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Koboldcpp 24

hi,sorry for report bug here, no on github (shadow ban me for freevpn dirty ip ,i just forget to turn off vpn and login in )
after a adding patch for llama 3.1 ropefix cause bug, wrong detection of quant type not only llama models and cause quality loss,
Llama-3.1-8B-Instruct-abliterated.Q4_K_S but detected as Q3_K - Large

llm_load_print_meta: model ftype = Q3_K - Large

llama3.1 log

Welcome to KoboldCpp - Version 1.71.1
For command line arguments, please refer to --help
Loading model: /media/user/sdc1/Llama-3.1-8B-Instruct-abliterated.Q4_K_S.gguf

The reported GGUF Arch is: llama
Arch Category: 0
llama_model_loader: loaded meta data with 33 key-value pairs and 292 tensors from /media/user/sdc1/llama3/Llama-3.1-8B-Instruct-abliterated.Q4_K_S.gguf (version GGUF V3 (latest))
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = Q3_K - Large
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.36 GiB (4.67 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 8B Instruct Abliterated
llm_load_print_meta: BOS token = 128000 <|begin_of_text|>
llm_load_print_meta: EOS token = 128009 <|eot_id|>
llm_load_print_meta: LF token = 128 Ä
llm_load_print_meta: EOT token = 128009 <|eot_id|>
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.32 MiB
llm_load_tensors: offloading 13 repeating layers to GPU
llm_load_tensors: offloaded 13/33 layers to GPU
llm_load_tensors: CPU buffer size = 4467.80 MiB
llm_load_tensors: OpenCL buffer size = 1521.46 MiB

also non llama qwen2

Welcome to KoboldCpp - Version 1.71.1
For command line arguments, please refer to --help

Loading model: /media/user/sdc1/qwen2/Qwen2-7B-Instruct-abliterated-Q4_K_M-imat.0.gguf

The reported GGUF Arch is: qwen2
Arch Category: 5

Identified as GGUF model: (ver 6)
Attempting to Load...

llama_model_loader: loaded meta data with 25 key-value pairs and 339 tensors from /media/user/sdc1/qwen2/Qwen2-7B-Instruct-abliterated-Q4_K_M-imat.0.gguf (version GGUF V3 (latest))
llm_load_vocab: special tokens cache size = 421
llm_load_vocab: token to piece cache size = 0.9352 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 152064
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 18944
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = Q3_K - Large
llm_load_print_meta: model params = 7.62 B
llm_load_print_meta: model size = 4.36 GiB (4.91 BPW)
llm_load_print_meta: general.name = Qwen2-7B-Instruct-abliterated
llm_load_print_meta: BOS token = 151643 <|endoftext|>
llm_load_print_meta: EOS token = 151645 <|im_end|>
llm_load_print_meta: PAD token = 151643 <|endoftext|>
llm_load_print_meta: LF token = 148848 ÄĬ
llm_load_print_meta: EOT token = 151645 <|im_end|>
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.35 MiB
llm_load_tensors: offloading 13 repeating layers to GPU
llm_load_tensors: offloaded 13/29 layers to GPU
llm_load_tensors: CPU buffer size = 4460.45 MiB