Add SmolLM2 support — reuse the Qwen3 converter path?

[Mikyx-1]

Hi @jan-wassenberg — I'd like to contribute SmolLM2 (135M / 360M / 1.7B) support, and wanted to check the approach with you before writing any code.

SmolLM2 is plain Llama-style: RMSNorm, SwiGLU, RoPE theta 10000, no biases, no QK-norm, tied embeddings, byte-level BPE, ChatML turn tokens. As far as I can tell that needs no new kernels — everything is already on the Qwen3 path.

Sketch:

1. python/convert_from_safetensors.py — the HF tensor names are identical to Qwen3's, so export_qwen3_lm_sbs almost works as-is. The only blockers are the has_qk_norm assert and deriving head_dim from q_norm.weight. Plus a smollm2-* dispatch prefix.
2. gemma/configs.{h,cc} — new Model enum values + config functions.
3. gemma/tokenizer.cc — reuse the Qwen3 branch (same <|im_start|> / <|im_end|>).
4. gemma/gemma.cc — HasEmbeddingScaling() has to return false for it.

Questions:

• Is a third family outside Gemma/Qwen welcome here, or would you rather keep the model list narrow?
• Prefer a family-neutral export_llama_style_lm_sbs that both Qwen3 and SmolLM2 route through, or a separate function?
• For HasEmbeddingScaling, would you rather grow the per-family check, or add a ModelConfig field?

Happy to send a PR if the direction sounds right.