From 83be769033b3fdcc8111910df20ea0ebc8ddab9e Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 27 Jul 2026 00:55:11 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: ContextualAI/ctxl-rerank-v2-instruct-multilingual-6b Source: Original Platform --- .gitattributes | 37 + 1_LogitScore/config.json | 5 + Contextual_AI_Brand_Mark_Dark.png | Bin 0 -> 14090 bytes README.md | 277 + chat_template.jinja | 7 + config.json | 32 + config_sentence_transformers.json | 11 + generation_config.json | 6 + main_benchmark.png | 3 + model-00001-of-00004.safetensors | 3 + model-00002-of-00004.safetensors | 3 + model-00003-of-00004.safetensors | 3 + model-00004-of-00004.safetensors | 3 + model.safetensors.index.json | 190 + modules.json | 14 + sentence_bert_config.json | 15 + special_tokens_map.json | 24 + tokenizer.json | 3 + tokenizer_config.json | 8015 +++++++++++++++++++++++++++++ 19 files changed, 8651 insertions(+) create mode 100644 .gitattributes create mode 100644 1_LogitScore/config.json create mode 100644 Contextual_AI_Brand_Mark_Dark.png create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 config_sentence_transformers.json create mode 100644 generation_config.json create mode 100644 main_benchmark.png create mode 100644 model-00001-of-00004.safetensors create mode 100644 model-00002-of-00004.safetensors create mode 100644 model-00003-of-00004.safetensors create mode 100644 model-00004-of-00004.safetensors create mode 100644 model.safetensors.index.json create mode 100644 modules.json create mode 100644 sentence_bert_config.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes 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filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +main_benchmark.png filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/1_LogitScore/config.json b/1_LogitScore/config.json new file mode 100644 index 0000000..8b2f89b --- /dev/null +++ b/1_LogitScore/config.json @@ -0,0 +1,5 @@ +{ + "true_token_id": 0, + "false_token_id": null, + 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+license: cc-by-nc-sa-4.0 +pipeline_tag: text-ranking +tags: + - sentence-transformers + - cross-encoder + - reranker +--- + +
+ +# Contextual AI Reranker v2 6B + +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) + +
+ +
+ +## 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 + +

+ +

+ +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 {doc}\n {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 {doc}\n {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. \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..7aff9f2 --- /dev/null +++ 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_text }} + {{ query_text }}{% if instruction_text %} {{ instruction_text }}{% endif %} ?? diff --git a/config.json b/config.json new file mode 100644 index 0000000..6bcf7a5 --- /dev/null +++ b/config.json @@ -0,0 +1,32 @@ +{ + "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", + 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