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Model: ContextualAI/ctxl-rerank-v2-instruct-multilingual-6b Source: Original Platform
2026-07-27 00:55:11 +08:00
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+{
+    "true_token_id": 0,
+    "false_token_id": null,
+    "module_input_name": "causal_logits"
+}
--- a/Contextual_AI_Brand_Mark_Dark.png
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+---
+library_name: transformers
+license: cc-by-nc-sa-4.0
+pipeline_tag: text-ranking
+tags:
+  - sentence-transformers
+  - cross-encoder
+  - reranker
+---
+
+<div align="center">
+
+# Contextual AI Reranker v2 6B
+
+<img src="Contextual_AI_Brand_Mark_Dark.png" width="10%" alt="Contextual_AI"/>
+
+[![Blog Post](https://img.shields.io/badge/📝%20Blog-ContextualReranker-green)](https://contextual.ai/blog/rerank-v2)
+[![Hugging Face Collection](https://img.shields.io/badge/🤗%20Hugging%20Face-Model%20Collection-yellow)](https://huggingface.co/collections/ContextualAI/contextual-ai-reranker-v2)
+
+</div>
+
+<hr>
+
+## Highlights
+
+Contextual AI's reranker is the **first instruction-following reranker** capable of handling retrieval conflicts and ranking with custom instructions (e.g., prioritizing recent information). It achieves state-of-the-art performance on BEIR and sits on the cost/performance Pareto frontier across:
+
+- Instruction following
+- Question answering  
+- Multilinguality (100+ languages)
+- Product search & recommendation
+- Real-world use cases
+
+<p align="center">
+    <img src="main_benchmark.png" width="1200"/>
+<p>
+
+For detailed benchmarks, see our [blog post](https://contextual.ai/blog/rerank-v2).
+
+## Overview
+
+- **Model Type**: Text Reranking
+- **Supported Languages**: 100+
+- **Parameters**: 6B
+- **Context Length**: up to 32K
+
+## When to Use This Model
+
+Use this reranker when you need to:
+- Re-rank retrieved documents with custom instructions
+- Handle conflicting information in retrieval results
+- Prioritize documents by recency or other criteria
+- Support multilingual search (100+ languages)
+- Process long contexts (up to 32K tokens)
+
+## Quickstart
+
+Each path below uses the same example inputs:
+
+```
+Query: What are the health benefits of exercise?
+Instruction: Prioritize recent medical research
+Documents:
+  - Regular exercise reduces risk of heart disease and improves mental health.
+  - A 2024 study shows exercise enhances cognitive function in older adults.
+  - Ancient Greeks valued physical fitness for military training.
+```
+
+**Expected Output:**
+```
+Score: -2.2969 | Doc: A 2024 study shows exercise enhances cognitive function in older adults.
+Score: -4.6875 | Doc: Regular exercise reduces risk of heart disease and improves mental health.
+Score: -12.3750 | Doc: Ancient Greeks valued physical fitness for military training.
+```
+
+### Using Sentence Transformers
+
+Install Sentence Transformers:
+```bash
+pip install sentence_transformers
+```
+
+```python
+import torch
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder("ContextualAI/ctxl-rerank-v2-instruct-multilingual-6b", model_kwargs={"dtype": torch.bfloat16})
+
+query = "What are the health benefits of exercise?"
+instruction = "Prioritize recent medical research"
+documents = [
+    "Regular exercise reduces risk of heart disease and improves mental health.",
+    "A 2024 study shows exercise enhances cognitive function in older adults.",
+    "Ancient Greeks valued physical fitness for military training.",
+]
+
+pairs = [(query, doc) for doc in documents]
+scores = model.predict(pairs, prompt=instruction)
+print(scores)
+# [ -4.6875   -2.171875 -12.4375  ]
+
+rankings = model.rank(query, documents, prompt=instruction)
+print(rankings)
+# [{'corpus_id': 1, 'score': np.float32(-2.171875)}, {'corpus_id': 0, 'score': np.float32(-4.6875)}, {'corpus_id': 2, 'score': np.float32(-12.4375)}]
+```
+
+The `prompt` argument is optional, you can omit it to score pairs without any custom instruction. Scores are the raw bfloat16 logits at token id 0 at the final position (matching the `Transformers` path below), so higher means more relevant.
+
+### vLLM Usage (Recommended for Production)
+
+Requires `vllm==0.10.0` for NVFP4 or `vllm>=0.8.5` for BF16.
+
+```python
+import os
+os.environ['VLLM_USE_V1'] = '0'  # v1 engine doesn't support logits processor yet
+
+import torch
+from vllm import LLM, SamplingParams
+
+
+def logits_processor(_, scores):
+    """Custom logits processor for vLLM reranking."""
+    index = scores[0].view(torch.uint16)
+    scores = torch.full_like(scores, float("-inf"))
+    scores[index] = 1
+    return scores
+
+
+def format_prompts(query: str, instruction: str, documents: list[str]) -> list[str]:
+    """Format query and documents into prompts for reranking."""
+    if instruction:
+        instruction = f" {instruction}"
+    prompts = []
+    for doc in documents:
+        prompt = f"Check whether a given document contains information helpful to answer the query.\n<Document> {doc}\n<Query> {query}{instruction} ??"
+        prompts.append(prompt)
+    return prompts
+
+
+def infer_w_vllm(model_path: str, query: str, instruction: str, documents: list[str]):
+    model = LLM(
+        model=model_path,
+        gpu_memory_utilization=0.85,
+        max_model_len=8192,
+        dtype="bfloat16",
+        max_logprobs=2,
+        max_num_batched_tokens=262144,
+    )
+    sampling_params = SamplingParams(
+        temperature=0,
+        max_tokens=1,
+        logits_processors=[logits_processor]
+    )
+    prompts = format_prompts(query, instruction, documents)
+
+    outputs = model.generate(prompts, sampling_params, use_tqdm=False)
+
+    # Extract scores and create results
+    results = []
+    for i, output in enumerate(outputs):
+        score = (
+            torch.tensor([output.outputs[0].token_ids[0]], dtype=torch.uint16)
+            .view(torch.bfloat16)
+            .item()
+        )    
+        results.append((score, i, documents[i]))
+
+    # Sort by score (descending)
+    results = sorted(results, key=lambda x: x[0], reverse=True)
+
+    print(f"Query: {query}")
+    print(f"Instruction: {instruction}")
+    for score, doc_id, doc in results:
+        print(f"Score: {score:.4f} | Doc: {doc}")
+
+
+# Example usage
+if __name__ == "__main__":
+    model_path = "ContextualAI/reranker_v2_6b"
+    query = "What are the health benefits of exercise?"
+    instruction = "Prioritize recent medical research"
+    documents = [
+        "Regular exercise reduces risk of heart disease and improves mental health.",
+        "A 2024 study shows exercise enhances cognitive function in older adults.",
+        "Ancient Greeks valued physical fitness for military training."
+    ]
+    
+    infer_w_vllm(model_path, query, instruction, documents)
+```
+
+
+### Transformers Usage (Simpler Setup)
+
+Requires `transformers>=4.51.0` for BF16. Not supported for NVFP4.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+def format_prompts(query: str, instruction: str, documents: list[str]) -> list[str]:
+    """Format query and documents into prompts for reranking."""
+    if instruction:
+        instruction = f" {instruction}"
+    prompts = []
+    for doc in documents:
+        prompt = f"Check whether a given document contains information helpful to answer the query.\n<Document> {doc}\n<Query> {query}{instruction} ??"
+        prompts.append(prompt)
+    return prompts
+
+
+def infer_w_hf(model_path: str, query: str, instruction: str, documents: list[str]):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "left"  # so -1 is the real last token for all prompts
+
+    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype).to(device)
+    model.eval()
+
+    prompts = format_prompts(query, instruction, documents)
+    enc = tokenizer(
+        prompts,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+    )
+    input_ids = enc["input_ids"].to(device)
+    attention_mask = enc["attention_mask"].to(device)
+
+    with torch.no_grad():
+        out = model(input_ids=input_ids, attention_mask=attention_mask)
+
+    next_logits = out.logits[:, -1, :]  # [batch, vocab]
+
+    scores_bf16 = next_logits[:, 0].to(torch.bfloat16)
+    scores = scores_bf16.float().tolist()
+
+    # Sort by score (descending)
+    results = sorted([(s, i, documents[i]) for i, s in enumerate(scores)], key=lambda x: x[0], reverse=True)
+
+    print(f"Query: {query}")
+    print(f"Instruction: {instruction}")
+    for score, doc_id, doc in results:
+        print(f"Score: {score:.4f} | Doc: {doc}")
+    """
+    Query: What are the health benefits of exercise?
+    Instruction: Prioritize recent medical research
+    Score: -2.1719 | Doc: A 2024 study shows exercise enhances cognitive function in older adults.
+    Score: -4.6875 | Doc: Regular exercise reduces risk of heart disease and improves mental health.
+    Score: -12.4375 | Doc: Ancient Greeks valued physical fitness for military training.
+    """
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{ctxl_rerank_v2_instruct_multilingual,
+      title={Contextual AI Reranker v2}, 
+      author={Halal, George and Agrawal, Sheshansh},
+      year={2025},
+      url={https://contextual.ai/blog/rerank-v2}, 
+}
+```
+
+## License
+
+Creative Commons Attribution Non Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)
+
+## Contact
+
+For questions or issues, please open an issue on the model repository.
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,7 @@
+{%- set instruction_text = messages | selectattr("role", "eq", "system") | map(attribute="content") | first | default("") -%}
+{%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%}
+{%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%}
+{{- bos_token -}}
+Check whether a given document contains information helpful to answer the query.
+<Document> {{ document_text }}
+<Query> {{ query_text }}{% if instruction_text %} {{ instruction_text }}{% endif %} ??
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "max_position_embeddings": 1024000,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 20,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "vocab_size": 131072
+}
--- a/config_sentence_transformers.json
+++ b/config_sentence_transformers.json
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+{
+  "__version__": {
+    "pytorch": "2.10.0+cu128",
+    "sentence_transformers": "5.4.0",
+    "transformers": "5.5.0.dev0"
+  },
+  "activation_fn": "torch.nn.modules.linear.Identity",
+  "default_prompt_name": null,
+  "model_type": "CrossEncoder",
+  "prompts": {}
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.51.3"
+}
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