Eval bug: GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type") failed with Vulkan #12815

.\llama-server.exe --embedding -ub 8192 -b 8192 -c 8192 --host 127.0.0.1 --port 8080 -m snowflake-arctic-embed-l-v2.0-q8_0.gguf -ngl 99 -fa -ctk q8_0 -ctv q8_0
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon RX 6600M (AMD proprietary driver) | uma: 0 | fp16: 1 | warp size: 32 | shared memory: 32768 | int dot: 1 | matrix cores: none
build: 5061 (916c83bf) with MSVC 19.38.33134.0 for x64
system info: n_threads = 8, n_threads_batch = 8, total_threads = 16

system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |

main: binding port with default address family
main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 15
main: loading model
srv    load_model: loading model 'snowflake-arctic-embed-l-v2.0-q8_0.gguf'
llama_model_load_from_file_impl: using device Vulkan0 (AMD Radeon RX 6600M) - 8176 MiB free
llama_model_loader: loaded meta data with 36 key-value pairs and 389 tensors from snowflake-arctic-embed-l-v2.0-q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = bert
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Snowflake Arctic Embed L v2.0
llama_model_loader: - kv   3:                            general.version str              = v2.0
llama_model_loader: - kv   4:                           general.basename str              = snowflake-arctic-embed-l
llama_model_loader: - kv   5:                         general.size_label str              = 567M
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                               general.tags arr[str,8]       = ["sentence-transformers", "feature-ex...
llama_model_loader: - kv   8:                          general.languages arr[str,74]      = ["af", "ar", "az", "be", "bg", "bn", ...
llama_model_loader: - kv   9:                           bert.block_count u32              = 24
llama_model_loader: - kv  10:                        bert.context_length u32              = 8192
llama_model_loader: - kv  11:                      bert.embedding_length u32              = 1024
llama_model_loader: - kv  12:                   bert.feed_forward_length u32              = 4096
llama_model_loader: - kv  13:                  bert.attention.head_count u32              = 16
llama_model_loader: - kv  14:          bert.attention.layer_norm_epsilon f32              = 0.000010
llama_model_loader: - kv  15:                          general.file_type u32              = 7
llama_model_loader: - kv  16:                      bert.attention.causal bool             = false
llama_model_loader: - kv  17:                          bert.pooling_type u32              = 2
llama_model_loader: - kv  18:                       tokenizer.ggml.model str              = t5
llama_model_loader: - kv  19:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  20:                      tokenizer.ggml.tokens arr[str,250002]  = ["<s>", "<pad>", "</s>", "<unk>", ","...
llama_model_loader: - kv  21:                      tokenizer.ggml.scores arr[f32,250002]  = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  22:                  tokenizer.ggml.token_type arr[i32,250002]  = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  23:            tokenizer.ggml.add_space_prefix bool             = true
llama_model_loader: - kv  24:            tokenizer.ggml.token_type_count u32              = 1
llama_model_loader: - kv  25:    tokenizer.ggml.remove_extra_whitespaces bool             = true
llama_model_loader: - kv  26:        tokenizer.ggml.precompiled_charsmap arr[u8,237539]   = [0, 180, 2, 0, 0, 132, 0, 0, 0, 0, 0,...
llama_model_loader: - kv  27:                tokenizer.ggml.bos_token_id u32              = 0
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  29:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  30:          tokenizer.ggml.seperator_token_id u32              = 2
llama_model_loader: - kv  31:            tokenizer.ggml.padding_token_id u32              = 1
llama_model_loader: - kv  32:               tokenizer.ggml.mask_token_id u32              = 250001
llama_model_loader: - kv  33:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  34:               tokenizer.ggml.add_eos_token bool             = true
llama_model_loader: - kv  35:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  244 tensors
llama_model_loader: - type q8_0:  145 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 598.63 MiB (8.86 BPW)
load: model vocab missing newline token, using special_pad_id instead
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 4
load: token to piece cache size = 2.1668 MB
print_info: arch             = bert
print_info: vocab_only       = 0
print_info: n_ctx_train      = 8192
print_info: n_embd           = 1024
print_info: n_layer          = 24
print_info: n_head           = 16
print_info: n_head_kv        = 16
print_info: n_rot            = 64
print_info: n_swa            = 0
print_info: n_swa_pattern    = 1
print_info: n_embd_head_k    = 64
print_info: n_embd_head_v    = 64
print_info: n_gqa            = 1
print_info: n_embd_k_gqa     = 1024
print_info: n_embd_v_gqa     = 1024
print_info: f_norm_eps       = 1.0e-05
print_info: f_norm_rms_eps   = 0.0e+00
print_info: f_clamp_kqv      = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale    = 0.0e+00
print_info: f_attn_scale     = 0.0e+00
print_info: n_ff             = 4096
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: causal attn      = 0
print_info: pooling type     = 2
print_info: rope type        = 2
print_info: rope scaling     = linear
print_info: freq_base_train  = 10000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn  = 8192
print_info: rope_finetuned   = unknown
print_info: ssm_d_conv       = 0
print_info: ssm_d_inner      = 0
print_info: ssm_d_state      = 0
print_info: ssm_dt_rank      = 0
print_info: ssm_dt_b_c_rms   = 0
print_info: model type       = 335M
print_info: model params     = 566.70 M
print_info: general.name     = Snowflake Arctic Embed L v2.0
print_info: vocab type       = UGM
print_info: n_vocab          = 250002
print_info: n_merges         = 0
print_info: BOS token        = 0 '<s>'
print_info: EOS token        = 2 '</s>'
print_info: UNK token        = 3 '<unk>'
print_info: SEP token        = 2 '</s>'
print_info: PAD token        = 1 '<pad>'
print_info: MASK token       = 250001 '[PAD250000]'
print_info: LF token         = 0 '<s>'
print_info: EOG token        = 2 '</s>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 24 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 25/25 layers to GPU
load_tensors:      Vulkan0 model buffer size =   307.22 MiB
load_tensors:   CPU_Mapped model buffer size =   291.41 MiB
......................................................
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 8192
llama_context: n_ctx_per_seq = 8192
llama_context: n_batch       = 8192
llama_context: n_ubatch      = 8192
llama_context: causal_attn   = 0
llama_context: flash_attn    = 1
llama_context: freq_base     = 10000.0
llama_context: freq_scale    = 1
llama_context: Vulkan_Host  output buffer size =     0.00 MiB
init: kv_size = 8192, offload = 1, type_k = 'q8_0', type_v = 'q8_0', n_layer = 24, can_shift = 1
init:    Vulkan0 KV buffer size =   408.00 MiB
llama_context: KV self size  =  408.00 MiB, K (q8_0):  204.00 MiB, V (q8_0):  204.00 MiB
llama_context:    Vulkan0 compute buffer size =   512.00 MiB
llama_context: Vulkan_Host compute buffer size =   320.09 MiB
llama_context: graph nodes  = 706 (with bs=8192), 826 (with bs=1)
llama_context: graph splits = 52 (with bs=8192), 2 (with bs=1)
common_init_from_params: setting dry_penalty_last_n to ctx_size = 8192
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv          init: initializing slots, n_slots = 1
slot         init: id  0 | task -1 | new slot n_ctx_slot = 8192
main: model loaded
main: chat template, chat_template: {%- for message in messages -%}
  {{- '<|im_start|>' + message.role + '
' + message.content + '<|im_end|>
' -}}
{%- endfor -%}
{%- if add_generation_prompt -%}
  {{- '<|im_start|>assistant
' -}}
{%- endif -%}, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
'
main: server is listening on http://127.0.0.1:8080 - starting the main loop
srv  update_slots: all slots are idle
slot launch_slot_: id  0 | task 0 | processing task
slot update_slots: id  0 | task 0 | new prompt, n_ctx_slot = 8192, n_keep = 0, n_prompt_tokens = 11
slot update_slots: id  0 | task 0 | kv cache rm [0, end)
slot update_slots: id  0 | task 0 | prompt processing progress, n_past = 11, n_tokens = 11, progress = 1.000000
slot update_slots: id  0 | task 0 | prompt done, n_past = 11, n_tokens = 11
slot      release: id  0 | task 0 | stop processing: n_past = 11, truncated = 0
slot launch_slot_: id  0 | task 17 | processing task
slot update_slots: id  0 | task 17 | new prompt, n_ctx_slot = 8192, n_keep = 0, n_prompt_tokens = 1024
slot update_slots: id  0 | task 17 | kv cache rm [0, end)
slot update_slots: id  0 | task 17 | prompt processing progress, n_past = 1024, n_tokens = 1024, progress = 1.000000
slot update_slots: id  0 | task 17 | prompt done, n_past = 1024, n_tokens = 1024
C:\Sources\llama.cpp\ggml\src\ggml-cpu\ggml-cpu.c:10344: GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type") failed
C:\Sources\llama.cpp\ggml\src\ggml-cpu\ggml-cpu.c:10344: