feat(gemma): initial implementation of Gemma model and configs

- Add core Gemma model (architecture, attention, GeGLU, RoPE, RMSNorm, etc)
- Add configs for training and generation: gemma_train.json, gemma_generate.json
- Add Gemma notebook for exploratory analysis and demonstration
- Add __init__.py for Gemma submodule
- Update run_llm_experiment.py to support Gemma experiment configs

test(gemma): add comprehensive unit tests for Gemma

- Test forward pass (with/without cache)
- Test autoregressive generation (greedy, top-k, top-p)
- Test shape correctness and max sequence length errors
- Test multi-layer stack and token embeddings

docs: add documentation notebook for Gemma usage and analysis

Closes: #issue (if applicable)

experiments/llm_only/configs/gemma_generate.json

@@ -0,0 +1,19 @@
+{
+    "bpe_tokenizer": "checkpoints/bpe_tokenizer.json",
+    "test_prompts": [
+      "Open weights",
+      "The Llama model is",
+      "Efficient transformers"
+    ],
+    "model_config_path": "checkpoints/gemma-bpe/config.json",
+    "model_weights": "checkpoints/gemma-bpe/model.pt",
+    "generation": {
+      "max_new_tokens": 40,
+      "temperature": 0.8,
+      "do_sample": true,
+      "top_k": null,
+      "top_p": null
+    },
+    "log_path": "checkpoints/gemma_only_generation_logs.json"
+  }
+