model: support Rnj-1

[philip-essential]

This adds support for Rnj-1, which is an 8B model we just released. We've been using llama.cpp to play around with the model internally, and we released a GGUF checkpoint for the instruction-tuned version.

The model architecture is similar enough to Gemma3 that in Transformers/VLLM/SGLang we can reuse the same model file. However, in llama.cpp we need some small changes, so I've added a new implementation, based closely on the Gemma3 one. The changes are:

• All layers use global attention.
• Long-context is via YaRN.

Because our huggingface config.json uses "Gemma3ForCausalLM" as the architecture, convert_hf_to_gguf.py is unable to tell that these configs are for Rnj-1. The solution I came up with is to manually change the architecture to Rnj1ForCausalLM before converting the checkpoint. I added a note in convert_hf_to_gguf.py about this. But perhaps there's a better solution?

[CISC]

Because our huggingface config.json uses "Gemma3ForCausalLM" as the architecture, convert_hf_to_gguf.py is unable to tell that these configs are for Rnj-1. The solution I came up with is to manually change the architecture to Rnj1ForCausalLM before converting the checkpoint. I added a note in convert_hf_to_gguf.py about this. But perhaps there's a better solution?

Instead change llm_build_gemma3_iswa into a templated llm_build_gemma3, like f.ex. smallthinker and add support for YaRN and non-SWA in Gemma3Model conversion.