Original model: https://huggingface.co./brucethemoose/Yi-34B-200K-RPMerge

Steps:
1. Convert to GGUF using llama.cpp (clone from source, install requirements, then run this)
   > `python convert.py /mnt/d/LLM_Models/Yi-34B-200K-RPMerge/ --vocab-type hfft --outtype f32 --outfile Yi-34B-200K-RPMerge.gguf`
2. Create imatrix (offload as much as you can to the GPU)
   > `./imatrix -m /mnt/d/LLM_Models/Yi-34B-200K-RPMerge.gguf -f /mnt/d/LLM_Models/8k_random_data.txt -o /mnt/d/LLM_Models/Yi-34B-200K-RPMerge.imatrix.dat -ngl 20`
3. Quantize using imatrix
   > `./quantize --imatrix /mnt/d/LLM_Models/Yi-34B-200K-RPMerge.imatrix.dat /mnt/d/LLM_Models/Yi-34B-200K-RPMerge.gguf /mnt/d/LLM_Models/Yi-34B-200K-RPMerge.IQ2_XXS.gguf IQ2_XXS

I have also uploaded [8k_random_data.txt from this github discussion](https://github.com/ggerganov/llama.cpp/discussions/5006)
And the importance matrix I made (`Yi-34B-200K-RPMerge.imatrix.dat`)