated documentation/logging issues.

Both mention Qwen MoE, but one is LoRA compatibility across versions and the other is Qwen3VL MoE loading; not the same concrete bug.

Import latency and missing model_type handling are different issues with different causes.

Missing Qwen2_5_VLImageProcessor import and missing HybridCache import are distinct symbol/export problems.

A position_ids buffer read error and a Gemma-4 zero3 from_pretrained crash do not share the same underlying failure.

Sliding_window behavior in Qwen3-MoE is a different issue from Qwen3.5-35B chat crashes.

Summary: The set is heterogeneous; none of the proposed soft edges look like true duplicates. Most pairs share only a broad model/tokenizer/import theme, but the concrete bugs and code paths differ. No PRs are present.


issue:44987 is the clearest standalone user-facing regression in this set: it has a concrete version boundary (>=5.1.0), a specific failure mode, and a reproducible loading path. It is the best representative of the broader "v5 loading breakage" theme, but not a duplicate of the other issues.

Different bugs: one is BeitImageProcessorFast returning the wrong label reduction result, the other is unsafe sys.modules access in modeling_utils. No shared code path or symptom.

Different model families and failures: SigLIP produces bad outputs via AutoModel/pipeline, while Gemma 4 resize_token_embeddings does not propagate to per-layer/output embeddings.

ANSI escape codes in loading_report when not attached to a terminal is unrelated to GLM-5 RoPE implementation discussion.

Both are v5 regressions, but the concrete problems differ: missing GenerationMixin cache during chunked_prefill versus removal of is_torch_fx_available breaking trust_remote_code models.

Different subsystems and symptoms: MoE load-balancing loss gating on output_router_logits versus a LwDetrImageLoss AMP/CUDA crash.

Unrelated model-specific issues: ESM2 is broken broadly, while Gemma 4 requires mm_token_type_ids defaulting for text-only fine-tuning.

Both mention tokenizers and v5, but one is about decoder type selection in Transformers v5 and the other is Deepseek tokenizer output corruption; different model-specific bugs.

One is a loading failure for physical-intelligence/fast on transformers>=5.1.0; the other is tokenizer error handling that hides protobuf-related decode errors. Different failure points and remedies.

Completely unrelated: Qwen2_5_VLImageProcessor import error versus a deepspeed zero3/BertModel index error.

Summary: The cluster is heterogeneous: the soft pairs share only broad Transformer/v5 or model-loading vocabulary, but they describe distinct bugs, models, and code paths. I would not merge any of the soft pairs as duplicates.


#44779 is the most concrete and reproducible issue in the set, with a narrowly scoped behavior regression and clear expected-vs-actual semantics.

If one issue must represent the cluster, #44779 is the clearest standalone regression report: it names a specific model family, a version boundary (v4 to v5), and a concrete correctness failure in tokenizer behavior.

Both concern config strictness, but they hit different models and different missing-field symptoms; this looks like related theme, not the same underlying bug.

Different code paths and model families: one is FlashAttention failure for Ministral-3, the other is video input handling in Qwen3OmniMoe.

Tokenizer offline cache loading and Gemma4 multi-GPU CUDA OOM are unrelated failures with no shared concrete bug.

One is an import/API exposure problem, the other is a video-classification pipeline processor-selection bug; similar terminology, different underlying issue.

Import-time slowness and Gemma eos_token_id inconsistency are separate concerns with no common code-path bug.

A stale cache crash on second forward pass is not the same as missing default mm_token_type_ids for text-only fine-tuning.

Tokenizer correctness regression and resize_token_embeddings not updating output embeddings are different tokenizer/model-state bugs.

Position_ids buffer access error and a performance regression in data filtering are unrelated symptoms and fixes.

Generation kwargs handling after sync_gpus and ANSI leakage in loading_report are entirely different subsystems.

Summary: The soft pairs are thematically similar but each points to a different failure mode, model, or subsystem. None look like true duplicate bugs or mergeable PR-equivalents.


No single issue is a good global representative because the cluster is not a true duplicate set; the soft links are only superficially similar.

No canonical issue: the items span unrelated problems across SAM3, Qwen, audio processors, import behavior, and docs, and the soft pairs do not describe the same concrete bug.

One is a pipeline docs mismatch about `return_all_scores`; the other is unsafe `sys.modules` access in `modeling_utils`. Different symptoms and code paths.

Both mention SAM3, but one is about a dummy `nn.Conv2d` in the pixel decoder and the other is a model-type/config mismatch warning. Not the same bug.

Audio processor sampling-rate defaults and slow `import transformers` are unrelated issues.

Both involve DeepSpeed, but one is Qwen3 MOE weight conversion during loading and the other is a ZeRO3 deque underflow. Different failures and fix paths.

An unrecognized model/config error is unrelated to unsafe `sys.modules` access.

Different models and different errors: missing `fpn_position_embeddings` in SAM3 Video vs video-processing `ValueError` in Qwen3OmniMoe.

Custom processor/tokenizer regression is unrelated to `resize_token_embeddings` not updating output embeddings.

Both concern import-related behavior, but one is a nonexistent image_utils import and the other is protobuf-decoder error masking in tokenization; not the same bug.

One is a tied-weights warning in Qwen3-Next, the other is a `patchtsmixer` `post_init` policy issue. No shared code-path problem.

Summary: The cluster appears heterogeneous: the soft-linked pairs share broad jargon or subsystem names, but each describes a different bug, warning, or docs issue rather than the same underlying change. No canonical duplicate stands out.


#45278 is the best single issue to anchor the cluster because it is the most general and closest to a cross-cutting upgrade regression. It is still not a true duplicate hub for the other reports.

If a representative issue must be chosen, #45278 is the broadest post-upgrade regression report and the most plausible umbrella item. That said, the cluster does not form a true duplicate set.

Different models and failure modes: a tied-embeddings warning during LoRA fine-tuning vs flash-attention generation failure. Same general area, not the same bug.

Both involve embeddings, but one is about inconsistent lm_head serialization/tied weights and the other about resize_token_embeddings not updating decoder.embed_tokens. Different concrete code paths.

Tokenizer-related issues, but one crashes Whisper word-timestamp decoding on a trailing replacement character while the other is a Kimi-K2.5 codec/warning regression. Not the same bug.

Both touch generation/cache behavior, but one is missing cache in chunked_prefill and the other is a KV-cache continuation indexing crash in serve responses. Different failure points and fixes.

Unrelated topics: BigBirdTokenizer special-token decode behavior vs a Chinese performance regression after upgrade.

AutoTokenizer loading failure for a specific tokenizer is unrelated to a broad slowdown regression.

Both concern generation internals, but one is about _update_model_kwargs_for_generation after sync_gpus and the other about cache missing in chunked_prefill. Not the same underlying defect.

A Qwen3-VL weight-shape mismatch is unrelated to a tied-embeddings warning in Qwen3.5 LoRA fine-tuning.

Different models and different symptoms: FlashAttention failure for Ministral-3 vs device mismatch under CPU offload for bark-small.

Cache-generation bug vs generic import errors after upgrade; too broad and not the same code-path problem.

Custom model initialization breakage in Transformers v5 is not the same as the ESM2-specific broken workflow report.

Summary: The cluster is highly heterogeneous: most items are distinct regression reports across different models, tokenizers, generation paths, and upgrade symptoms. No soft pair looks like the same underlying bug or change, so I would not merge any of the candidate edges.


issue:42673 is the strongest representative of the only clear duplicate thread in this set; the rest of the cluster is too mixed to provide a better global anchor.

issue:42673 is the best canonical issue because issue:43704 is an exact title duplicate of the same Qwen3ForCausalLM VRAM leak bug, and 42673 has much more discussion/context.

Exact same bug and same title: Qwen3ForCausalLM leaking VRAM across multiple dataloader threads.

Both involve save_pretrained, but one is a tokenizer_config mismatch and the other is missing processor files; different failures and likely different fixes.

Gradient checkpointing in eval mode is unrelated to resize_token_embeddings not updating output_embeddings.

One is a missing PyTorch ImportError; the other is a loading hang in sentence transformers. Different underlying problems.

clean_up_tokenization_spaces v5 behavior change is unrelated to the ESM2 breakage report.

Different model behaviors: eval-mode gradient checkpointing versus decoder.embed_tokens not updating after resize_token_embeddings.

Both are Qwen2.5-VL import errors from the transformers package/module export path, and could plausibly be fixed together as the same missing-export issue.

SAM 3 video missing attribute and Qwen2.5-VL video position-id bug are different model-specific issues.

TF32 API settings guidance and InternVL2 meta-initialization incompatibility are not the same bug or change.

Summary: This cluster is largely heterogeneous, with most pairs clearly unrelated. The only strong duplicate is the repeated Qwen3ForCausalLM VRAM leak report, and there is also a plausible duplicate pair in the Qwen2.5-VL import-error reports.


issue:43873 is the best cluster representative because it is general, open, and discussion-heavy, but it should not be treated as a true duplicate anchor for the other issues.

issue:43873 is the most broadly scoped and most referenced item in the set, with active discussion and inbound references; it reads like a central runtime behavior bug rather than a narrow model-specific symptom.

Both are AttributeError crashes, but they affect different code paths: Mistral tokenizer regex patching vs auto-docstring kwargs processing.

One is a GGUF loading regression in from_pretrained; the other is unsafe sys.modules access in modeling_utils. Related import/load surface, but not the same bug.

Both mention missing imports, but one is about AutoImageProcessor export from transformers and the other is a different image_utils import problem.

Both involve missing attributes/fields, but SAM 3 video output and trust_remote_code AutoConfig object truncation are distinct failures.

Different models and symptoms: vitpose breakage vs Gemma 4 token-type defaults for text-only fine-tuning.

Quantized model saving failure and Llama4Config missing pad_token_id are separate configuration/runtime issues.

Both are Qwen3.5-related, but one is a generic from_pretrained error and the other is a specific vision-config field being dropped by @strict.

Both touch Qwen3-VL-Moe/processor flows, but one is a model compatibility bug and the other is missing files on processor.save_pretrained.

Quantization/offloading behavior and incorrect pipeline-parallel LLM output are unrelated code-path problems.

Summary: This cluster is heterogeneous: the soft pairs share superficial error patterns or model-family overlap, but they do not look like the same underlying bug/change. No PRs are present. If a single issue must represent the cluster, the broadest and most connected report is the quantization/offloading issue.


issue:44164 is the most concrete, reusable bug report in the serialization/save-load subset, with a clear failure mode and broader applicability than the model-specific reports.

No single canonical issue fits the whole cluster; the items are semantically heterogeneous. If forced to pick a representative anchor, it would only be for the save/from_pretrained subset, not the full cluster.

Both are bugs, but one is an unspecific report and the other is a ZeRO3/from_pretrained failure for Gemma-4; no shared code-path evidence.

SAM 3 Video missing attribute and add-new-model-like tokenizer mapping failure are different model/tooling bugs, not one underlying change.

Both mention Qwen3.5, but one is a chat crash and the other is a config field dropped by @strict; different failure modes.

Generation kwargs update after sync_gpus and attention-kernel divergence under torch.compile are separate generation/compile issues.

Import errors for different symbols; they may share a packaging theme, but not the same missing export or fix.

FP8 save_pretrained for MoE vs Qwen3.5 MoE from_pretrained error are related only at a very high level; not one concrete bug.

Accuracy degradation in FineGrainedFP8 is not the same as a save_pretrained failure for FP8 MoE models.

Both involve serialization, but quantized model saving failure and extra_state handling are distinct defects.

Missing pad_token_id in Llama4Config and missing deepstack_visual_indexes in Qwen3_5MoeVisionConfig are separate model-specific schema bugs.

Both are import errors, but for different APIs and module paths; no evidence of the same broken export.

Both are from_pretrained bugs, but for different models and likely different root causes, so they should not be merged.

Summary: This cluster is not a true duplicate set: it mixes unrelated import/export regressions, generation internals, model-specific config bugs, and serialization/save-load failures. No soft edge pair is the same underlying bug/change closely enough to merge.


44038 is the most plausible representative only because it is a broad v5/model-regression report; however, the cluster is too heterogeneous for a strong canonical issue.

Issue 44038 is the least specific and most cluster-like of the set, so it is the best available anchor, but it is still not a true duplicate center because the surrounding issues are mostly unrelated.

Different bugs: MIMI encoder padding-mask inconsistency vs MoE load-balancing loss gating on router logits.

Config initialization from None is unrelated to ModelOutput key assignment when a prior value was None.

Generation kwargs after sync_gpus is a generation-state bug; the Janus test failure is model-specific and not the same code path.

Stuck loading for Sentence Transformers is unrelated to output embedding reinitialization after resizing token embeddings.

Missing cache in chunked_prefill and attention-kernel branching under torch.compile are different generation/runtime problems.

Both mention Qwen/MoE, but one is a vague Qwen3-VL-Moe issue and the other is a from_pretrained regression in Qwen3.5-Moe; not the same defect.

Multimodal config defaults being reinitialized has nothing to do with Whisper timestamp decoding crashing on a trailing replacement character.

Both are v5 tokenizer regressions, but clean_up_tokenization_spaces and add_special_tokens/BOS-EOS handling are separate behaviors and code paths.

Torch extra dependency/backend selection is unrelated to a missing image_utils import.

Summary: The cluster is very noisy: these issues span unrelated bugs across generation, tokenization, multimodal configs, model outputs, and backend packaging. None of the soft pairs look like the same underlying change or defect, so I would not merge any of them.


Different failures in different import paths: sentence-transformers hits a NameError for `nn`, while Qwen2.5-VL is a missing top-level model export/import error.

Python version compatibility regression is unrelated to a nonexistent `image_utils` import; different subsystems and symptoms.

`torch.compile` attention-kernel divergence is a runtime kernel branching bug, while this is only a wrong docstring for `position_ids`.

Model loading failure for Phi-4-multimodal is unrelated to a DeepSpeed ZeRO3 deque pop error; different code paths and layers.

Both are Qwen-related, but one is a tied-weights warning bug and the other is a missing `_tp_plan` for tensor parallelism; not the same defect.

Serve KV-cache continuation indexing crash and auto_docstring crashing under `from __future__ import annotations` are unrelated bugs in different components.

Both involve tokenizers, but one is AutoTokenizer loading in v5 and the other is `add_special_tokens=True` failing to add BOS/EOS for a specific tokenizer; distinct behaviors.

General `clean_up_tokenization_spaces` behavior change and Kimi-K2.5 codec-handling regression are separate tokenizer regressions with different triggers.

Embedding dtype mismatch between train/eval has no overlap with a ZeRO3 deque underflow; entirely different training/runtime issues.

Summary: The items are a loose similarity cluster, but they cover many unrelated problems: tokenizer regressions, model-loading/import errors, distributed-training bugs, a docstring issue, and a serve/runtime crash. None of the soft-edge pairs look like true duplicates of the same underlying bug or change.


issue:43874 is the most concrete and self-contained bug report: it names a specific AttributeError, a specific class, and a specific failing code path, so it is the best representative issue among this set.

Both involve from_pretrained/save_pretrained style regressions, but they describe different failures on different models and code paths.

Both are multimodal/vision-related compatibility bugs, but one is a missing method on an image processor and the other is remote-code/meta-init incompatibility; not the same bug.

Both mention tokenizer behavior changes in v5, but one is about clean_up_tokenization_spaces while the other is a DeepSeek-specific incorrect-tokenization regression.

Completely different subsystems and failures: PatchTSMixer post_init validation versus MoE router load-balancing loss.

Both are v4/v5 output-behavior regressions, but the concrete bugs differ: cleanup-space handling versus Pegasus generation output drift.

Unrelated failures: DeepSpeed ZeRO3 deque crash versus Gemma4 embedding resize not propagating to tied/output embeddings.

Different import-time/error-handling problems; one is missing AutoImageProcessor import, the other is protobuf-related tokenizer exception masking.

Both are v5 tokenizer/processor regressions, but the actual breakage is different: cleanup-space defaults versus processor.tokenizer/image-passing behavior.

Distinct bugs in different areas: Phi4MultimodalConfig default initialization versus non-persistent buffer initialization in v5.0.0.

No shared underlying code path: one is an unsupported feature-arg in a feature extractor, the other is an auto-docstring crash under future annotations.

Summary: These are mostly unrelated bug reports sharing broad Transformer/v5/tokenizer themes. None of the soft pairs look like the same underlying issue or a mergeable duplicate PR case, so I would reject all soft edges.


Issue 44222 is the best representative issue overall: it is concrete, centrally connected to several nearby serialization-related candidates, and is more specific than the broader, unrelated issues in the list.

Issue 44222 is the closest cluster anchor because it sits in the densest soft-similarity neighborhood and represents the save_pretrained/serialization theme, even though the set is not a true duplicate group.

Both are tokenizer-related, but one is a specific AutoTokenizer failure while the other is about protobuf-related error handling during tokenizer decode. Different code paths and fixes.

Both mention save_pretrained, but one is FP8 model/moe saving and the other is tokenizer_config metadata mismatch. Not the same bug.

These are different save_pretrained concerns: FP8/moe save behavior versus a question about the default max_shard_size. Not duplicate bugs.

Processor.save_pretrained missing files and FP8 save_pretrained behavior are separate serialization problems affecting different components.

An accuracy issue in FineGrainedFP8 is unrelated to missing files from processor.save_pretrained.

Same broad DSA/indexer area, but one is about torch.split return values and the other about a missing ReLU. Distinct bugs and fixes.

Tokenizer loading in v5 and Qwen3.5 flash-attention generation are unrelated failures.

One is a /v1/chat/completions request handling problem; the other is a Gemma4Processor missing _tokenizer crash. Different underlying issues.

Both are import errors, but one is missing transformers.onnx and the other is missing PyTorch. Different dependency/entrypoint problems.

GlmMoeDsaIndexer torch.split return semantics and InternVL2 v5 meta-initialization incompatibility are not the same bug.

apply_chat_template batching with padding=False and auto_docstring future-annotations AttributeError are unrelated failures.

Summary: The cluster is heterogeneous: the soft-linked pairs mostly share broad themes like tokenizer loading, save_pretrained, or indexer behavior, but they do not look like the same underlying bug or fix. I would not merge any of the proposed soft pairs.


#43723 is the strongest single issue to anchor triage because it is general, model-agnostic, and aligned with the repeated loading/serialization regressions seen in nearby reports.

issue #43723 is the broadest and most central report in the tokenizer-loading subset: it describes a generic AutoTokenizer.from_pretrained failure in v5, which makes it the best umbrella representative for the cluster's closest shared theme.

Both involve model/config loading behavior, but one is a tokenizer save metadata mismatch and the other is a Qwen3.5 from_pretrained error; they do not look like the same bug or patch.

Both are config-default/reload problems, but they affect different models, different fields, and different code paths.

CPU offload device mismatch for bark-small is unrelated to processor.save_pretrained missing files.

Both are tokenizer/model loading complaints, but one is a generic AutoTokenizer v5 failure and the other is a Gemma-3 eos_token_id inconsistency; no clear shared root cause.

These concern different subsystems: MLA q_a_layernorm precision/config handling versus InternVL2 meta-initialization compatibility.

Both mention tokenizer loading in v5, but the reports are model-specific and do not establish the same underlying failure mode.

A docstring typo is not the same as a GLM-5 RoPE implementation discussion.

FastSpeech2ConformerTokenizer loading failure may be adjacent to v5 tokenizer regressions, but there is not enough evidence that it is the same concrete bug as the generic AutoTokenizer failure.

Both concern Qwen3.5/vision-multimodal behavior, but one is a from_pretrained/meta-init incompatibility and the other is an incorrect save_pretrained key regression; they are not the same issue.

Sentence Transformers loading hangs and zero probabilities in siglip2 are unrelated symptoms affecting different models and code paths.

Summary: This cluster is mostly a loose collection of unrelated Transformers bugs. The closest theme is tokenizer/config serialization and loading regressions in v5, but the soft pairs are not concrete duplicates.


If one issue must be used as the anchor, issue:44704 is the clearest actionable report with a specific failure mode and likely fix path; the rest are too heterogeneous to serve as a shared canonical.

No pull requests are present in the cluster.

No single canonical issue fits the full set: the titles and problem statements span multiple unrelated code paths rather than one duplicated bug.

Both are bug reports, but one is tokenizer cache/offline loading and the other is quantized model saving; different code paths and symptoms.

Both mention saving/serialization, but one is about reading position_ids after registering a buffer and the other is missing files on processor.save_pretrained; not the same bug.

Both are import errors, but for different symbols and likely different model/processor registrations; not the same missing-export issue.

Both relate to model behavior, but FineGrainedFP8 accuracy and Qwen3.5-Moe from_pretrained failure are distinct issues with different failure modes.

One is the v5 env command breaking, the other is a NameError when importing sentence-transformers; separate commands/code paths.

The first is a runtime bug in torch.split usage, while the second is a docstring problem for position_ids; documentation vs code bug.

One reports tokenization changes across versions, the other all-zero assistant masks for multimodal chat templates; different behavior and subsystems.

Both involve from_pretrained/metadata handling, but one is a mapping-name None edge case and the other is kwargs not forwarded to cached_file; related area, not same defect.

Offline tokenizer cache loading and wrong tokenizer decoder type are separate tokenizer regressions with different triggers and fixes.

FineGrainedFP8 accuracy regression and Qwen3-VL-Moe bug are different model/precision issues; too broad to be duplicates.

Summary: These issues are mostly unrelated across different bugs in transformers (imports, tokenizers, quantization, processor save/load, and multimodal behavior). None of the soft pairs look like the same underlying problem, so no duplicate merges should be accepted.