Add support for R-4B multimodal model

[infil00p]

This commit adds support for the R-4B model (YannQi/R-4B), a multimodal large language model with auto-thinking capabilities.

Changes:

• convert_hf_to_gguf.py: Added RVisionModel and RTextModel classes to handle the R model architecture (RForConditionalGeneration)

  • RVisionModel uses LFM2 projector type with scale_factor=1 (no patch merging)
  • RTextModel extends Qwen3Model for the language component
  • Proper tensor name mapping for the projector (pre_norm, linear_1, linear_2)

• tools/mtmd/clip.cpp: Modified build_patch_merge_permute() to support scale_factor=1, which skips patch merging for models that don't need it

  • R model uses SigLIP vision encoder with 729 tokens (27x27 patches)
  • Projector: LayerNorm → Linear → GELU → Linear (no patch downsampling)

Architecture:

• Base text model: Qwen3-4B
• Vision encoder: SigLIP (384x384, patch size 14)
• Projector: 2-layer MLP with pre-normalization (no patch merging)
• Feature selection: full (keeps all 729 vision tokens)

Tested with llama-mtmd-cli and successfully generates English responses with Chinese internal reasoning ( tags).

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