llama_model_loader: loaded meta data with 35 key-value pairs and 363 tensors from Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/Mistral-NeMo-Minitron-8B-Base.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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Mistral NeMo Minitron 8B Base
llama_model_loader: - kv   3:                       general.organization str              = Nvidia
llama_model_loader: - kv   4:                           general.finetune str              = Base
llama_model_loader: - kv   5:                           general.basename str              = Mistral-NeMo-Minitron
llama_model_loader: - kv   6:                         general.size_label str              = 8B
llama_model_loader: - kv   7:                            general.license str              = other
llama_model_loader: - kv   8:                       general.license.name str              = nvidia-open-model-license
llama_model_loader: - kv   9:                       general.license.link str              = https://developer.download.nvidia.com...
llama_model_loader: - kv  10:                          llama.block_count u32              = 40
llama_model_loader: - kv  11:                       llama.context_length u32              = 8192
llama_model_loader: - kv  12:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv  13:                  llama.feed_forward_length u32              = 11520
llama_model_loader: - kv  14:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  15:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  16:                       llama.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  17:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  18:                 llama.attention.key_length u32              = 128
llama_model_loader: - kv  19:               llama.attention.value_length u32              = 128
llama_model_loader: - kv  20:                          general.file_type u32              = 7
llama_model_loader: - kv  21:                           llama.vocab_size u32              = 131072
llama_model_loader: - kv  22:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  23:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = tekken
llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,131072]  = ["<unk>", "<s>", "</s>", "[INST]", "[...
llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,131072]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,269443]  = ["Ġ Ġ", "Ġ t", "e r", "i n", "Ġ �...
llama_model_loader: - kv  29:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  30:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  31:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  32:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  33:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  34:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   81 tensors
llama_model_loader: - type q8_0:  282 tensors
llm_load_vocab: special tokens cache size = 1000
llm_load_vocab: token to piece cache size = 0.8498 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          = 131072
llm_load_print_meta: n_merges         = 269443
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_layer          = 40
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             = 11520
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  = 1000000.0
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       = 13B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 8.41 B
llm_load_print_meta: model size       = 8.33 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = Mistral NeMo Minitron 8B Base
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 2 '</s>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: LF token         = 1196 'Ä'
llm_load_print_meta: max token length = 150
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 =    0.34 MiB
llm_load_tensors: offloading 40 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 41/41 layers to GPU
llm_load_tensors:        CPU buffer size =   544.00 MiB
llm_load_tensors:      CUDA0 buffer size =  7982.78 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  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =    80.00 MiB
llama_new_context_with_model: KV self size  =   80.00 MiB, K (f16):   40.00 MiB, V (f16):   40.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.50 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   264.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     9.01 MiB
llama_new_context_with_model: graph nodes  = 1286
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 108.019 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 0.81 seconds per pass - ETA 1.72 minutes
[1]5.0917,[2]3.8243,[3]3.5221,[4]4.3283,[5]4.4019,[6]3.8244,[7]4.1977,[8]4.4898,[9]4.7123,
save_imatrix: stored collected data after 10 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[10]4.2822,[11]4.5663,[12]4.8948,[13]5.2375,[14]5.4594,[15]5.7700,[16]5.9816,[17]6.1292,[18]6.3001,[19]6.0685,
save_imatrix: stored collected data after 20 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[20]6.1563,[21]6.3118,[22]6.2370,[23]6.3808,[24]6.3845,[25]6.5794,[26]6.4172,[27]6.0985,[28]6.1277,[29]6.0738,
save_imatrix: stored collected data after 30 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[30]6.0397,[31]5.7730,[32]5.6212,[33]5.5789,[34]5.5094,[35]5.4731,[36]5.6957,[37]5.6947,[38]5.7691,[39]5.8936,
save_imatrix: stored collected data after 40 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[40]6.0177,[41]6.1334,[42]6.2400,[43]6.4518,[44]6.6405,[45]6.7468,[46]6.6502,[47]6.6859,[48]6.8168,[49]6.9292,
save_imatrix: stored collected data after 50 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[50]6.7899,[51]6.7992,[52]6.8174,[53]6.8827,[54]6.9915,[55]7.0901,[56]7.1331,[57]7.1425,[58]7.1123,[59]7.0535,
save_imatrix: stored collected data after 60 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[60]7.0041,[61]6.9069,[62]6.8566,[63]6.9065,[64]6.9412,[65]6.8610,[66]6.8269,[67]6.8264,[68]6.7904,[69]6.7598,
save_imatrix: stored collected data after 70 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[70]6.7628,[71]6.7871,[72]6.7442,[73]6.7638,[74]6.7438,[75]6.7203,[76]6.7261,[77]6.6959,[78]6.6498,[79]6.5994,
save_imatrix: stored collected data after 80 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[80]6.6226,[81]6.6484,[82]6.6333,[83]6.6263,[84]6.6079,[85]6.6463,[86]6.5846,[87]6.5644,[88]6.5804,[89]6.5922,
save_imatrix: stored collected data after 90 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[90]6.5901,[91]6.5320,[92]6.4827,[93]6.4232,[94]6.3606,[95]6.3125,[96]6.2706,[97]6.2153,[98]6.1597,[99]6.1305,
save_imatrix: stored collected data after 100 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[100]6.1450,[101]6.2110,[102]6.2667,[103]6.3205,[104]6.3722,[105]6.4656,[106]6.4593,[107]6.4949,[108]6.4325,[109]6.4323,
save_imatrix: stored collected data after 110 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[110]6.3983,[111]6.3213,[112]6.2552,[113]6.2476,[114]6.2984,[115]6.2985,[116]6.2961,[117]6.3105,[118]6.3396,[119]6.3363,
save_imatrix: stored collected data after 120 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[120]6.3302,[121]6.3419,[122]6.3261,[123]6.3507,[124]6.3745,[125]6.3947,[126]6.4361,[127]6.4525,[128]6.4737,
save_imatrix: stored collected data after 128 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =    2332.08 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 =   80639.92 ms / 65536 tokens (    1.23 ms per token,   812.70 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 =   83049.27 ms / 65537 tokens

Final estimate: PPL = 6.4737 +/- 0.08410