[Frontend] Support using chat template as custom score template for reranking models

docs/models/supported_models.md

@@ -489,6 +489,7 @@ These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) A
 | `GteNewModel`<sup>C</sup> | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. |  |  |
 | `ModernBertModel`<sup>C</sup> | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. |  |  |
 | `NomicBertModel`<sup>C</sup> | Nomic BERT | `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc. |  |  |
+| `LlamaBidirectionalModel`<sup>C</sup> | Llama-based with bidirectional attention | `nvidia/llama-nemotron-embed-1b-v2`, etc. | ✅︎ | ✅︎ |
 | `LlamaModel`<sup>C</sup>, `LlamaForCausalLM`<sup>C</sup>, `MistralModel`<sup>C</sup>, etc. | Llama-based | `intfloat/e5-mistral-7b-instruct`, etc. | ✅︎ | ✅︎ |
 | `Qwen2Model`<sup>C</sup>, `Qwen2ForCausalLM`<sup>C</sup> | Qwen2-based | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc. | ✅︎ | ✅︎ |
 | `Qwen3Model`<sup>C</sup>, `Qwen3ForCausalLM`<sup>C</sup> | Qwen3-based | `Qwen/Qwen3-Embedding-0.6B`, etc. | ✅︎ | ✅︎ |
@@ -542,8 +543,9 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
 | `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | | |
 | `GemmaForSequenceClassification` | Gemma-based | `BAAI/bge-reranker-v2-gemma` (see note), etc. | ✅︎ | ✅︎ |
 | `GteNewForSequenceClassification` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-reranker-base`, etc. |  |  |
-| `Qwen2ForSequenceClassification` | Qwen2-based | `mixedbread-ai/mxbai-rerank-base-v2` (see note), etc. | ✅︎ | ✅︎ |
-| `Qwen3ForSequenceClassification` | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. | ✅︎ | ✅︎ |
+| `Qwen2ForSequenceClassification`<sup>C</sup> | Qwen2-based | `mixedbread-ai/mxbai-rerank-base-v2` (see note), etc. | ✅︎ | ✅︎ |
+| `LlamaBidirectionalForSequenceClassification`<sup>C</sup> | Llama-based with bidirectional attention | `nvidia/llama-nemotron-rerank-1b-v2` (see note), etc. | ✅︎ | ✅︎ |
+| `Qwen3ForSequenceClassification`<sup>C</sup> | 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. | | |
 | `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* |
@@ -568,6 +570,11 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
     vllm serve mixedbread-ai/mxbai-rerank-base-v2 --hf_overrides '{"architectures": ["Qwen2ForSequenceClassification"],"classifier_from_token": ["0", "1"], "method": "from_2_way_softmax"}'
     ```
 
+!!! note
+    `nvidia/llama-nemotron-rerank-1b-v2` require a specific prompt format to work correctly.
+
+    Examples : [offline_using_template.py](../../examples/pooling/score/offline_using_template.py) [online_using_template.py](../../examples/pooling/score/online_using_template.py)
+
 !!! note
     Load the official original `Qwen3 Reranker` by using the following command. More information can be found at: [examples/pooling/score/offline_reranker.py](../../examples/pooling/score/offline_reranker.py).
 

docs/serving/openai_compatible_server.md

@@ -669,6 +669,21 @@ You can find the documentation for cross encoder models at [sbert.net](https://w
 
 Code example: [examples/pooling/score/openai_cross_encoder_score.py](../../examples/pooling/score/openai_cross_encoder_score.py)
 
+#### Score Template
+
+Some scoring models require a specific prompt format to work correctly. You can specify a custom score template using the `--chat-template` parameter (see [Chat Template](#chat-template)).
+
+Score templates are supported for **cross-encoder** models only. If you are using an **embedding** model for scoring, vLLM does not apply a score template.
+
+Like chat templates, the score template receives a `messages` list. For scoring, each message has a `role` attribute—either `"query"` or `"document"`. For the usual kind of point-wise cross-encoder, you can expect exactly two messages: one query and one document. To access the query and document content, use Jinja's `selectattr` filter:
+
+- **Query**: `{{ (messages | selectattr("role", "eq", "query") | first).content }}`
+- **Document**: `{{ (messages | selectattr("role", "eq", "document") | first).content }}`
+
+This approach is more robust than index-based access (`messages[0]`, `messages[1]`) because it selects messages by their semantic role. It also avoids assumptions about message ordering if additional message types are added to `messages` in the future.
+
+Example template file: [examples/pooling/score/template/nemotron-rerank.jinja](../../examples/pooling/score/template/nemotron-rerank.jinja)
+
 #### Single inference
 
 You can pass a string to both `text_1` and `text_2`, forming a single sentence pair.

examples/pooling/score/offline_using_template.py

@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+from pathlib import Path
+
+from vllm import LLM
+
+model_name = "nvidia/llama-nemotron-rerank-1b-v2"
+
+# Path to template file
+template_path = Path(__file__).parent / "template" / "nemotron-rerank.jinja"
+chat_template = template_path.read_text()
+
+llm = LLM(model=model_name, runner="pooling", trust_remote_code=True)
+
+query = "how much protein should a female eat?"
+documents = [
+    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments.",
+    "Calorie intake should not fall below 1,200 a day in women or 1,500 a day in men, except under the supervision of a health professional.",
+]
+
+outputs = llm.score(query, documents, chat_template=chat_template)
+
+print("-" * 30)
+print([output.outputs.score for output in outputs])
+print("-" * 30)

examples/pooling/score/online_using_template.py

@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+"""
+Example of using the rerank API with template.
+
+run:
+    vllm serve nvidia/llama-nemotron-rerank-1b-v2 --runner pooling --trust-remote-code --chat-template examples/pooling/score/template/nemotron-rerank.jinja
+"""
+
+import json
+
+import requests
+
+url = "http://127.0.0.1:8000/rerank"
+
+headers = {"accept": "application/json", "Content-Type": "application/json"}
+
+query = "how much protein should a female eat?"
+documents = [
+    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments.",
+    "Calorie intake should not fall below 1,200 a day in women or 1,500 a day in men, except under the supervision of a health professional.",
+]
+
+data = {
+    "model": "nvidia/llama-nemotron-rerank-1b-v2",
+    "query": query,
+    "documents": documents,
+}
+
+
+def main():
+    response = requests.post(url, headers=headers, json=data)
+
+    # Check the response
+    if response.status_code == 200:
+        print("Request successful!")
+        print(json.dumps(response.json(), indent=2))
+    else:
+        print(f"Request failed with status code: {response.status_code}")
+        print(response.text)
+
+
+if __name__ == "__main__":
+    main()

examples/pooling/score/template/nemotron-rerank.jinja

@@ -0,0 +1,3 @@
+question:{{ (messages | selectattr("role", "eq", "query") | first).content }} 
+
+ passage:{{ (messages | selectattr("role", "eq", "document") | first).content }}