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llm-arch-research/experiments/llm_only/configs/gemma_generate.json

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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)
2025-10-21 01:02:15 +03:00
{
"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"
}
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