[Feature Proposal] Add GGUF support for Korean-specific LLM architectures
Author: Kim-Ju-won
Pull request: no
Labels: name: Feature request

### Feature request

This is a feature proposal to extend GGUF support in `transformers` for several Korean-specific LLM architectures that have recently been released on the Hugging Face Hub.

Reference issue: [Community contribution: Adding GGUF support for more architectures](https://github.com/huggingface/transformers/issues/33260)

By supporting these models, we can help researchers and developers in the Korean NLP community more easily load, fine-tune, and deploy their models in the efficient GGUF format.

### Motivation

Over the past months, many Korean companies and research institutes (e.g. Naver, LG AI Research, Motif) have published their own LLMs on the Hugging Face Hub.  

Currently, GGUF support in `transformers` is limited to a few well-known architectures (e.g. Llama, Mistral, Qwen2), and I understand that work is already ongoing for some others. However, several Korean-specific architectures are not yet covered by GGUF mappings or converters.  

Adding GGUF support for these models would make them much more accessible for the Korean AI community, enabling efficient inference and experimentation on local machines, edge devices, and research environments. It would also align them with the broader ecosystem of GGML-compatible tools.  

I believe this extension will help lower the barrier for adopting and sharing Korean LLMs across both academia and industry.

### Your contribution

I would like to contribute by extending GGUF support for the following Korean-specific architectures:

| Company | Model architecture | Model name |
|---------|--------------------|------------|
| Naver | `HyperCLOVAXForCausalLM` | naver-hyperclovax/HyperCLOVAX-SEED-Think-14B |
| LG AI Research | `Exaone4ForCausalLM` | LGAI-EXAONE/EXAONE-4.0-1.2B |
| Motif Technologies | `MotifForCausalLM` | Motif-Technologies/Motif-2.6B |

My plan is to follow the same process as outlined in the reference issue:
1. Update `GGUF_TENSOR_MAPPING` and `GGUF_CONFIG_MAPPING`  
2. Implement dedicated `GGUFXXXConverter` classes for the new tokenizers  
3. Write tests for loading/saving  

I am happy to start working on this and would like to know if it is okay for me to proceed with opening a PR.

Comments:
cc @sunmarc maybe?

Hey @Kim-Ju-won, sorry for the very slow reply here, we're catching up on the backlog.

This is a welcome contribution and fits squarely in the community GGUF effort tracked in #33260, so please go ahead and open a PR (ideally one architecture per PR, it keeps review manageable). Two of your targets, `Exaone4ForCausalLM` and `HyperCLOVAXForCausalLM`, already have their base architectures in transformers, so they just need entries in the GGUF mappings see https://github.com/huggingface/transformers/blob/9f909e0f7027285d83bb6addc88155f1b80244ab/src/transformers/integrations/ggml.py#L35
plus a converter class and a loading/saving test. `MotifForCausalLM` isn't in transformers yet, though, so GGUF support for it is blocked until the model itself lands via the new-model flow. I'd drop it from this round.

cc @SunMarc @MekkCyber, who own the GGUF integration and can review your PRs. Thanks for helping extend Korean LLM support!

Hi @qgallouedec, thank you for the detailed guidance — no worries about the delay!
That plan sounds good to me. I'll open two separate PRs, one for `Exaone4ForCausalLM` and one for `HyperCLOVAXForCausalLM`, each including the GGUF_CONFIG_MAPPING entry, the converter class, and loading/saving tests as you described.
Understood on MotifForCausalLM — I'll drop it from this round and revisit GGUF support once the model lands in transformers through the new-model flow.
I'll link both PRs to #33260 and tag @SunMarc and @MekkCyber for review. Thanks again!