Add SmolLM2 support — reuse the Qwen3 converter path?

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.

先阅读 python/convert_from_safetensors.py 和现有的 Qwen3 路径，然后检查 gemma/configs.{h,cc}、gemma/tokenizer.cc 和 gemma/gemma.cc，以了解模型注册、token 处理和 embedding 缩放。完成的标准是维护者已经选定模型系列和 converter 设计，并且已在不新增 kernel 的情况下实现对 SmolLM2 135M、360M 和 1.7B 的支持。

由索引模型根据 Issue 内容生成。