Initial Private Upload

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README.md

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+---
+library_name: transformers
+license: cc-by-nc-sa-4.0
+pipeline_tag: text-ranking
+---
+
+# Contextual AI Reranker v2 6B
+
+## Highlights
+
+Our reranker is on the cost/performance Pareto frontier across 5 key areas:
+- Instruction following (including capability to rank more recent information higher)
+- Question answering
+- Multilinguality
+- Product search / recommendation systems
+- Real-world use cases
+
+<p align="center">
+    <img src="main_benchmark.png" width="1200"/>
+<p>
+
+For more details on these and other benchmarks, please refer to our [blogpost](https://contextual.ai/blog/rerank-v2).
+
+## Overview
+
+- Model Type: Text Reranking
+- Supported Languages: 100+
+- Number of Paramaters: 6B
+- Context Length: up to 32K
+- Blogpost: https://contextual.ai/blog/rerank-v2
+
+## Quickstart
+
+### vLLM usage
+
+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}")
+```
+
+
+### Transformers Usage
+
+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}")
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{ctxl_rerank_v2_instruct_multilingual,
+      title={Contextual AI Reranker v2}, 
+      author={George Halal, Sheshansh Agrawal, Bo Han, Arnav Palkhiwala},
+      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 or contact [email protected].

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
+}

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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