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-E2B-it
+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/kerasformers) for all Gemma 3n sizes and variants.*
+
+# Run Gemma 3n with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-181717?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-Gemma_3n-1f6feb)](https://imvision12.github.io/KerasFormers/gemma3n/) [![HuggingFace](https://img.shields.io/badge/HuggingFace-Gemma_3n-ffd21e?logo=huggingface&logoColor=black)](https://huggingface.co/kerasformers)
+
+# kerasformers/gemma-3n-e2b-it
+
+Pure-**Keras 3** conversion of [`google/gemma-3n-E2B-it`](https://huggingface.co/google/gemma-3n-E2B-it) for
+[kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on
+**TensorFlow / Torch / JAX**. This is the instruction-tuned checkpoint, served here as **image + audio + text -> text** via `Gemma3nConditionalGenerate`; weights are
+stored in **bfloat16**.
+
+For model details, license, and usage terms, see Google's
+[model card](https://huggingface.co/google/gemma-3n-E2B-it).
+
+## ✨ 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-e2b-it")
+tokenizer = Gemma3nTokenizer.from_weights("kerasformers/gemma-3n-e2b-it")
+
+inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
+outputs = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Image + audio + text
+
+```python
+from kerasformers.models.gemma3n import Gemma3nConditionalGenerate, Gemma3nProcessor
+
+model = Gemma3nConditionalGenerate.from_weights("kerasformers/gemma-3n-e2b-it")
+processor = Gemma3nProcessor.from_weights("kerasformers/gemma-3n-e2b-it")
+
+conversation = [
+    {"role": "user", "content": [
+        {"type": "image", "url": "https://.../image.jpg"},
+        {"type": "text", "text": "Describe this image."},
+    ]},
+]
+inputs = processor(conversation)
+outputs = model.generate(**inputs, max_new_tokens=64)
+print(processor.decode(outputs[0]))
+```
+
+Load any Gemma 3n variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub |
+| --- | --- |
+| `gemma-3n-e2b` | [kerasformers/gemma-3n-e2b](https://huggingface.co/kerasformers/gemma-3n-e2b) |
+| `gemma-3n-e2b-it` | [kerasformers/gemma-3n-e2b-it](https://huggingface.co/kerasformers/gemma-3n-e2b-it) |
+| `gemma-3n-e4b` | [kerasformers/gemma-3n-e4b](https://huggingface.co/kerasformers/gemma-3n-e4b) |
+| `gemma-3n-e4b-it` | [kerasformers/gemma-3n-e4b-it](https://huggingface.co/kerasformers/gemma-3n-e4b-it) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Loads in **bfloat16** by default. Pass `load_dtype="float32"` for full precision,
+  or `quantization="int8"` to shrink further.
+- See the [Gemma 3n docs](https://imvision12.github.io/KerasFormers/gemma3n/).
+- Community / upstream weights still work via the `hf:` prefix:
+  `Gemma3nConditionalGenerate.from_weights("hf:google/gemma-3n-E2B-it")`.
+
+## 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-e2b-it",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "bfloat16",
+  "model_type": "gemma3n",
+  "text_config": {
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+    "embed_dim": 2048,
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+    "num_heads": 8,
+    "num_kv_heads": 2,
+    "head_dim": 256,
+    "sliding_window": 512,
+    "sliding_window_pattern": 5,
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+    "altup_active_idx": 0,
+    "altup_coef_clip": 120.0,
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+    "vocab_offset": 262144,
+    "rms_norm_eps": 1e-06,
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+  "audio_config": {
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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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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.1",
+  "preprocessor_module": "kerasformers.models.gemma3n",
+  "preprocessor_class": "Gemma3nImageProcessor",
+  "variant": "gemma-3n-e2b-it",
+  "size": 768,
+  "rescale_factor": 0.00392156862745098,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
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+    0.5,
+    0.5
+  ]
+}

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