If your model is not in the above list, we will try to automatically convert the model using
[as_classification_model][vllm.model_executor.models.adapters.as_classification_model]. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.
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@@ -451,12 +452,12 @@ If your model is not in the above list, we will try to automatically convert the
| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | |
| `Qwen3ForSequenceClassification` | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. | |
| `RobertaForSequenceClassification` | RoBERTa-based | `cross-encoder/quora-roberta-base`, etc. | |
| `XLMRobertaForSequenceClassification` | XLM-RoBERTa-based | `BAAI/bge-reranker-v2-m3`, etc. | |
!!! note
Load the official original `Qwen3 Reranker` by using the following command. More information can be found at: <gh-file:examples/offline_inference/qwen3_reranker.py>.
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@@ -542,7 +543,7 @@ Specified using `--task generate`.
| `LlavaOnevisionForConditionalGeneration` | LLaVA-Onevision | T + I<sup>+</sup> + V<sup>+</sup> | `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc. | | ✅︎ | ✅︎ |
vLLM V1 currently excludes model architectures with the `SupportsV0Only` protocol,
and the majority fall into the following categories:
vLLM V1 currently excludes model architectures with the `SupportsV0Only` protocol.
!!! tip
This corresponds to the V1 column in our [list of supported models][supported-models].
See below for the status of models that are still not yet supported in V1.
#### Embedding Models
**Embedding Models**
The initial support will be provided by [PR #16188](https://github.com/vllm-project/vllm/pull/16188).
Later, we will consider using [hidden states processor](https://github.com/vllm-project/vllm/issues/12249),
which is based on [global logits processor](https://github.com/vllm-project/vllm/pull/13360)
to enable simultaneous generation and embedding using the same engine instance in V1.
**Mamba Models**
#### Mamba Models
Models using selective state-space mechanisms instead of standard transformer attention (e.g., `MambaForCausalLM`, `JambaForCausalLM`)
will be supported via [PR #19327](https://github.com/vllm-project/vllm/pull/19327).
**Encoder-Decoder Models**
vLLM V1 is currently optimized for decoder-only transformers.
Models requiring cross-attention between separate encoder and decoder are not yet supported (e.g., `BartForConditionalGeneration`, `MllamaForConditionalGeneration`).
#### Encoder-Decoder Models
For a complete list of supported models, see the [list of supported models](https://docs.vllm.ai/en/latest/models/supported_models.html).
Models requiring cross-attention between separate encoder and decoder (e.g., `BartForConditionalGeneration`, `MllamaForConditionalGeneration`)
DarkLight1337 <tlleungac@connect.ust.hk>