Add support for InternLM2 model architecture

[amdrajeevp1]

Summary

This commit adds support for exporting InternLM2 models to ONNX format.

Key changes:

• Add InternLM2Model class in src/python/py/models/builders/internlm.py
• Register InternLM2ForCausalLM architecture in builder.py
• Implement grouped/interleaved QKV weight splitting for GQA
• Map InternLM2-specific attribute names to base model equivalents
• Add documentation and example in examples/python/internlm2/

Architecture Details

InternLM2 uses a Llama-based architecture with grouped query attention and a unique grouped/interleaved QKV weight layout. The implementation correctly handles this layout during weight extraction.

Tested with:

Tested with InternLM2-1.8B:

• ✅ FP32 export and inference
• ✅ INT4 RTN quantization
• ✅ INT4 AWQ quantization (accuracy level 4)
• ✅ Model generates coherent text and valid Python code

References

• Model: https://huggingface.co/internlm/internlm2-1_8b
• Paper: https://arxiv.org/abs/2403.17297
• GitHub: https://github.com/InternLM/InternLM

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[kunal-vaishnavi]

Thanks for your contribution! Can you also make the following additions for InternLM in alphabetical order?

• Add the model type that gets generated in the genai_config.json here:
  

  onnxruntime-genai/src/models/model_type.h

  Line 15 in 88729a0

  static constexpr std::array<std::string_view, 20> LLM = {"chatglm", "decoder", "ernie4_5", "gemma", "gemma2", "gemma3_text", "gpt2", "gptoss", "granite", "llama", "mistral", "nemotron", "olmo", "phi", "phimoe", "phi3", "phi3small", "qwen2", "qwen3", "smollm3"};

• Add the model type to the model builder README
• Add the model type to the repo's main README