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README.md

@@ -72,7 +72,7 @@ tags:
 
 **Model Summary:** Granite-Embedding-311M-Multilingual-R2 is a 311M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings. It produces 768-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with enhanced support for 52 languages and programming code that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets.
 
-> Granite Embedding 311M Multilingual R2 shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. The multilingual R2 model scores **64.0** on [Multilingual MTEB Retrieval (18 tasks)](https://huggingface.co/spaces/mteb/leaderboard) — a **+11.8 point improvement** over granite-embedding-278m-multilingual (52.2) — and averages **56.0** across all retrieval benchmarks, representing a **+14.2 point gain** over the previous generation. It supports Matryoshka dimension reduction, 32k-token context, and ships with ONNX and OpenVINO models for production deployment.
+> Granite Embedding 311M Multilingual R2 shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. The multilingual R2 model scores **65.2** on [Multilingual MTEB Retrieval (18 tasks)](https://huggingface.co/spaces/mteb/leaderboard) — a **+13 point improvement** over granite-embedding-278m-multilingual (52.2) — and averages **56.3** across all retrieval benchmarks, representing a **+14.5 point gain** over the previous generation. It supports Matryoshka dimension reduction, 32k-token context, and ships with ONNX and OpenVINO models for production deployment.
 
 ### What's New in R2
 
@@ -81,7 +81,7 @@ tags:
 - **Expanded vocabulary:** 262K multilingual tokenizer trained on text and code across 200+ languages.
 - **Matryoshka support:** Truncate embeddings to 512, 384, 256, or 128 dimensions with graceful degradation.
 - **Broader code coverage:** Code retrieval supported for Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++.
-- **Training advances:** Knowledge distillation from multiple teachers, contrastive fine-tuning, and model merging yield +14.2 points on average retrieval benchmarks.
+- **Training advances:** Knowledge distillation from multiple teachers, contrastive fine-tuning, and model merging yield +14.5 points on average retrieval benchmarks.
 - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).
 
 The model uses a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, code, and documents, enabling seamless comparison through cosine similarity. Built using contrastive fine-tuning, knowledge distillation, and model merging, the Granite Embedding 311M Multilingual R2 model is optimized to ensure strong alignment between query and passage embeddings across many languages.
@@ -96,7 +96,7 @@ The Granite Embedding Multilingual R2 release consists of two multilingual embed
 - **Developed by:** Granite Embedding Team, IBM
 - **Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
 - **Project Page:** [IBM Granite](https://www.ibm.com/granite)
-- **Paper:** Coming May 2026
+- **Paper:** [Granite Embedding Multilingual R2 Models](https://huggingface.co/papers/2605.13521)
 - **Language(s) (NLP):** 200+ languages supported, with enhanced support for 52 languages and programming code (see [full language list](#supported-languages))
 - **Release Date**: April 29, 2026
 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
@@ -286,21 +286,21 @@ Note: Ollama does not currently support ModernBERT-based models.
 ## Evaluation Results
 
 Granite-Embedding-311M-Multilingual-R2 is in the top three in the under-500M multilingual class on average across retrieval, code search,
-long-document, and reasoning benchmarks, with a **+14.2 point** average gain over the previous-generation
+long-document, and reasoning benchmarks, with a **+14.5 point** average gain over the previous-generation
 Granite-Embedding-278M-Multilingual.
 
 ### Multilingual Retrieval Performance
 
 Performance on Multilingual MTEB Retrieval, MTEB English Retrieval, MTEB Code Retrieval, long-document search (LongEmbed), and Reasoning as Retrieval (RaR-b) benchmarks. Scores are averages across tasks; higher is better. Throughput (documents per second) is measured on a single NVIDIA H100 GPU with batches of 1024 sequences at 512 tokens.
 
-Granite-Embedding-311M-Multilingual-R2 scores 64.0 on MTEB Multilingual Retrieval — a **+11.8 point improvement** over its R1 predecessor — while encoding nearly 2,000 documents per second at comparable speed.
+Granite-Embedding-311M-Multilingual-R2 scores 65.2 on MTEB Multilingual Retrieval — a **+13 point improvement** over its R1 predecessor — while encoding nearly 2,000 documents per second at comparable speed.
 
 | Model                                      | Parameters (M) | Embedding Size | MTEB ML Retrieval (18) | MTEB Retrieval (eng, v2) (10) | MTEB (Code, v1) (12) | LongEmbed (6) | RaR-b (17) | **AVG**  | Throughput (docs/s) |
 | ------------------------------------------ | -------------- | -------------- | ---------------------- | ----------------------------- | -------------------- | ------------- | ---------- | -------- | ------------------: |
-| granite-embedding-107m-multilingual        | 107            | 384            | 48.1                   | 47.9                          | 40.7                 | 34.3          | 17.1       | 37.6     |               3,337 |
-| granite-embedding-278m-multilingual        | 278            | 768            | 52.2                   | 51.5                          | 48.5                 | 37.7          | 18.9       | 41.8     |               2,185 |
-| granite-embedding-97m-multilingual-r2      | 97             | 384            | 59.6                   | 50.1                          | 60.5                 | 65.5          | 24.9       | 52.1     |               2,894 |
-| **granite-embedding-311m-multilingual-r2** | **311**        | **768**        | **64.0**               | **52.6**                      | **63.9**             | **71.7**      | **28.0**   | **56.0** |           **1,944** |
+| granite-embedding-107m-multilingual        | 107            | 384            | 48.1                   | 47.9                          | 40.7                 | 34.3          | 17.1       | 37.6     |               3,113 |
+| granite-embedding-278m-multilingual        | 278            | 768            | 52.2                   | 51.5                          | 48.5                 | 37.7          | 18.9       | 41.8     |               2,164 |
+| granite-embedding-97m-multilingual-r2      | 97             | 384            | 60.3                   | 50.1                          | 60.4                 | 65.5          | 24.9       | 52.2     |               2,534 |
+| **granite-embedding-311m-multilingual-r2** | **311**        | **768**        | **65.2**               | **52.6**                      | **63.8**             | **71.7**      | **28.0**   | **56.3** |           **1,828** |
 
 ### Matryoshka Embeddings Performance
 
@@ -380,9 +380,13 @@ Performance varies across languages: higher-resource languages and those in the
 ## Citation
 
 ```
-@misc{granite-embedding-311m-multilingual-r2,
-      title={Granite Embedding Multilingual R2 Models},
-      author={IBM Granite Embedding Team},
+@misc{awasthy2026graniteembeddingmultilingualr2,
+      title={Granite Embedding Multilingual R2 Models}, 
+      author={Parul Awasthy and Aashka Trivedi and Yushu Yang and Ken Barker and Yulong Li and Bhavani Iyer and Martin Franz and Juergen Bross and Meet Doshi and Vignesh P and Vishwajeet Kumar and Todd Ward and Abraham Daniels and Madison Lee and Luis Lastras and Jaydeep Sen and Radu Florian},
       year={2026},
+      eprint={2605.13521},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2605.13521}, 
 }
 ```

config.json

@@ -8,7 +8,7 @@
   "classifier_activation": "silu",
   "classifier_bias": false,
   "classifier_dropout": 0.0,
-  "classifier_pooling": "mean",
+  "classifier_pooling": "cls",
   "cls_token_id": 2,
   "decoder_bias": true,
   "deterministic_flash_attn": false,
@@ -26,7 +26,7 @@
   "layer_norm_eps": 1e-12,
   "local_attention": 128,
   "local_rope_theta": 160000.0,
-  "max_position_embeddings": 8192,
+  "max_position_embeddings": 32768,
   "mlp_bias": false,
   "mlp_dropout": 0.0,
   "model_type": "modernbert",

model.sig

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