llama_model_loader: loaded meta data with 34 key-value pairs and 464 tensors from shieldgemma-9b-IMat-GGUF/shieldgemma-9b.Q8_0.gguf.hardlink.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              = gemma2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Shieldgemma 9b
llama_model_loader: - kv   3:                           general.basename str              = shieldgemma
llama_model_loader: - kv   4:                         general.size_label str              = 9B
llama_model_loader: - kv   5:                            general.license str              = gemma
llama_model_loader: - kv   6:                               general.tags arr[str,1]       = ["text-generation"]
llama_model_loader: - kv   7:                      gemma2.context_length u32              = 8192
llama_model_loader: - kv   8:                    gemma2.embedding_length u32              = 3584
llama_model_loader: - kv   9:                         gemma2.block_count u32              = 42
llama_model_loader: - kv  10:                 gemma2.feed_forward_length u32              = 14336
llama_model_loader: - kv  11:                gemma2.attention.head_count u32              = 16
llama_model_loader: - kv  12:             gemma2.attention.head_count_kv u32              = 8
llama_model_loader: - kv  13:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  14:                gemma2.attention.key_length u32              = 256
llama_model_loader: - kv  15:              gemma2.attention.value_length u32              = 256
llama_model_loader: - kv  16:                          general.file_type u32              = 7
llama_model_loader: - kv  17:              gemma2.attn_logit_softcapping f32              = 50.000000
llama_model_loader: - kv  18:             gemma2.final_logit_softcapping f32              = 30.000000
llama_model_loader: - kv  19:            gemma2.attention.sliding_window u32              = 4096
llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  23:                      tokenizer.ggml.scores arr[f32,256000]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  24:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  25:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  26:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  27:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  28:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  29:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  30:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  31:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if messages[-1]....
llama_model_loader: - kv  32:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  33:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  169 tensors
llama_model_loader: - type q8_0:  295 tensors
llm_load_vocab: special tokens cache size = 249
llm_load_vocab: token to piece cache size = 1.6014 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = gemma2
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 256000
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 3584
llm_load_print_meta: n_layer          = 42
llm_load_print_meta: n_head           = 16
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_rot            = 256
llm_load_print_meta: n_swa            = 4096
llm_load_print_meta: n_embd_head_k    = 256
llm_load_print_meta: n_embd_head_v    = 256
llm_load_print_meta: n_gqa            = 2
llm_load_print_meta: n_embd_k_gqa     = 2048
llm_load_print_meta: n_embd_v_gqa     = 2048
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             = 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        = 2
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  = 8192
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       = 9B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 9.24 B
llm_load_print_meta: model size       = 9.15 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = Shieldgemma 9b
llm_load_print_meta: BOS token        = 2 '<bos>'
llm_load_print_meta: EOS token        = 1 '<eos>'
llm_load_print_meta: UNK token        = 3 '<unk>'
llm_load_print_meta: PAD token        = 0 '<pad>'
llm_load_print_meta: LF token         = 227 '<0x0A>'
llm_load_print_meta: EOT token        = 107 '<end_of_turn>'
llm_load_print_meta: max token length = 48
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size =    0.41 MiB
llm_load_tensors: offloading 42 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 43/43 layers to GPU
llm_load_tensors:        CPU buffer size =   929.69 MiB
llm_load_tensors:      CUDA0 buffer size =  9366.12 MiB
....................................................................................
llama_new_context_with_model: n_ctx      = 512
llama_new_context_with_model: n_batch    = 512
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =   168.00 MiB
llama_new_context_with_model: KV self size  =  168.00 MiB, K (f16):   84.00 MiB, V (f16):   84.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   507.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     9.01 MiB
llama_new_context_with_model: graph nodes  = 1690
llama_new_context_with_model: graph splits = 2

system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
compute_imatrix: tokenizing the input ..
compute_imatrix: tokenization took 127.069 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 0.83 seconds per pass - ETA 1.77 minutes
[1]7.3374,[2]5.0984,[3]4.5613,[4]5.6956,[5]5.8577,[6]4.9333,[7]5.4379,[8]5.7817,[9]6.0045,
save_imatrix: stored collected data after 10 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[10]5.3221,[11]5.4437,[12]6.0204,[13]6.5565,[14]6.7867,[15]7.3188,[16]7.6393,[17]7.7817,[18]8.1130,[19]7.7774,
save_imatrix: stored collected data after 20 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[20]7.9485,[21]8.1086,[22]8.0617,[23]8.2047,[24]8.3050,[25]8.4663,[26]8.1978,[27]8.4473,[28]8.6323,[29]8.5557,
save_imatrix: stored collected data after 30 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[30]8.4874,[31]7.9852,[32]7.7378,[33]7.6474,[34]7.5178,[35]7.4549,[36]7.4790,[37]7.4819,[38]7.5538,[39]7.7192,
save_imatrix: stored collected data after 40 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[40]7.8833,[41]8.0263,[42]8.3116,[43]8.6135,[44]8.8847,[45]9.0421,[46]8.8968,[47]8.9272,[48]9.1215,[49]9.2696,
save_imatrix: stored collected data after 50 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[50]9.0588,[51]9.0860,[52]9.1306,[53]9.2732,[54]9.4697,[55]9.5793,[56]9.6371,[57]9.6371,[58]9.6535,[59]9.4988,
save_imatrix: stored collected data after 60 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[60]9.3814,[61]9.2582,[62]9.2137,[63]9.2545,[64]9.2488,[65]9.2318,[66]9.2642,[67]9.2104,[68]9.1374,[69]9.1585,
save_imatrix: stored collected data after 70 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[70]9.1244,[71]9.1132,[72]9.1258,[73]9.1023,[74]9.0472,[75]9.0081,[76]9.0088,[77]9.0233,[78]9.0102,[79]8.9599,
save_imatrix: stored collected data after 80 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[80]9.0174,[81]9.0655,[82]9.0441,[83]9.0438,[84]9.0963,[85]8.9669,[86]8.9308,[87]8.8686,[88]8.8771,[89]8.9035,
save_imatrix: stored collected data after 90 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[90]8.9240,[91]8.8541,[92]8.7786,[93]8.6921,[94]8.6069,[95]8.5458,[96]8.4692,[97]8.4011,[98]8.3388,[99]8.3775,
save_imatrix: stored collected data after 100 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[100]8.4012,[101]8.4919,[102]8.5715,[103]8.6479,[104]8.8050,[105]8.9196,[106]8.9416,[107]8.9679,[108]8.9875,[109]8.9641,
save_imatrix: stored collected data after 110 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[110]8.9429,[111]8.8727,[112]8.7988,[113]8.8436,[114]8.8606,[115]8.8644,[116]8.8585,[117]8.9005,[118]8.9193,[119]8.9265,
save_imatrix: stored collected data after 120 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat
[120]8.9359,[121]8.9646,[122]8.9229,[123]8.9784,[124]9.0340,[125]9.0724,[126]9.1437,[127]9.2022,[128]9.2556,
save_imatrix: stored collected data after 128 chunks in shieldgemma-9b-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =    2647.18 ms
llama_print_timings:      sample time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_print_timings: prompt eval time =   87099.37 ms / 65536 tokens (    1.33 ms per token,   752.43 tokens per second)
llama_print_timings:        eval time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_print_timings:       total time =   90378.31 ms / 65537 tokens

Final estimate: PPL = 9.2556 +/- 0.15057