Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+pipeline_tag: image-text-to-text
+license: gemma
+base_model: google/gemma-3n-E4B
+library_name: kerasformers
+extra_gated_heading: Access Gemma on Hugging Face
+extra_gated_prompt: >-
+  To access Gemma on Hugging Face, you're required to review and agree to
+  Google's usage license. To do this, please ensure you're logged in to Hugging
+  Face and click below. Requests are processed immediately.
+extra_gated_button_content: Acknowledge license
+license_link: https://ai.google.dev/gemma/terms
+language:
+- en
+tags:
+- keras
+- kerasformers
+- gemma3n
+- gemma-3n
+- image-text-to-text
+- audio-text-to-text
+- multimodal
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/kerasformers/gemma-3n-6a7a507adf78dde12680accf) for all versions of Gemma 3n.***
+
+# Run Gemma 3n with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-Gemma%203n-blue)](https://imvision12.github.io/KerasFormers/gemma3n/) [![Collection](https://img.shields.io/badge/HF-Gemma%203n%20collection-yellow)](https://huggingface.co/collections/kerasformers/gemma-3n-6a7a507adf78dde12680accf)
+
+# kerasformers/gemma-3n-e4b
+
+Gemma 3n is Google's on-device **multimodal** (image + audio + text) model. Its
+decoder layers several on-device innovations on the Gemma shape: **AltUp**
+(alternating updates over parallel hidden streams), **LAuReL** (learned augmented
+residuals), **MatFormer** (nested per-layer widths), **Per-Layer Embeddings**, and
+activation sparsity, with tail **KV-sharing** and a 5:1 sliding/global attention
+schedule. Vision is a **MobileNet-V5** encoder and audio a **USM** conformer, both
+feeding soft tokens into the decoder. Base checkpoints are for completion; `-it`
+variants are instruction-tuned.
+
+For more details, see Google's original [model card](https://huggingface.co/google/gemma-3n-E4B).
+
+Pure-**Keras 3** conversion of [`google/gemma-3n-E4B`](https://huggingface.co/google/gemma-3n-E4B) for
+[kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on
+**TensorFlow / Torch / JAX**.
+
+This is a **base** (pretrained) checkpoint, for completion / fine-tuning.
+
+## ✨ Quick start
+
+### Text-only
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.gemma3n import Gemma3nTextGenerate, Gemma3nTokenizer
+
+model = Gemma3nTextGenerate.from_weights("kerasformers/gemma-3n-e4b")
+tokenizer = Gemma3nTokenizer.from_weights("kerasformers/gemma-3n-e4b")
+
+inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
+outputs = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Image + text
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+from kerasformers.models.gemma3n import Gemma3nConditionalGenerate, Gemma3nProcessor
+
+model = Gemma3nConditionalGenerate.from_weights("kerasformers/gemma-3n-e4b")
+processor = Gemma3nProcessor.from_weights("kerasformers/gemma-3n-e4b")
+
+inputs = processor(conversation=[
+    {"role": "user", "content": [
+        {"type": "image", "image": Image.open("cat.jpg")},
+        {"type": "text", "text": "Describe this image in one sentence."},
+    ]}
+])
+outputs = model.generate(**inputs, max_new_tokens=64)
+print(processor.decode(outputs[0]))
+```
+
+All Gemma 3n variants load the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Type |
+|---|---|---|
+| `gemma-3n-e2b` | [`kerasformers/gemma-3n-e2b`](https://huggingface.co/kerasformers/gemma-3n-e2b) | multimodal / base |
+| `gemma-3n-e2b-it` | [`kerasformers/gemma-3n-e2b-it`](https://huggingface.co/kerasformers/gemma-3n-e2b-it) | multimodal / instruct |
+| `gemma-3n-e4b` | [`kerasformers/gemma-3n-e4b`](https://huggingface.co/kerasformers/gemma-3n-e4b) | multimodal / base |
+| `gemma-3n-e4b-it` | [`kerasformers/gemma-3n-e4b-it`](https://huggingface.co/kerasformers/gemma-3n-e4b-it) | multimodal / instruct |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Loads in **bfloat16** by default (the weights are bf16). Pass
+  `load_dtype="float32"` for full precision, or `quantization="int8"` to shrink further.
+- Gemma 3n is **audio-capable** too: pass `audio` content items in the
+  conversation to transcribe / reason over speech.
+- See [Gemma 3n docs](https://imvision12.github.io/KerasFormers/gemma3n/) and
+  [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Upstream safetensors still work via the `hf:` prefix, e.g.
+  `Gemma3nConditionalGenerate.from_weights("hf:google/gemma-3n-E4B")`.
+
+## Special Thanks
+
+A huge thank you to the Google Gemma authors for creating and releasing these models.
+
+License: Gemma (gated). Accept the license on the upstream Hub card before downloading.

kf_config.json

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+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.1",
+  "model_module": "kerasformers.models.gemma3n",
+  "model_class": "Gemma3nConditionalGenerate",
+  "variant": "gemma-3n-e4b",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "bfloat16",
+  "model_type": "gemma3n",
+  "text_config": {
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+    "head_dim": 256,
+    "sliding_window": 512,
+    "sliding_window_pattern": 5,
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+    "hidden_activation": "gelu_pytorch_tanh",
+    "tie_embeddings": true,
+    "vocab_size_per_layer_input": 262144,
+    "hidden_size_per_layer_input": 256,
+    "altup_num_inputs": 4,
+    "altup_active_idx": 0,
+    "altup_coef_clip": 120.0,
+    "altup_correct_scale": true,
+    "num_kv_shared_layers": 15,
+    "laurel_rank": 64,
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+  },
+  "audio_soft_tokens_per_image": 188,
+  "vision_soft_tokens_per_image": 256,
+  "boi_token_id": 255999,
+  "eoi_token_id": 262144,
+  "image_token_id": 262145,
+  "boa_token_id": 256000,
+  "eoa_token_id": 262272,
+  "audio_token_id": 262273,
+  "tie_word_embeddings": true
+}

kf_preprocessor.json

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+  "kerasformers_version": "1.2.1",
+  "preprocessor_module": "kerasformers.models.gemma3n",
+  "preprocessor_class": "Gemma3nImageProcessor",
+  "variant": "gemma-3n-e4b",
+  "size": 768,
+  "rescale_factor": 0.00392156862745098,
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+  "image_std": [
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+  ]
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