llama_model_loader: loaded meta data with 39 key-value pairs and 508 tensors from datagemma-rig-27b-it-IMat-GGUF/datagemma-rig-27b-it.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              = Rig_27B_Transformers_Checkpoint_15000
llama_model_loader: - kv   3:                           general.finetune str              = it
llama_model_loader: - kv   4:                           general.basename str              = datagemma-rig
llama_model_loader: - kv   5:                         general.size_label str              = 27B
llama_model_loader: - kv   6:                            general.license str              = gemma
llama_model_loader: - kv   7:                   general.base_model.count u32              = 1
llama_model_loader: - kv   8:                  general.base_model.0.name str              = Gemma 2 27b It
llama_model_loader: - kv   9:          general.base_model.0.organization str              = Google
llama_model_loader: - kv  10:              general.base_model.0.repo_url str              = https://huggingface.co/google/gemma-2...
llama_model_loader: - kv  11:                               general.tags arr[str,2]       = ["conversational", "text-generation"]
llama_model_loader: - kv  12:                      gemma2.context_length u32              = 8192
llama_model_loader: - kv  13:                    gemma2.embedding_length u32              = 4608
llama_model_loader: - kv  14:                         gemma2.block_count u32              = 46
llama_model_loader: - kv  15:                 gemma2.feed_forward_length u32              = 36864
llama_model_loader: - kv  16:                gemma2.attention.head_count u32              = 32
llama_model_loader: - kv  17:             gemma2.attention.head_count_kv u32              = 16
llama_model_loader: - kv  18:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  19:                gemma2.attention.key_length u32              = 128
llama_model_loader: - kv  20:              gemma2.attention.value_length u32              = 128
llama_model_loader: - kv  21:                          general.file_type u32              = 7
llama_model_loader: - kv  22:              gemma2.attn_logit_softcapping f32              = 50.000000
llama_model_loader: - kv  23:             gemma2.final_logit_softcapping f32              = 30.000000
llama_model_loader: - kv  24:            gemma2.attention.sliding_window u32              = 4096
llama_model_loader: - kv  25:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  26:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  27:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  28:                      tokenizer.ggml.scores arr[f32,256000]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  32:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  33:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  34:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  35:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...
llama_model_loader: - kv  37:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  38:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  185 tensors
llama_model_loader: - type q8_0:  323 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           = 4608
llm_load_print_meta: n_layer          = 46
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 16
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_swa            = 4096
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
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             = 36864
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 27B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 27.23 B
llm_load_print_meta: model size       = 26.94 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = Rig_27B_Transformers_Checkpoint_15000
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 =    0.45 MiB
llm_load_tensors: offloading 37 repeating layers to GPU
llm_load_tensors: offloaded 37/47 layers to GPU
llm_load_tensors:        CPU buffer size = 27591.06 MiB
llm_load_tensors:      CUDA0 buffer size = 21231.35 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:  CUDA_Host KV buffer size =    36.00 MiB
llama_kv_cache_init:      CUDA0 KV buffer size =   148.00 MiB
llama_new_context_with_model: KV self size  =  184.00 MiB, K (f16):   92.00 MiB, V (f16):   92.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =  1704.31 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =    11.01 MiB
llama_new_context_with_model: graph nodes  = 1850
llama_new_context_with_model: graph splits = 121

system_info: n_threads = 25 (n_threads_batch = 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 | RISCV_VECT = 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 128.146 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 2.14 seconds per pass - ETA 4.55 minutes
[1]105.3030,[2]48.2459,[3]37.3551,[4]64.6273,[5]58.9392,[6]37.2118,[7]48.0783,[8]55.2094,[9]60.8348,
save_imatrix: stored collected data after 10 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[10]47.4368,[11]52.2511,[12]62.5349,[13]72.8301,[14]76.6891,[15]88.0492,[16]94.4319,[17]98.4147,[18]102.0178,[19]88.1819,
save_imatrix: stored collected data after 20 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[20]98.0518,[21]98.3230,[22]98.1572,[23]99.9073,[24]99.7030,[25]107.9212,[26]102.9446,[27]106.7155,[28]111.2190,[29]111.2565,
save_imatrix: stored collected data after 30 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[30]109.9259,[31]97.0510,[32]90.6971,[33]90.2245,[34]89.5476,[35]90.3216,[36]88.7417,[37]89.0847,[38]91.9367,[39]95.8369,
save_imatrix: stored collected data after 40 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[40]97.8025,[41]100.7910,[42]105.4919,[43]109.2177,[44]112.0317,[45]113.8144,[46]112.3453,[47]112.6577,[48]117.0320,[49]120.4270,
save_imatrix: stored collected data after 50 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[50]118.2151,[51]119.8638,[52]120.5760,[53]125.0480,[54]129.1875,[55]130.9818,[56]131.6318,[57]132.9954,[58]133.8396,[59]129.0727,
save_imatrix: stored collected data after 60 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[60]126.6368,[61]123.9368,[62]125.3806,[63]126.4441,[64]127.2459,[65]127.0590,[66]127.9807,[67]126.9014,[68]125.0932,[69]126.0750,
save_imatrix: stored collected data after 70 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[70]125.7800,[71]125.5559,[72]125.7992,[73]126.4120,[74]125.5738,[75]124.9096,[76]124.8906,[77]125.2136,[78]125.9563,[79]124.9185,
save_imatrix: stored collected data after 80 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[80]127.0135,[81]128.7838,[82]128.5292,[83]129.6607,[84]131.4402,[85]126.4915,[86]126.0314,[87]124.2917,[88]124.8511,[89]125.4370,
save_imatrix: stored collected data after 90 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[90]125.3858,[91]123.5666,[92]121.7223,[93]119.4879,[94]117.2487,[95]115.7179,[96]114.0167,[97]112.5808,[98]110.9505,[99]111.4794,
save_imatrix: stored collected data after 100 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[100]112.0440,[101]114.0937,[102]115.4479,[103]117.9564,[104]120.7776,[105]123.3646,[106]123.9526,[107]124.5521,[108]124.0001,[109]123.4625,
save_imatrix: stored collected data after 110 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[110]122.9281,[111]121.4704,[112]119.4281,[113]121.1361,[114]122.5180,[115]122.2572,[116]122.8735,[117]124.5789,[118]124.8416,[119]125.0191,
save_imatrix: stored collected data after 120 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat
[120]125.5826,[121]126.2783,[122]125.2195,[123]125.7870,[124]126.1228,[125]126.8911,[126]127.9069,[127]129.0043,[128]128.6395,
save_imatrix: stored collected data after 128 chunks in datagemma-rig-27b-it-IMat-GGUF/imatrix.dat

llama_perf_print:        load time =    5108.73 ms
llama_perf_print: prompt eval time =  224316.01 ms / 65536 tokens (    3.42 ms per token,   292.16 tokens per second)
llama_perf_print:        eval time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_perf_print:       total time =  228887.72 ms / 65537 tokens

Final estimate: PPL = 128.6395 +/- 4.82771