[Model] Support Qwen3ForSequenceClassification for Qwen3-Embed Model (#7957)

docs/supported_models/embedding_models.md

-# Embedding Models
-
-SGLang provides robust support for embedding models by integrating efficient serving mechanisms with its flexible programming interface. This integration allows for streamlined handling of embedding tasks, facilitating faster and more accurate retrieval and semantic search operations. SGLang's architecture enables better resource utilization and reduced latency in embedding model deployment.
-
-```{important}
-They are executed with `--is-embedding` and some may require `--trust-remote-code`
-```
-
-## Example Launch Command
-
-```shell
-python3 -m sglang.launch_server \
-  --model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \
-  --is-embedding \
-  --host 0.0.0.0 \
-  --chat-template gme-qwen2-vl \
-  --port 30000
-```
-## Example Client Request
-```python
-import requests
-
-url = "http://127.0.0.1:30000"
-
-text_input = "Represent this image in embedding space."
-image_path = "https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild/resolve/main/images/023.jpg"
-
-payload = {
-    "model": "gme-qwen2-vl",
-    "input": [
-        {
-            "text": text_input
-        },
-        {
-            "image": image_path
-        }
-    ],
-}
-
-response = requests.post(url + "/v1/embeddings", json=payload).json()
-
-print("Embeddings:", [x.get("embedding") for x in response.get("data", [])])
-```
-
-
-## Supported models
-
-| Model Family (Embedding)                        | Example HuggingFace Identifier                | Chat Template | Description                                                                                                                          |
-|-------------------------------------------------|-----------------------------------------------|---------------|--------------------------------------------------------------------------------------------------------------------------------------|
-| **Llama/Mistral based (E5EmbeddingModel)**      | `intfloat/e5-mistral-7b-instruct`             | N/A           | Mistral/Llama-based embedding model fine‑tuned for high‑quality text embeddings (top‑ranked on the MTEB benchmark).                   |
-| **GTE (QwenEmbeddingModel)**                    | `Alibaba-NLP/gte-Qwen2-7B-instruct`           | N/A           | Alibaba’s general text embedding model (7B), achieving state‑of‑the‑art multilingual performance in English and Chinese.             |
-| **GME (MultimodalEmbedModel)**                  | `Alibaba-NLP/gme-Qwen2-VL-2B-Instruct`        | `gme-qwen2-vl`          | Multimodal embedding model (2B) based on Qwen2‑VL, encoding image + text into a unified vector space for cross‑modal retrieval.      |
-| **CLIP (CLIPEmbeddingModel)**                   | `openai/clip-vit-large-patch14-336`           | N/A           | OpenAI’s CLIP model (ViT‑L/14) for embedding images (and text) into a joint latent space; widely used for image similarity search.   |
-| **BGE (BgeEmbeddingModel)**                     | `BAAI/bge-large-en-v1.5`                        | N/A                 | Currently only support `attention-backend`   `triton` and `torch_native`. BAAI's BGE embedding models optimized for retrieval and reranking tasks. |
+# Embedding Models
+
+SGLang provides robust support for embedding models by integrating efficient serving mechanisms with its flexible programming interface. This integration allows for streamlined handling of embedding tasks, facilitating faster and more accurate retrieval and semantic search operations. SGLang's architecture enables better resource utilization and reduced latency in embedding model deployment.
+
+```{important}
+Embedding models are executed with `--is-embedding` flag and some may require `--trust-remote-code`
+```
+
+## Quick Start
+
+### Launch Server
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path Qwen/Qwen3-Embedding-4B \
+  --is-embedding \
+  --host 0.0.0.0 \
+  --port 30000
+```
+
+### Client Request
+
+```python
+import requests
+
+url = "http://127.0.0.1:30000"
+
+payload = {
+    "model": "Qwen/Qwen3-Embedding-4B",
+    "input": "What is the capital of France?",
+    "encoding_format": "float"
+}
+
+response = requests.post(url + "/v1/embeddings", json=payload).json()
+print("Embedding:", response["data"][0]["embedding"])
+```
+
+
+
+## Multimodal Embedding Example
+
+For multimodal models like GME that support both text and images:
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \
+  --is-embedding \
+  --chat-template gme-qwen2-vl \
+  --host 0.0.0.0 \
+  --port 30000
+```
+
+```python
+import requests
+
+url = "http://127.0.0.1:30000"
+
+text_input = "Represent this image in embedding space."
+image_path = "https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild/resolve/main/images/023.jpg"
+
+payload = {
+    "model": "gme-qwen2-vl",
+    "input": [
+        {
+            "text": text_input
+        },
+        {
+            "image": image_path
+        }
+    ],
+}
+
+response = requests.post(url + "/v1/embeddings", json=payload).json()
+
+print("Embeddings:", [x.get("embedding") for x in response.get("data", [])])
+```
+
+## Supported Models
+
+| Model Family                               | Example Model                          | Chat Template | Description                                                                 |
+| ------------------------------------------ | -------------------------------------- | ------------- | --------------------------------------------------------------------------- |
+| **E5 (Llama/Mistral based)**              | `intfloat/e5-mistral-7b-instruct`     | N/A           | High-quality text embeddings based on Mistral/Llama architectures          |
+| **GTE-Qwen2**                             | `Alibaba-NLP/gte-Qwen2-7B-instruct`   | N/A           | Alibaba's text embedding model with multilingual support                   |
+| **Qwen3-Embedding**                       | `Qwen/Qwen3-Embedding-4B`             | N/A           | Latest Qwen3-based text embedding model for semantic representation        |
+| **BGE**                                    | `BAAI/bge-large-en-v1.5`              | N/A           | BAAI's text embeddings (requires `attention-backend` triton/torch_native)  |
+| **GME (Multimodal)**                      | `Alibaba-NLP/gme-Qwen2-VL-2B-Instruct`| `gme-qwen2-vl`| Multimodal embedding for text and image cross-modal tasks                  |
+| **CLIP**                                   | `openai/clip-vit-large-patch14-336`   | N/A           | OpenAI's CLIP for image and text embeddings                                |

python/sglang/srt/configs/model_config.py

@@ -642,6 +642,7 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
         or "InternLM2ForRewardModel" in model_architectures
         or "Qwen2ForRewardModel" in model_architectures
         or "Qwen2ForSequenceClassification" in model_architectures
+        or "Qwen3ForSequenceClassification" in model_architectures
         or "CLIPModel" in model_architectures
         or "BertModel" in model_architectures
         or "Contriever" in model_architectures

python/sglang/srt/models/qwen3_classification.py

+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import Qwen2Config  # Qwen3 uses Qwen2Config
+
+from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.qwen3 import Qwen3ForCausalLM, Qwen3Model
+from sglang.srt.utils import add_prefix
+
+
+class Qwen3ForSequenceClassification(nn.Module):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen3Model(
+            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+        # Use normalize=True for qwen3 embedding based on official implementation
+        # Reference: https://github.com/QwenLM/Qwen3-Embedding/blob/main/examples/qwen3_embedding_transformers.py#L55
+        # Official code: output = F.normalize(output, p=2, dim=1)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+        self.eos_token_id = config.eos_token_id
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert (
+            get_embedding
+        ), "Qwen3ForSequenceClassification is only used for embedding"
+
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        logits = self.score(hidden_states)
+        pooled_logits = self.pooler(logits, forward_batch).embeddings
+
+        return EmbeddingPoolerOutput(pooled_logits)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        # Filter out lm_head weights of Qwen3ForCausalLM
+        filtered_weights = [
+            (name, w) for name, w in weights if not name.startswith("lm_head")
+        ]
+        return Qwen3ForCausalLM.load_weights(self, filtered_weights)
+
+
+EntryClass = [
+    Qwen3ForSequenceClassification,
+]