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[Feature] Add Qwen3-ForcedAligner support via token classification pooling (#35367)

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Signed-off-by: haosdent <haosdent@gmail.com>
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haosdent
2026-03-29 08:27:52 +08:00
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docs/models/pooling_models/token_classify.md
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@@ -15,7 +15,7 @@ Many classification models support both (sequence) classification and token clas
!!! note
Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task (classify) is not
Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task (classify) is not
what you want, you need to manually specify it via `PoolerConfig(task="token_classify")` offline or
`--pooler-config.task token_classify` online.
@@ -29,6 +29,12 @@ Offline: [examples/pooling/token_classify/ner_offline.py](../../../examples/pool
Online: [examples/pooling/token_classify/ner_online.py](../../../examples/pooling/token_classify/ner_online.py)
### Forced Alignment
Forced alignment takes audio and reference text as input and produces word-level timestamps.
Offline: [examples/pooling/token_classify/forced_alignment_offline.py](../../../examples/pooling/token_classify/forced_alignment_offline.py)
### Sparse retrieval (lexical matching)
The BAAI/bge-m3 model leverages token classification for sparse retrieval. For more information, see [this page](specific_models.md#baaibge-m3).
@@ -43,12 +49,25 @@ The BAAI/bge-m3 model leverages token classification for sparse retrieval. For m
| `Qwen3ForTokenClassification`<sup>C</sup> | Qwen3-based | `bd2lcco/Qwen3-0.6B-finetuned` | | |
| `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* |
<sup>C</sup> Automatically converted into a classification model via `--convert classify`. ([details](./README.md#model-conversion))
<sup>C</sup> Automatically converted into a classification model via `--convert classify`. ([details](./README.md#model-conversion))
\* Feature support is the same as that of the original model.
If your model is not in the above list, we will try to automatically convert the model using
[as_seq_cls_model][vllm.model_executor.models.adapters.as_seq_cls_model]. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.
### Multimodal Models
!!! note
For more information about multimodal models inputs, see [this page](../supported_models.md#list-of-multimodal-language-models).
| Architecture | Models | Inputs | Example HF Models | [LoRA](../../features/lora.md) | [PP](../../serving/parallelism_scaling.md) |
| --------------------------------------------- | ------------------- | ----------------- | ------------------------------------------ | ------------------------------ | ------------------------------------------ |
| `Qwen3ASRForcedAlignerForTokenClassification` | Qwen3-ForcedAligner | T + A<sup>+</sup> | `Qwen/Qwen3-ForcedAligner-0.6B` (see note) | | ✅︎ |
!!! note
Forced alignment usage requires `--hf-overrides '{"architectures": ["Qwen3ASRForcedAlignerForTokenClassification"]}'`.
Please refer to [examples/pooling/token_classify/forced_alignment_offline.py](../../../examples/pooling/token_classify/forced_alignment_offline.py).
### As Reward Models
Using token classification models as reward models. For details on reward models, see [Reward Models](reward.md).