Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+pipeline_tag: text-generation
+license: gemma
+base_model: google/gemma-2-2b
+library_name: kerasformers
+extra_gated_heading: Access Gemma on Hugging Face
+language:
+- en
+tags:
+- keras
+- kerasformers
+- gemma2
+- gemma
+- gemma-2-2b
+- text-generation
+- arxiv:2408.00118
+- pytorch
+- jax
+- tf
+---
+
+*See [our collection](https://huggingface.co/kerasformers) for all Gemma 2 sizes and variants.*
+
+# Run Gemma 2 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_2-1f6feb)](https://imvision12.github.io/KerasFormers/gemma2/) [![HuggingFace](https://img.shields.io/badge/HuggingFace-Gemma_2-ffd21e?logo=huggingface&logoColor=black)](https://huggingface.co/kerasformers)
+
+# kerasformers/gemma-2-2b
+
+Pure-**Keras 3** conversion of [`google/gemma-2-2b`](https://huggingface.co/google/gemma-2-2b) for
+[kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on
+**TensorFlow / Torch / JAX**. This is a base (pretrained) checkpoint, served here as **text -> text** via `Gemma2TextGenerate`; weights are
+stored in **bfloat16**.
+
+For model details, license, and usage terms, see Google's
+[model card](https://huggingface.co/google/gemma-2-2b).
+
+## ✨ Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.gemma2 import Gemma2TextGenerate, Gemma2Tokenizer
+
+model = Gemma2TextGenerate.from_weights("kerasformers/gemma-2-2b")
+tokenizer = Gemma2Tokenizer.from_weights("kerasformers/gemma-2-2b")
+
+inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
+outputs = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.decode(outputs[0]))
+```
+
+Load any Gemma 2 variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub |
+| --- | --- |
+| `gemma-2-27b` | [kerasformers/gemma-2-27b](https://huggingface.co/kerasformers/gemma-2-27b) |
+| `gemma-2-27b-it` | [kerasformers/gemma-2-27b-it](https://huggingface.co/kerasformers/gemma-2-27b-it) |
+| `gemma-2-2b` | [kerasformers/gemma-2-2b](https://huggingface.co/kerasformers/gemma-2-2b) |
+| `gemma-2-2b-it` | [kerasformers/gemma-2-2b-it](https://huggingface.co/kerasformers/gemma-2-2b-it) |
+| `gemma-2-9b` | [kerasformers/gemma-2-9b](https://huggingface.co/kerasformers/gemma-2-9b) |
+| `gemma-2-9b-it` | [kerasformers/gemma-2-9b-it](https://huggingface.co/kerasformers/gemma-2-9b-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 2 docs](https://imvision12.github.io/KerasFormers/gemma2/).
+- Community / upstream weights still work via the `hf:` prefix:
+  `Gemma2TextGenerate.from_weights("hf:google/gemma-2-2b")`.
+
+## 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.gemma2",
+  "model_class": "Gemma2TextGenerate",
+  "variant": "gemma-2-2b",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "bfloat16",
+  "model_type": "gemma2",
+  "text_config": {
+    "vocab_size": 256000,
+    "embed_dim": 2304,
+    "mlp_dim": 9216,
+    "num_layers": 26,
+    "num_heads": 8,
+    "num_kv_heads": 4,
+    "head_dim": 256,
+    "query_pre_attn_scalar": 256.0,
+    "attn_logit_softcapping": 50.0,
+    "final_logit_softcapping": 30.0,
+    "sliding_window": 4096,
+    "norm_eps": 1e-06,
+    "rope_theta": 10000.0,
+    "tie_embeddings": true
+  }
+}

model.weights.h5

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tokenizer.json

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