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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

LICENSE

@@ -0,0 +1,201 @@
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+      meet the following conditions:
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+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
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+      the conditions stated in this License.
+
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
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+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
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+   limitations under the License.

NOTICE

@@ -0,0 +1,11 @@
+Gemma 4 E2B Monarch model
+
+This model is derived from google/gemma-4-E2B-it.
+
+Modifications:
+- Replaced language-model MLP layers 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 with two-factor rectangular
+  Monarch linear maps.
+- Initialized factors by rank-one SVD projection of each dense Monarch slice.
+- Distilled the modified layers against the original model.
+
+The original and modified files are distributed under the Apache License 2.0.

README.md

@@ -0,0 +1,50 @@
+---
+license: apache-2.0
+base_model: google/gemma-4-E2B-it
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- gemma4
+- monarch-matrices
+- model-compression
+---
+
+# Gemma 4 E2B Monarch 35-MLP
+
+Experimental Gemma 4 E2B model in which language-model MLP layers 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 are
+replaced by two-factor rectangular Monarch linear maps. The factors were initialized
+with dense-to-Monarch SVD projection and trained with activation alignment followed by
+full-model logit distillation.
+
+The exported model contains 3,682,268,704 parameters and preserves the original
+Gemma 4 multimodal processor and architecture outside the selected MLPs.
+
+## Usage
+
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+model_id = "hexoy/gemma-4-e2b-monarch-35mlp"
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    dtype="auto",
+    device_map="auto",
+)
+```
+
+This repository contains custom modeling code, so review it before enabling
+`trust_remote_code=True`.
+
+## Limitations
+
+- This is an experimental compression artifact, not an official Google model.
+- Only 35 of the 35 language-model MLPs are compressed.
+- Quality and inference speed have not been established on broad downstream benchmarks.
+- The model should be evaluated for the intended task before deployment.
+
+## Attribution
+
+Derived from [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it). See `NOTICE` for
+the modification summary.

chat_template.jinja

@@ -0,0 +1,386 @@
+{#
+  Template: Google Gemma 4 Canonical Chat Template
+  Author: Google Gemma Engineering Team
+  Published: 2026-07-09
+  Context: Fixed tool-calling loops, turn closures, and thinking content-ordering.
+#}
+{%- macro format_parameters(properties, required, filter_keys=false) -%}
+    {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
+    {%- set ns = namespace(found_first=false) -%}
+    {%- for key, value in properties | dictsort -%}
+        {%- set add_comma = false -%}
+        {%- if not filter_keys or key not in standard_keys -%}
+            {%- if ns.found_first %},{% endif -%}
+            {%- set ns.found_first = true -%}
+            {{ key }}:{
+            {%- if value['description'] -%}
+                description:<|"|>{{ value['description'] }}<|"|>
+                {%- set add_comma = true -%}
+            {%- endif -%}
+            {%- if value['type'] | upper == 'STRING' -%}
+                {%- if value['enum'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    enum:{{ format_argument(value['enum']) }}
+                {%- endif -%}
+            {%- elif value['type'] | upper == 'ARRAY' -%}
+                {%- if value['items'] is mapping and value['items'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    items:{
+                    {%- set ns_items = namespace(found_first=false) -%}
+                    {%- for item_key, item_value in value['items'] | dictsort -%}
+                        {%- if item_value is not none -%}
+                            {%- if ns_items.found_first %},{% endif -%}
+                            {%- set ns_items.found_first = true -%}
+                            {%- if item_key == 'properties' -%}
+                                properties:{
+                                {%- if item_value is mapping -%}
+                                    {{- format_parameters(item_value, value['items']['required'] | default([])) -}}
+                                {%- endif -%}
+                                }
+                            {%- elif item_key == 'required' -%}
+                                required:[
+                                {%- for req_item in item_value -%}
+                                    <|"|>{{- req_item -}}<|"|>
+                                    {%- if not loop.last %},{% endif -%}
+                                {%- endfor -%}
+                                ]
+                            {%- elif item_key == 'type' -%}
+                                {%- if item_value is string -%}
+                                    type:{{ format_argument(item_value | upper) }}
+                                {%- else -%}
+                                    type:{{ format_argument(item_value | map('upper') | list) }}
+                                {%- endif -%}
+                            {%- else -%}
+                                {{ item_key }}:{{ format_argument(item_value) }}
+                            {%- endif -%}
+                        {%- endif -%}
+                    {%- endfor -%}
+                    }
+                {%- endif -%}
+            {%- endif -%}
+            {%- if value['nullable'] %}
+                {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                nullable:true
+            {%- endif -%}
+            {%- if value['type'] | upper == 'OBJECT' -%}
+                {%- if value['properties'] is defined and value['properties'] is mapping -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    properties:{
+                    {{- format_parameters(value['properties'], value['required'] | default([])) -}}
+                    }
+                {%- elif value is mapping -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    properties:{
+                    {{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}
+                    }
+                {%- endif -%}
+                {%- if value['required'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    required:[
+                    {%- for item in value['required'] | default([]) -%}
+                        <|"|>{{- item -}}<|"|>
+                        {%- if not loop.last %},{% endif -%}
+                    {%- endfor -%}
+                    ]
+                {%- endif -%}
+            {%- endif -%}
+            {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+            type:<|"|>{{ value['type'] | upper }}<|"|>}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endmacro -%}
+{%- macro format_function_declaration(tool_data) -%}
+    declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|>
+    {%- set params = tool_data['function']['parameters'] -%}
+    {%- if params -%}
+        ,parameters:{
+        {%- if params['properties'] -%}
+            properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
+        {%- endif -%}
+        {%- if params['required'] -%}
+            required:[
+            {%- for item in params['required'] -%}
+                <|"|>{{- item -}}<|"|>
+                {{- ',' if not loop.last -}}
+            {%- endfor -%}
+            ],
+        {%- endif -%}
+        {%- if params['type'] -%}
+            type:<|"|>{{- params['type'] | upper -}}<|"|>}
+        {%- endif -%}
+    {%- endif -%}
+    {%- if 'response' in tool_data['function'] -%}
+        {%- set response_declaration = tool_data['function']['response'] -%}
+        ,response:{
+        {%- if response_declaration['description'] -%}
+            description:<|"|>{{- response_declaration['description'] -}}<|"|>,
+        {%- endif -%}
+        {%- if response_declaration['type'] | upper == 'OBJECT' -%}
+            type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>}
+        {%- endif -%}
+    {%- endif -%}
+    }
+{%- endmacro -%}
+{%- macro format_argument(argument, escape_keys=True) -%}
+    {%- if argument is none -%}
+        {{- 'null' -}}
+    {%- elif argument is string -%}
+        {{- '<|"|>' + argument + '<|"|>' -}}
+    {%- elif argument is boolean -%}
+        {{- 'true' if argument else 'false' -}}
+    {%- elif argument is mapping -%}
+        {{- '{' -}}
+        {%- set ns = namespace(found_first=false) -%}
+        {%- for key, value in argument | dictsort -%}
+            {%- if ns.found_first %},{% endif -%}
+            {%- set ns.found_first = true -%}
+            {%- if escape_keys -%}
+                {{- '<|"|>' + key + '<|"|>' -}}
+            {%- else -%}
+                {{- key -}}
+            {%- endif -%}
+            :{{- format_argument(value, escape_keys=escape_keys) -}}
+        {%- endfor -%}
+        {{- '}' -}}
+    {%- elif argument is sequence -%}
+        {{- '[' -}}
+        {%- for item in argument -%}
+            {{- format_argument(item, escape_keys=escape_keys) -}}
+            {%- if not loop.last %},{% endif -%}
+        {%- endfor -%}
+        {{- ']' -}}
+    {%- else -%}
+        {{- argument -}}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro strip_thinking(text) -%}
+    {%- set ns = namespace(result='') -%}
+    {%- for part in text.split('<channel|>') -%}
+        {%- if '<|channel>' in part -%}
+            {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
+        {%- else -%}
+            {%- set ns.result = ns.result + part -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- ns.result | trim -}}
+{%- endmacro -%}
+
+{%- macro format_tool_response_block(tool_name, response) -%}
+    {{- '<|tool_response>' -}}
+    {%- if response is mapping -%}
+        {{- 'response:' + tool_name + '{' -}}
+        {%- for key, value in response | dictsort -%}
+            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}
+            {%- if not loop.last %},{% endif -%}
+        {%- endfor -%}
+        {{- '}' -}}
+    {%- else -%}
+        {{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}}
+    {%- endif -%}
+    {{- '<tool_response|>' -}}
+{%- endmacro -%}
+
+{#- ===== SETUP ===== -#}
+{%- set ns = namespace(prev_message_type=None, prev_non_tool_role=None) -%}
+{%- set loop_messages = messages -%}
+{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+{{- bos_token -}}
+{#- Handle System/Tool Definitions Block -#}
+{%- if enable_thinking or tools or (messages and messages[0]['role'] in ['system', 'developer']) -%}
+    {{- '<|turn>system\n' -}}
+    {#- Inject Thinking token at the very top of the FIRST system turn -#}
+    {%- if enable_thinking -%}
+        {{- '<|think|>\n' -}}
+        {%- set ns.prev_message_type = 'think' -%}
+    {%- endif -%}
+    {%- if messages and messages[0]['role'] in ['system', 'developer'] -%}
+        {%- if messages[0]['content'] is string -%}
+            {{- messages[0]['content'] | trim -}}
+        {%- elif messages[0]['content'] is sequence -%}
+            {%- for item in messages[0]['content'] -%}
+                {{- item['text'] | trim + ' '-}}
+            {%- endfor -%}
+        {%- endif -%}
+        {%- set loop_messages = messages[1:] -%}
+    {%- endif -%}
+    {%- if tools -%}
+        {%- for tool in tools %}
+            {{- '<|tool>' -}}
+            {{- format_function_declaration(tool) | trim -}}
+            {{- '<tool|>' -}}
+        {%- endfor %}
+        {%- set ns.prev_message_type = 'tool' -%}
+    {%- endif -%}
+    {{- '<turn|>\n' -}}
+{%- endif %}
+
+{#- Pre-scan: find last user message index for reasoning guard -#}
+{%- set ns_turn = namespace(last_user_idx=-1) -%}
+{%- for i in range(loop_messages | length) -%}
+    {%- if loop_messages[i]['role'] == 'user' -%}
+        {%- set ns_turn.last_user_idx = i -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{#- Loop through messages -#}
+{%- for message in loop_messages -%}
+    {%- if message['role'] != 'tool' -%}
+    {%- set ns.prev_message_type = None -%}
+    {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
+    {#- Detect continuation using tracked state — O(1) instead of O(n) backward scan -#}
+    {%- set continue_same_model_turn = (role == 'model' and ns.prev_non_tool_role == 'assistant') -%}
+    {%- if not continue_same_model_turn -%}
+        {{- '<|turn>' + role + '\n' }}
+    {%- endif -%}
+
+    {#- Render reasoning/reasoning_content as thinking channel -#}
+    {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
+    {%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or (preserve_thinking and message.get('tool_calls')) -%}
+    {%- if thinking_text and thinking_gate -%}
+        {{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
+    {%- endif -%}
+
+            {%- if message.get('tool_calls') -%}
+                {%- for tool_call in message.get('tool_calls') -%}
+                    {%- set function = tool_call['function'] -%}
+                    {{- '<|tool_call>call:' + function['name'] + '{' -}}
+                    {%- if function['arguments'] is mapping -%}
+                        {%- set ns_args = namespace(found_first=false) -%}
+                        {%- for key, value in function['arguments'] | dictsort -%}
+                            {%- if ns_args.found_first %},{% endif -%}
+                            {%- set ns_args.found_first = true -%}
+                            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}
+                        {%- endfor -%}
+                    {%- elif function['arguments'] is none -%}
+                    {%- else -%}
+                        {{- raise_exception(
+                            "chat_template: tool_calls[].function.arguments must be a "
+                            "JSON object (mapping), not a string. Deserialize arguments "
+                            "before passing to the template."
+                        ) -}}
+                    {%- endif -%}
+                    {{- '}<tool_call|>' -}}
+                {%- endfor -%}
+                {%- set ns.prev_message_type = 'tool_call' -%}
+            {%- endif -%}
+
+            {%- set ns_tr_out = namespace(flag=false) -%}
+            {%- if message.get('tool_responses') -%}
+                {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}
+                {%- for tool_response in message.get('tool_responses') -%}
+                    {{- format_tool_response_block(tool_response['name'] | default('unknown', true), tool_response['response']) -}}
+                    {%- set ns_tr_out.flag = true -%}
+                    {%- set ns.prev_message_type = 'tool_response' -%}
+                {%- endfor -%}
+            {%- elif message.get('tool_calls') -%}
+                {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}
+                {%- set ns_tool_scan = namespace(stopped=false) -%}
+                {%- for k in range(loop.index0 + 1, loop_messages | length) -%}
+                    {%- if ns_tool_scan.stopped -%}
+                    {%- elif loop_messages[k]['role'] != 'tool' -%}
+                        {%- set ns_tool_scan.stopped = true -%}
+                    {%- else -%}
+                        {%- set follow = loop_messages[k] -%}
+                        {#- Resolve tool_call_id to function name -#}
+                        {%- set ns_tname = namespace(name=follow.get('name') or 'unknown') -%}
+                        {%- for tc in message.get('tool_calls') -%}
+                            {%- if tc.get('id') == follow.get('tool_call_id') -%}
+                                {%- set ns_tname.name = tc['function']['name'] -%}
+                            {%- endif -%}
+                        {%- endfor -%}
+                        {#- Handle content as string or content-parts array -#}
+                        {%- set tool_body = follow.get('content') -%}
+                        {%- if tool_body is string -%}
+                            {{- format_tool_response_block(ns_tname.name, tool_body) -}}
+                        {%- elif tool_body is sequence and tool_body is not string -%}
+                            {%- set ns_txt = namespace(s='') -%}
+                            {%- for part in tool_body -%}
+                                {%- if part.get('type') == 'text' -%}
+                                    {%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}
+                                {%- endif -%}
+                            {%- endfor -%}
+                            {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
+                            {%- for part in tool_body -%}
+                                {%- if part.get('type') in ['image', 'image_url'] -%}
+                                    {{- '<|image|>' -}}
+                                {%- elif part.get('type') in ['audio', 'input_audio'] -%}
+                                    {{- '<|audio|>' -}}
+                                {%- elif part.get('type') == 'video' -%}
+                                    {{- '<|video|>' -}}
+                                {%- endif -%}
+                            {%- endfor -%}
+                        {%- else -%}
+                            {{- format_tool_response_block(ns_tname.name, tool_body) -}}
+                        {%- endif -%}
+                        {%- set ns_tr_out.flag = true -%}
+                        {%- set ns.prev_message_type = 'tool_response' -%}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- endif -%}
+
+            {%- set captured_content -%}
+            {%- if message.get('content') is string -%}
+                {%- if role == 'model' -%}
+                    {{- strip_thinking(message['content']) -}}
+                {%- else -%}
+                    {{- message['content'] | trim -}}
+                {%- endif -%}
+            {%- elif message.get('content') is sequence -%}
+                {%- for item in message['content'] -%}
+                    {%- if item.get('type') == 'text' -%}
+                        {%- if role == 'model' -%}
+                            {{- strip_thinking(item['text']) -}}
+                        {%- else -%}
+                            {{- item['text'] | trim -}}
+                        {%- endif -%}
+                    {%- elif item.get('type') in ['image', 'image_url'] -%}
+                        {{- '<|image|>' -}}
+                    {%- elif item.get('type') in ['audio', 'input_audio'] -%}
+                        {{- '<|audio|>' -}}
+                    {%- elif item.get('type') == 'video' -%}
+                        {{- '<|video|>' -}}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- endif -%}
+            {%- endset -%}
+
+            {{- captured_content -}}
+            {%- set has_content = captured_content | trim | length > 0 -%}
+
+        {#- Forward-scan: find next non-tool message role for continuation detection -#}
+        {%- set next_nt = namespace(role=None, found=false) -%}
+        {%- for j in range(loop.index0 + 1, loop_messages | length) -%}
+            {%- if not next_nt.found -%}
+                {%- if loop_messages[j]['role'] != 'tool' -%}
+                    {%- set next_nt.role = loop_messages[j]['role'] -%}
+                    {%- set next_nt.found = true -%}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endfor -%}
+
+        {%- set continues_into_next = (
+            role == 'model'
+            and next_nt.role == 'assistant'
+            and (not message.get('tool_calls') or ns_tr_out.flag)
+        ) -%}
+
+        {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
+            {{- '<|tool_response>' -}}
+        {%- elif continues_into_next -%}
+        {%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}
+            {{- '<turn|>\n' -}}
+        {%- endif -%}
+
+    {#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}
+    {%- set ns.prev_non_tool_role = message['role'] -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
+        {{- '<|turn>model\n' -}}
+    {%- elif ns.prev_message_type == 'tool_response' and enable_thinking -%}
+        {{- '<|channel>thought\n' -}}
+    {%- endif -%}
+{%- endif -%}

config.json

@@ -0,0 +1,236 @@
+{
+  "architectures": [
+    "MonarchGemma4ForConditionalGeneration"
+  ],
+  "audio_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_chunk_size": 12,
+    "attention_context_left": 13,
+    "attention_context_right": 0,
+    "attention_invalid_logits_value": -1000000000.0,
+    "attention_logit_cap": 50.0,
+    "chunk_size_feed_forward": 0,
+    "conv_kernel_size": 5,
+    "dtype": "bfloat16",
+    "gradient_clipping": 10000000000.0,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "model_type": "gemma4_audio",
+    "num_attention_heads": 8,
+    "num_hidden_layers": 12,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_proj_dims": 1536,
+    "problem_type": null,
+    "residual_weight": 0.5,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "subsampling_conv_channels": [
+      128,
+      32
+    ],
+    "use_clipped_linears": true
+  },
+  "audio_token_id": 258881,
+  "auto_map": {
+    "AutoConfig": "configuration_monarch_gemma4.MonarchGemma4Config",
+    "AutoModelForImageTextToText": "modeling_monarch_gemma4.MonarchGemma4ForConditionalGeneration"
+  },
+  "boa_token_id": 256000,
+  "boi_token_id": 255999,
+  "dtype": "bfloat16",
+  "eoa_token_id": 258883,
+  "eoa_token_index": 258883,
+  "eoi_token_id": 258882,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "image_token_id": 258880,
+  "initializer_range": 0.02,
+  "model_type": "monarch_gemma4",
+  "monarch_base_model": "google/gemma-4-E2B-it",
+  "monarch_blocks_weights": 128,
+  "monarch_compressed_layers": [
+    34,
+    33,
+    32,
+    31,
+    30,
+    29,
+    28,
+    27,
+    26,
+    25,
+    24,
+    23,
+    22,
+    21,
+    20,
+    19,
+    18,
+    17,
+    16,
+    15,
+    14,
+    13,
+    12,
+    11,
+    10,
+    9,
+    8,
+    7,
+    6,
+    5,
+    4,
+    3,
+    2,
+    1,
+    0
+  ],
+  "monarch_factor_count": 2,
+  "monarch_format_version": 1,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attention_k_eq_v": false,
+    "bos_token_id": 2,
+    "dtype": "bfloat16",
+    "enable_moe_block": false,
+    "eos_token_id": 1,
+    "expert_intermediate_size": null,
+    "final_logit_softcapping": 30.0,
+    "global_head_dim": 512,
+    "head_dim": 256,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 1536,
+    "hidden_size_per_layer_input": 256,
+    "initializer_range": 0.02,
+    "intermediate_size": 6144,
+    "layer_types": [
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 131072,
+    "model_type": "gemma4_text",
+    "moe_intermediate_size": null,
+    "num_attention_heads": 8,
+    "num_experts": null,
+    "num_global_key_value_heads": null,
+    "num_hidden_layers": 35,
+    "num_key_value_heads": 1,
+    "num_kv_shared_layers": 20,
+    "pad_token_id": 0,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "full_attention": {
+        "partial_rotary_factor": 0.25,
+        "rope_theta": 1000000.0,
+        "rope_type": "proportional"
+      },
+      "sliding_attention": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+      }
+    },
+    "sliding_window": 512,
+    "tie_word_embeddings": true,
+    "top_k_experts": null,
+    "use_bidirectional_attention": null,
+    "use_cache": true,
+    "use_double_wide_mlp": true,
+    "vocab_size": 262144,
+    "vocab_size_per_layer_input": 262144
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "video_token_id": 258884,
+  "vision_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "chunk_size_feed_forward": 0,
+    "default_output_length": 280,
+    "dtype": "bfloat16",
+    "global_head_dim": 64,
+    "head_dim": 64,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "max_position_embeddings": 131072,
+    "model_type": "gemma4_vision",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 16,
+    "num_key_value_heads": 12,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "position_embedding_size": 10240,
+    "problem_type": null,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 100.0,
+      "rope_type": "default"
+    },
+    "standardize": false,
+    "use_clipped_linears": true
+  },
+  "vision_soft_tokens_per_image": 280
+}

configuration_monarch_gemma4.py

@@ -0,0 +1,28 @@
+from transformers import Gemma4Config
+
+
+class MonarchGemma4Config(Gemma4Config):
+    """Gemma 4 configuration with selected MLPs replaced by Monarch factors."""
+
+    model_type = "monarch_gemma4"
+
+    def __init__(
+        self,
+        monarch_compressed_layers=None,
+        monarch_blocks_weights=128,
+        monarch_factor_count=2,
+        monarch_format_version=1,
+        monarch_base_model="google/gemma-4-E2B-it",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.monarch_compressed_layers = list(monarch_compressed_layers or [])
+        self.monarch_blocks_weights = int(monarch_blocks_weights)
+        self.monarch_factor_count = int(monarch_factor_count)
+        self.monarch_format_version = int(monarch_format_version)
+        self.monarch_base_model = str(monarch_base_model)
+
+        if self.monarch_factor_count != 2:
+            raise ValueError("this model format supports exactly two Monarch factors")
+        if len(set(self.monarch_compressed_layers)) != len(self.monarch_compressed_layers):
+            raise ValueError("monarch_compressed_layers contains duplicate layer indices")

export_manifest.json

@@ -0,0 +1,44 @@
+{
+  "base_model": "google/gemma-4-E2B-it",
+  "compressed_layers": [
+    34,
+    33,
+    32,
+    31,
+    30,
+    29,
+    28,
+    27,
+    26,
+    25,
+    24,
+    23,
+    22,
+    21,
+    20,
+    19,
+    18,
+    17,
+    16,
+    15,
+    14,
+    13,
+    12,
+    11,
+    10,
+    9,
+    8,
+    7,
+    6,
+    5,
+    4,
+    3,
+    2,
+    1,
+    0
+  ],
+  "monarch_blocks_weights": 128,
+  "monarch_factor_count": 2,
+  "parameter_count": 3682268704,
+  "checkpoint": "monarch_checkpoints_b8_all35mlp_400p1_800p2_seq512_projinit_p2lr3e4/step_034_model_language_model_layers_0_mlp/unfrozen_weights.pt"
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 2,
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106,
+    50
+  ],
+  "pad_token_id": 0,
+  "temperature": 1.0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "5.13.1"
+}

model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ea02bad44856b48a53786446e9d32217ea9c377b26d16032919b8785c2b87be
+size 4697620648

model-00002-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5edeb987bdf1f8fa2ab6f82144790a5131236f746ad38289c9940e9f643b94eb
+size 2667187158

modeling_monarch_gemma4.py

@@ -0,0 +1,26 @@
+from transformers import Gemma4ForConditionalGeneration
+
+from .configuration_monarch_gemma4 import MonarchGemma4Config
+from .monarch import replace_linear_with_monarch
+
+
+class MonarchGemma4ForConditionalGeneration(Gemma4ForConditionalGeneration):
+    """Gemma 4 with configured language-model MLPs stored as Monarch factors."""
+
+    config_class = MonarchGemma4Config
+
+    def __init__(self, config: MonarchGemma4Config):
+        super().__init__(config)
+        layers = self.model.language_model.layers
+        for layer_index in config.monarch_compressed_layers:
+            if layer_index < 0 or layer_index >= len(layers):
+                raise ValueError(
+                    f"compressed layer index {layer_index} is outside the language model's "
+                    f"0..{len(layers) - 1} range"
+                )
+            replace_linear_with_monarch(
+                layers[layer_index].mlp,
+                config.monarch_blocks_weights,
+                init_method="identity_noise",
+                module_path=f"model.language_model.layers.{layer_index}.mlp",
+            )

monarch.py

@@ -0,0 +1,203 @@
+import math
+
+import torch
+import torch.nn as nn
+
+
+class MonarchLinear(nn.Module):
+    def __init__(self, in_features, out_features, n_blocks, bias=True):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.is_down_proj = out_features < in_features
+
+        if not self.is_down_proj:
+            self.n1 = n_blocks
+            assert in_features % self.n1 == 0, f"in_features ({in_features}) must be divisible by n_blocks ({n_blocks})"
+            self.n2 = in_features // self.n1
+            assert out_features % self.n2 == 0, f"out_features ({out_features}) must be divisible by in_block_size ({self.n2})"
+            self.n3 = out_features // self.n2
+        else:
+            self.n3 = n_blocks
+            assert out_features % self.n3 == 0, f"out_features ({out_features}) must be divisible by n_blocks ({n_blocks})"
+            self.n2 = out_features // self.n3
+            assert in_features % self.n2 == 0, f"in_features ({in_features}) must be divisible by out_block_size ({self.n2})"
+            self.n1 = in_features // self.n2
+
+        self.blk1 = nn.Parameter(torch.empty(self.n1, self.n2, self.n2))
+        self.blk2 = nn.Parameter(torch.empty(self.n2, self.n1, self.n3))
+        self.bias = nn.Parameter(torch.empty(out_features)) if bias else None
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        # Hugging Face constructs modules on the meta device for low-memory loading.
+        # The serialized factors materialize them later, so there is nothing to initialize yet.
+        if self.blk1.is_meta:
+            return
+
+        nn.init.zeros_(self.blk1)
+        nn.init.zeros_(self.blk2)
+
+        for i in range(self.blk1.shape[0]):
+            self.blk1.data[i].copy_(torch.eye(self.blk1.shape[1]))
+
+        for i in range(self.blk2.shape[0]):
+            min_dim = min(self.blk2.shape[1], self.blk2.shape[2])
+            self.blk2.data[i, :min_dim, :min_dim].copy_(torch.eye(min_dim))
+
+            if self.blk2.shape[1] != self.blk2.shape[2]:
+                with torch.no_grad():
+                    bound = 1.0 / (min_dim ** 0.5)
+                    noise = torch.FloatTensor(self.blk2.shape[1], self.blk2.shape[2]).uniform_(-bound, bound)
+                    mask = torch.ones(self.blk2.shape[1], self.blk2.shape[2])
+                    mask[:min_dim, :min_dim] -= torch.eye(min_dim)
+                    self.blk2.data[i] += (noise * mask).to(self.blk2.device)
+
+        if self.bias is not None:
+            nn.init.zeros_(self.bias)
+
+    @torch.no_grad()
+    def initialize_from_dense(self, dense_layer: nn.Linear) -> float:
+        """Project a dense linear map onto this rectangular Monarch layout."""
+        if dense_layer.in_features != self.in_features or dense_layer.out_features != self.out_features:
+            raise ValueError(
+                "dense layer shape does not match Monarch layer: "
+                f"got ({dense_layer.out_features}, {dense_layer.in_features}), "
+                f"expected ({self.out_features}, {self.in_features})"
+            )
+
+        weight = dense_layer.weight.detach().to(device=self.blk1.device, dtype=torch.float32)
+        slices = (
+            weight.reshape(self.n3, self.n2, self.n1, self.n2)
+            .permute(1, 2, 0, 3)
+            .contiguous()
+        )
+        left_vectors, singular_values, right_vectors_h = torch.linalg.svd(slices, full_matrices=False)
+
+        scales = singular_values[..., 0].clamp_min(0.0).sqrt()
+        projected_blk2 = left_vectors[..., :, 0] * scales.unsqueeze(-1)
+        projected_blk1 = right_vectors_h[..., 0, :] * scales.unsqueeze(-1)
+
+        self.blk1.copy_(projected_blk1.permute(1, 2, 0).to(dtype=self.blk1.dtype))
+        self.blk2.copy_(projected_blk2.to(dtype=self.blk2.dtype))
+
+        if self.bias is not None:
+            if dense_layer.bias is None:
+                self.bias.zero_()
+            else:
+                self.bias.copy_(dense_layer.bias.detach().to(device=self.bias.device, dtype=self.bias.dtype))
+
+        squared_singular_values = singular_values.square()
+        total_energy = squared_singular_values.sum()
+        residual_energy = squared_singular_values[..., 1:].sum()
+        if total_energy.item() == 0.0:
+            return 0.0
+        return (residual_energy / total_energy).clamp_min(0.0).sqrt().item()
+
+    def forward(self, x):
+        orig_shape = x.shape
+        x = x.contiguous().view(-1, self.n1, self.n2)
+        x = torch.einsum("bij, ijk -> bik", x, self.blk1)
+        x = x.transpose(1, 2).contiguous()
+        x = torch.einsum("bij, ijk -> bik", x, self.blk2)
+        x = x.transpose(1, 2).contiguous()
+        x = x.view(*orig_shape[:-1], self.out_features)
+
+        if self.bias is not None:
+            x = x + self.bias
+
+        return x
+
+
+class MonarchEmbedding(nn.Module):
+    def __init__(self, num_embeddings, embedding_dim, n_blocks):
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+
+        self.n1 = n_blocks
+        assert num_embeddings % self.n1 == 0, "num_embeddings must be divisible by n_blocks"
+        self.n2 = num_embeddings // self.n1
+
+        assert embedding_dim % self.n2 == 0, f"embedding_dim ({embedding_dim}) must be divisible by in_block_size ({self.n2})"
+        self.n3 = embedding_dim // self.n2
+
+        self.blk1 = nn.Parameter(torch.empty(self.n1, self.n2, self.n2))
+        self.blk2 = nn.Parameter(torch.empty(self.n2, self.n1, self.n3))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.blk1, mean=0, std=1 / math.sqrt(self.n2))
+        nn.init.normal_(self.blk2, mean=0, std=1 / math.sqrt(self.n1))
+
+    def forward(self, x):
+        blk1_flat = self.blk1.view(self.num_embeddings, self.n2)
+        v1 = blk1_flat[x]
+
+        block_idx = x // self.n2
+        blk2_transposed = self.blk2.transpose(0, 1)
+        w2_selected = blk2_transposed[block_idx]
+
+        out_matrix = v1.unsqueeze(-1) * w2_selected
+        out = out_matrix.transpose(-1, -2).contiguous()
+        out = out.view(*x.shape, self.embedding_dim)
+        return out
+
+
+def replace_linear_with_monarch(module, blocks, init_method="identity_noise", module_path=""):
+    for name, child in module.named_children():
+        child_path = f"{module_path}.{name}" if module_path else name
+        if isinstance(child, nn.Linear):
+            device = child.weight.device
+            dtype = child.weight.dtype
+            monarch_layer = MonarchLinear(child.in_features, child.out_features, blocks, bias=child.bias is not None)
+            monarch_layer = monarch_layer.to(device=device, dtype=dtype)
+            if init_method == "dense_projection":
+                relative_error = monarch_layer.initialize_from_dense(child)
+                print(f"[Projection] {child_path} | relative Frobenius error: {relative_error:.6f}")
+            elif init_method != "identity_noise":
+                raise ValueError(f"unsupported Monarch initialization method: {init_method}")
+            setattr(module, name, monarch_layer)
+        else:
+            replace_linear_with_monarch(child, blocks, init_method=init_method, module_path=child_path)
+    return module
+
+
+def replace_with_monarch(
+    student_model,
+    module_path: str,
+    blocks_weights: int,
+    blocks_head: int,
+    init_method: str = "identity_noise",
+):
+    parent_path = ".".join(module_path.split(".")[:-1])
+    child_name = module_path.split(".")[-1]
+
+    parent_module = student_model if parent_path == "" else student_model.get_submodule(parent_path)
+    old_module = getattr(parent_module, child_name)
+
+    if module_path == "lm_head":
+        device, dtype = old_module.weight.device, old_module.weight.dtype
+        new_head = MonarchLinear(old_module.in_features, old_module.out_features, blocks_head, bias=old_module.bias is not None)
+        new_head = new_head.to(device=device, dtype=dtype)
+        if init_method == "dense_projection":
+            relative_error = new_head.initialize_from_dense(old_module)
+            print(f"[Projection] {module_path} | relative Frobenius error: {relative_error:.6f}")
+        elif init_method != "identity_noise":
+            raise ValueError(f"unsupported Monarch initialization method: {init_method}")
+        setattr(parent_module, child_name, new_head)
+        return getattr(parent_module, child_name)
+
+    if "embed_tokens" in module_path:
+        device, dtype = old_module.weight.device, old_module.weight.dtype
+        new_embed = MonarchEmbedding(old_module.num_embeddings, old_module.embedding_dim, blocks_head)
+        setattr(parent_module, child_name, new_embed.to(device=device, dtype=dtype))
+        return getattr(parent_module, child_name)
+
+    replace_linear_with_monarch(
+        old_module,
+        blocks_weights,
+        init_method=init_method,
+        module_path=module_path,
+    )
+    return old_module

processor_config.json

@@ -0,0 +1,75 @@
+{
+  "audio_ms_per_token": 40,
+  "audio_seq_length": 750,
+  "feature_extractor": {
+    "dither": 0.0,
+    "feature_extractor_type": "Gemma4AudioFeatureExtractor",
+    "feature_size": 128,
+    "fft_length": 512,
+    "fft_overdrive": false,
+    "frame_length": 320,
+    "hop_length": 160,
+    "input_scale_factor": 1.0,
+    "max_frequency": 8000.0,
+    "mel_floor": 0.001,
+    "min_frequency": 0.0,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "per_bin_mean": null,
+    "per_bin_stddev": null,
+    "preemphasis": 0.0,
+    "preemphasis_htk_flavor": true,
+    "return_attention_mask": true,
+    "sampling_rate": 16000
+  },
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": false,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "image_processor_type": "Gemma4ImageProcessor",
+    "image_seq_length": 280,
+    "image_std": [
+      1.0,
+      1.0,
+      1.0
+    ],
+    "max_soft_tokens": 280,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098
+  },
+  "image_seq_length": 280,
+  "processor_class": "Gemma4Processor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "image_mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "image_std": [
+      1.0,
+      1.0,
+      1.0
+    ],
+    "max_soft_tokens": 70,
+    "num_frames": 32,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "video_processor_type": "Gemma4VideoProcessor"
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
+size 32169626

tokenizer_config.json

@@ -0,0 +1,96 @@
+{
+  "audio_token": "<|audio|>",
+  "backend": "tokenizers",
+  "boa_token": "<|audio>",
+  "boi_token": "<|image>",
+  "bos_token": "<bos>",
+  "eoa_token": "<audio|>",
+  "eoc_token": "<channel|>",
+  "eoi_token": "<image|>",
+  "eos_token": "<eos>",
+  "eot_token": "<turn|>",
+  "escape_token": "<|\"|>",
+  "etc_token": "<tool_call|>",
+  "etd_token": "<tool|>",
+  "etr_token": "<tool_response|>",
+  "extra_special_tokens": [
+    "<|video|>"
+  ],
+  "image_token": "<|image|>",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {
+    "audio_token": "<|audio|>",
+    "boa_token": "<|audio>",
+    "boi_token": "<|image>",
+    "eoa_token": "<audio|>",
+    "eoc_token": "<channel|>",
+    "eoi_token": "<image|>",
+    "eot_token": "<turn|>",
+    "escape_token": "<|\"|>",
+    "etc_token": "<tool_call|>",
+    "etd_token": "<tool|>",
+    "etr_token": "<tool_response|>",
+    "image_token": "<|image|>",
+    "soc_token": "<|channel>",
+    "sot_token": "<|turn>",
+    "stc_token": "<|tool_call>",
+    "std_token": "<|tool>",
+    "str_token": "<|tool_response>",
+    "think_token": "<|think|>"
+  },
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "processor_class": "Gemma4Processor",
+  "response_schema": {
+    "properties": {
+      "content": {
+        "type": "string"
+      },
+      "role": {
+        "const": "assistant"
+      },
+      "thinking": {
+        "type": "string"
+      },
+      "tool_calls": {
+        "items": {
+          "properties": {
+            "function": {
+              "properties": {
+                "arguments": {
+                  "additionalProperties": {},
+                  "type": "object",
+                  "x-parser": "gemma4-tool-call"
+                },
+                "name": {
+                  "type": "string"
+                }
+              },
+              "type": "object",
+              "x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
+            },
+            "type": {
+              "const": "function"
+            }
+          },
+          "type": "object"
+        },
+        "type": "array",
+        "x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
+      }
+    },
+    "type": "object",
+    "x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
+  },
+  "soc_token": "<|channel>",
+  "sot_token": "<|turn>",
+  "stc_token": "<|tool_call>",
+  "std_token": "<|tool>",
+  "str_token": "<|tool_response>",
+  "think_token": "<|think|>",
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>"
+}