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@@ -33,3 +33,4 @@ saved_model/**/* 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

__init__.py

@@ -0,0 +1 @@
+from .modeling_gemma3_biencoder import Gemma3EncoderForMaskedLM

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3EncoderForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "auto_map": {
+    "AutoModelForMaskedLM": "modeling_gemma3_biencoder.Gemma3EncoderForMaskedLM"
+  },
+  "bos_token_id": 2,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 640,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "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"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 512,
+  "transformers_version": "4.57.3",
+  "use_bidirectional_attention": true,
+  "use_cache": false,
+  "vocab_size": 262145
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9dc0cc7558bc128b11280fbdbacf630a260a637110ad69d3de2f03ca9650093
+size 1072422288

modeling_gemma3_biencoder.py

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+# gemma3_biencoder.py
+from __future__ import annotations
+import copy
+import torch
+import torch.nn as nn
+from typing import Optional, Tuple, Union
+from transformers.modeling_outputs import MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
+from transformers.models.gemma3.configuration_gemma3 import Gemma3TextConfig
+from transformers.models.gemma3.modeling_gemma3 import (
+    Gemma3PreTrainedModel,
+    Gemma3TextModel,
+)
+
+class Gemma3EncoderForMaskedLM(Gemma3PreTrainedModel):
+    config_class = Gemma3TextConfig
+    base_model_prefix = "encoder"
+    _tied_weights_keys = ["lm_head.weight"]
+    _keys_to_ignore_on_load_missing = [r"lm_head\.weight"]
+
+    def __init__(self, config: Gemma3TextConfig):
+        cfg = copy.deepcopy(config)
+        if hasattr(cfg, "use_bidirectional_attention"):
+            cfg.use_bidirectional_attention = True
+        cfg.use_cache = False
+        super().__init__(cfg)
+
+        self.encoder = Gemma3TextModel(cfg)
+        self.vocab_size = cfg.vocab_size
+        self.lm_head = nn.Linear(cfg.hidden_size, cfg.vocab_size, bias=False)
+        self.post_init()  # calls tie_weights()
+
+    # Embeddings / head
+    def get_input_embeddings(self):
+        return self.encoder.embed_tokens
+
+    def set_input_embeddings(self, new_embeddings):
+        self.encoder.embed_tokens = new_embeddings
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_head: nn.Module):
+        self.lm_head = new_head
+
+    # Keep vocab_size in sync; ensure pointer-tying
+    def tie_weights(self):
+        if hasattr(self.config, "vocab_size"):
+            self.config.vocab_size = self.get_input_embeddings().num_embeddings
+            self.vocab_size = self.config.vocab_size
+        if getattr(self.config, "tie_word_embeddings", True):
+            self._tie_or_clone_weights(self.lm_head, self.get_input_embeddings())
+
+    # Ensure 'lm_head.weight' exists when saving (avoids resume warnings)
+    def state_dict(self, *args, **kwargs):
+        sd = super().state_dict(*args, **kwargs)
+        if "lm_head.weight" not in sd and getattr(self.config, "tie_word_embeddings", True):
+            emb_key = f"{self.base_model_prefix}.embed_tokens.weight"
+            if emb_key in sd:
+                sd["lm_head.weight"] = sd[emb_key]
+        return sd
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+        **kwargs,
+    ) -> Union[MaskedLMOutput, Tuple[torch.Tensor, ...]]:
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=False,
+            is_causal=False,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+            loss = loss_fct(logits.view(-1, self.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            out = (logits, hidden_states)
+            if output_hidden_states:
+                out += (outputs.hidden_states,)
+            if output_attentions:
+                out += (outputs.attentions,)
+            if loss is not None:
+                out = (loss,) + out
+            return out
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class Gemma3EncoderForSequenceClassification(Gemma3PreTrainedModel):
+    """Gemma3 Encoder with a sequence classification head (mean pooling + linear)."""
+    config_class = Gemma3TextConfig
+    base_model_prefix = "encoder"
+
+    def __init__(self, config: Gemma3TextConfig):
+        cfg = copy.deepcopy(config)
+        if hasattr(cfg, "use_bidirectional_attention"):
+            cfg.use_bidirectional_attention = True
+        cfg.use_cache = False
+        super().__init__(cfg)
+
+        self.num_labels = getattr(cfg, "num_labels", 2)
+        self.encoder = Gemma3TextModel(cfg)
+        
+        classifier_dropout = getattr(cfg, "classifier_dropout", 0.0)
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(cfg.hidden_size, self.num_labels)
+        
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.encoder.embed_tokens
+
+    def set_input_embeddings(self, new_embeddings):
+        self.encoder.embed_tokens = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+        **kwargs,
+    ) -> Union[SequenceClassifierOutput, Tuple[torch.Tensor, ...]]:
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=False,
+            is_causal=False,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state  # (batch, seq_len, hidden)
+        
+        # Mean pooling over non-padded tokens
+        if attention_mask is not None:
+            mask = attention_mask.unsqueeze(-1).float()  # (batch, seq_len, 1)
+            pooled = (hidden_states * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)
+        else:
+            pooled = hidden_states.mean(dim=1)
+        
+        pooled = self.dropout(pooled)
+        logits = self.classifier(pooled)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class Gemma3EncoderForTokenClassification(Gemma3PreTrainedModel):
+    """Gemma3 Encoder with a token classification head for NER/POS tagging."""
+    config_class = Gemma3TextConfig
+    base_model_prefix = "encoder"
+
+    def __init__(self, config: Gemma3TextConfig):
+        cfg = copy.deepcopy(config)
+        if hasattr(cfg, "use_bidirectional_attention"):
+            cfg.use_bidirectional_attention = True
+        cfg.use_cache = False
+        super().__init__(cfg)
+
+        self.num_labels = getattr(cfg, "num_labels", 2)
+        self.encoder = Gemma3TextModel(cfg)
+        
+        classifier_dropout = getattr(cfg, "classifier_dropout", 0.0)
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(cfg.hidden_size, self.num_labels)
+        
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.encoder.embed_tokens
+
+    def set_input_embeddings(self, new_embeddings):
+        self.encoder.embed_tokens = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+        **kwargs,
+    ) -> Union[TokenClassifierOutput, Tuple[torch.Tensor, ...]]:
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=False,
+            is_causal=False,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

special_tokens_map.json

@@ -0,0 +1,40 @@
+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
+size 33384567