feat: upload neomme files

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl 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

1_Pooling/config.json

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+{
+  "embedding_dimension": 1792,
+  "pooling_mode": "mean",
+  "include_prompt": true
+}

2_Normalize/config.json

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+{
+  "module_input_name": "sentence_embedding",
+  "module_output_name": "sentence_embedding"
+}

LICENSE

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README.md

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+---
+library_name: sentence-transformers
+license: apache-2.0
+language:
+- multilingual
+base_model:
+- Hcompany/NeoMME-800M-Retriever
+pipeline_tag: sentence-similarity
+tags:
+- multimodal
+- document-retrieval
+- dense-retrieval
+---
+
+<p align="left">
+  <img src="https://github.com/tonywu71/colpali-cookbooks/blob/6ef1332da6bcb48c7ef1f19b25bfa555be7031a8/assets/neomme/neomme_logo.webp?raw=true" alt="NeoMME logo" style="max-height: 140px;">
+</p>
+
+# NeoMME-Retriever (800M): Single-Tower Multimodal-Native Multilingual Foundation Encoder 🔎
+
+> [!IMPORTANT]
+> NeoMME-Retriever (800M) variants:
+>
+> - [Default (`transformers`)](https://huggingface.co/Hcompany/NeoMME-800M-Retriever): Returns dense and multi-vector embeddings together with a single forward pass. Recommended for most use cases and inference.
+> - [ST dense](https://huggingface.co/Hcompany/NeoMME-800M-Retriever-ST-dense) [current]: Supports independent dense fine-tuning with Sentence Transformers.
+> - [ST late-interaction](https://huggingface.co/Hcompany/NeoMME-800M-Retriever-ST-late): Supports independent multi-vector fine-tuning with Sentence Transformers.
+
+[![Hugging Face](https://img.shields.io/badge/Model_doc-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/docs/transformers/en/model_doc/neomme)
+[![Hugging Face](https://img.shields.io/badge/Collection-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://hf.co/collections/Hcompany/neomme)
+[![arXiv](https://img.shields.io/badge/arXiv-coming_soon-b31b1b.svg?style=for-the-badge)](https://arxiv.org)
+
+NeoMME-800M-Retriever-ST-dense is a model for multimodal document retrieval. Fine-tuned from [NeoMME-800M](https://huggingface.co/Hcompany/NeoMME-800M), it encodes text queries and documents (text or page screenshots) using one shared bidirectional Transformer encoder.
+
+This model can be used with Sentence Transformers, but can only generate dense embeddings.
+
+<table>
+<thead>
+<tr style="background-color: rgba(146, 81, 247, 0.20);"><th>Specification</th><th>Value</th></tr>
+</thead>
+<tbody>
+<tr><td>Parameters</td><td>800M</td></tr>
+<tr><td>Vocabulary</td><td>131,072 tokens</td></tr>
+<tr><td>Context length</td><td>16,384 tokens</td></tr>
+<tr><td>Hidden size</td><td>1,792</td></tr>
+<tr><td>Image patches</td><td>32 × 32 pixels, up to 2,048 pixels on the longest side (default)</td></tr>
+<tr><td>Dense embeddings</td><td>1,792 dimensions (Matryoshka: [128, 256, 512, 1,024, 1,792])</td></tr>
+<tr><td>Dense pooling strategy</td><td>Mean</td></tr>
+</tbody>
+</table>
+
+Dense embeddings are L2-normalized and use cosine similarity. They match `NeoMMEForRetrieval.dense_embeddings`.
+
+## Performance
+
+All scores use the metric shown at the full trained dimensions. Higher is better. ViDoRe v3, v2, and v1 measure visual document retrieval, while BEIR-15 measures text retrieval.
+
+<table>
+<thead>
+<tr><th rowspan="2">Benchmark</th><th rowspan="2">Metric</th><th colspan="2">NeoMME-260M</th><th colspan="2" style="background-color: rgba(37, 99, 235, 0.20);">NeoMME-800M</th></tr>
+<tr><th>Late interaction</th><th>Dense</th><th>Late interaction</th><th style="background-color: rgba(37, 99, 235, 0.20);">Dense [current]</th></tr>
+</thead>
+<tbody>
+<tr><td>ViDoRe v3</td><td>nDCG@10</td><td>0.5226</td><td>0.3907</td><td>0.5560</td><td style="background-color: rgba(37, 99, 235, 0.20);">0.4391</td></tr>
+<tr><td>ViDoRe v2</td><td>nDCG@5</td><td>0.5218</td><td>0.4075</td><td>0.5591</td><td style="background-color: rgba(37, 99, 235, 0.20);">0.4475</td></tr>
+<tr><td>ViDoRe v1</td><td>nDCG@5</td><td>0.8598</td><td>0.7552</td><td>0.8744</td><td style="background-color: rgba(37, 99, 235, 0.20);">0.7993</td></tr>
+<tr><td>BEIR-15</td><td>nDCG@10</td><td>0.4881</td><td>0.3055</td><td>0.5126</td><td style="background-color: rgba(37, 99, 235, 0.20);">0.3686</td></tr>
+</tbody>
+</table>
+
+
+## Usage
+
+```bash
+pip install -U "sentence-transformers[image]"
+```
+
+```python
+from sentence_transformers import SentenceTransformer
+
+
+model = SentenceTransformer("Hcompany/NeoMME-800M-Retriever-ST-dense")
+
+queries = [
+    "Quelle partie de la production pétrolière du Kazakhstan provient de champs en mer ?",
+    "Which hour of the day had the highest overall electricity generation in 2019?",
+]
+documents = [
+    "https://github.com/tonywu71/colpali-cookbooks/blob/main/examples/data/shift_kazakhstan.jpg?raw=true",
+    "https://github.com/tonywu71/colpali-cookbooks/blob/main/examples/data/energy_electricity_generation.jpg?raw=true",
+]
+
+query_embeddings = model.encode_query(queries, convert_to_tensor=True)
+document_embeddings = model.encode_document(documents, convert_to_tensor=True)
+scores = model.similarity(query_embeddings, document_embeddings)
+
+# Expected: scores[0, 0] > scores[0, 1] and scores[1, 1] > scores[1, 0].
+print(scores)
+```
+
+The score tensor has shape `(num_queries, num_documents)` and `scores[i, j]` is the score between query `i` and document `j`. A larger value indicates a closer match.
+
+## Training
+
+NeoMME-800M-Retriever was fine-tuned from [NeoMME-800M](https://huggingface.co/Hcompany/NeoMME-800M) on text retrieval and document-page images. Training uses a joint late-interaction and Matryoshka dense contrastive objective.
+
+The NeoMME technical report describes the full fine-tuning recipe (will be released soon).
+
+## Limitations
+
+With Sentence Transformers, only one of the two retrieval heads can be used at a time.
+
+## License
+
+Model weights are released under the Apache 2.0 license.
+
+## Citation
+
+```bibtex
+@techreport{neomme2026,
+  title = {NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference},
+  author = {Lac, Aurélien and Wu, Tony},
+  institution = {H Company},
+  year = {2026}
+}
+```

chat_template.jinja

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+{#-
+    NeoMME retrieval prompt template. Not a conversation format: NeoMME is an encoder and has no chat turns.
+
+    `task` selects the retrieval side. A sentence-transformers v6 `MultiVectorEncoder` routes
+    `task="query"` / `task="document"` here when the template declares the variable.
+
+      task="query"     ->  <query> … N x <mask>       ColBERT-style learned query expansion. <mask> is the
+                                                      masked-diffusion fill token. The exporter sets N from the
+                                                      checkpoint.
+      task="document"  ->  <doc> …                    Text passages. Image pages take the same <doc> prefix
+                                                      followed by one image token placeholder, which the processor
+                                                      expands after it knows the patch grid.
+-#}
+{%- if task is not defined -%}
+    {{- raise_exception("NeoMME chat templates require task='query' or task='document'.") -}}
+{%- endif -%}
+{%- if task not in ['query', 'document'] -%}
+    {{- raise_exception("task=" ~ task ~ " is not supported: expected 'query' or 'document'.") -}}
+{%- endif -%}
+{%- if messages is not defined or not messages -%}
+    {{- raise_exception("NeoMME chat conversations must contain at least one message.") -}}
+{%- endif -%}
+
+{# Collect content and validate one retrieval item without imposing batch-level policy. #}
+{%- set state = namespace(text='', has_text=false, image_count=0) -%}
+{%- for message in messages -%}
+    {%- set content = message.content -%}
+    {%- set items = [{'type': 'text', 'text': content}] if content is string else content -%}
+    {%- for item in items -%}
+        {%- if item.type == 'text' -%}
+            {%- if image_token in item.text -%}
+                {{- raise_exception(image_token ~ " is reserved for image documents.") -}}
+            {%- endif -%}
+            {%- set state.has_text = true -%}
+            {%- set state.text = state.text + item.text -%}
+        {%- elif item.type == 'image' -%}
+            {%- if item.image is not defined or item.image is none or item.image == '' -%}
+                {{- raise_exception("NeoMME image content must provide an image source.") -}}
+            {%- endif -%}
+            {%- set state.image_count = state.image_count + 1 -%}
+        {%- elif item.type == 'image_url' -%}
+            {%- if item.image_url is not defined or not item.image_url -%}
+                {{- raise_exception("NeoMME image_url content must provide an image source.") -}}
+            {%- endif -%}
+            {%- set state.image_count = state.image_count + 1 -%}
+        {%- else -%}
+            {{- raise_exception("NeoMME chat templates do not support content type " ~ item.type ~ ".") -}}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endfor -%}
+
+{%- if state.image_count and state.has_text -%}
+    {{- raise_exception("NeoMME cannot encode text and images in the same conversation.") -}}
+{%- endif -%}
+{%- if state.image_count > 1 -%}
+    {{- raise_exception("NeoMME accepts one image document per conversation.") -}}
+{%- endif -%}
+{%- if state.image_count and task != 'document' -%}
+    {{- raise_exception("NeoMME image content must use task='document'.") -}}
+{%- endif -%}
+
+{%- set content = image_token if state.image_count else state.text -%}
+{%- if task == 'query' -%}
+    {{- query_token + content + mask_token * 10 -}}
+{%- else -%}
+    {{- document_token + content -}}
+{%- endif -%}

config.json

@@ -0,0 +1,103 @@
+{
+  "architectures": [
+    "NeoMMEModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "document_token_id": 5,
+  "dtype": "bfloat16",
+  "embedding_dim": 128,
+  "embedding_rank": 256,
+  "head_dim": 64,
+  "hidden_act": "relu2",
+  "hidden_size": 1792,
+  "image_token_id": 6,
+  "initializer_range": 0.02,
+  "intermediate_size": 6400,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "model_type": "neomme",
+  "norm_eps": 1e-06,
+  "num_attention_heads": 28,
+  "num_hidden_layers": 20,
+  "num_key_value_heads": 7,
+  "pad_token_id": 0,
+  "patch_size": 32,
+  "per_layer_config": {
+    "01": {
+      "sliding_window": 1024
+    },
+    "03": {
+      "sliding_window": 1024
+    },
+    "05": {
+      "sliding_window": null
+    },
+    "06": {
+      "sliding_window": 1024
+    },
+    "08": {
+      "sliding_window": 1024
+    },
+    "10": {
+      "sliding_window": 1024
+    },
+    "11": {
+      "sliding_window": null
+    },
+    "13": {
+      "sliding_window": 1024
+    },
+    "15": {
+      "sliding_window": 1024
+    },
+    "17": {
+      "sliding_window": null
+    },
+    "18": {
+      "sliding_window": 1024
+    },
+    "19": {
+      "sliding_window": null
+    }
+  },
+  "residual_multiplier": 0.15811388300841897,
+  "rope_parameters": {
+    "full_attention": {
+      "partial_rotary_factor": 0.25,
+      "rope_theta": 1000000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "partial_rotary_factor": 1.0,
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  },
+  "sliding_window": 256,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.16.0.dev0",
+  "vocab_size": 131072
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "6.0.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:949454a191f1ed98e074be1e81317e89b24d29d7dc4d4567cbfff857c47ef81e
+size 1587451080

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.base.modules.normalize.Normalize"
+  }
+]

processor_config.json

@@ -0,0 +1,24 @@
+{
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "NeoMMEImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_side": 2048,
+    "patch_size": 32,
+    "resample": 2,
+    "rescale_factor": 0.00392156862745098
+  },
+  "processor_class": "NeoMMEProcessor"
+}

sentence_bert_config.json

@@ -0,0 +1,20 @@
+{
+  "transformer_task": "feature-extraction",
+  "modality_config": {
+    "text": {
+      "method": "forward",
+      "method_output_name": "last_hidden_state"
+    },
+    "image": {
+      "method": "forward",
+      "method_output_name": "last_hidden_state"
+    },
+    "message": {
+      "method": "forward",
+      "method_output_name": "last_hidden_state",
+      "format": "structured"
+    }
+  },
+  "module_output_name": "token_embeddings",
+  "unpad_inputs": false
+}

tokenizer_config.json

@@ -0,0 +1,21 @@
+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": false,
+  "document_token": "<doc>",
+  "eos_token": "<eos>",
+  "extra_special_tokens": [],
+  "image_token": "<img>",
+  "mask_token": "<mask>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 16384,
+  "pad_token": "<pad>",
+  "processor_class": "NeoMMEProcessor",
+  "query_token": "<query>",
+  "row_token": "<row>",
+  "tokenizer_class": "TokenizersBackend",
+  "truncation_side": "right",
+  "unk_token": "<unk>"
+}