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llama_model_loader: loaded meta data with 35 key-value pairs and 291 tensors from unsloth-Phi-3.5-mini-instruct-IMat-GGUF/unsloth-Phi-3.5-mini-instruct.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              = Unsloth Phi 3.5 Mini Instruct
llama_model_loader: - kv   3:                           general.finetune str              = instruct
llama_model_loader: - kv   4:                           general.basename str              = unsloth-Phi-3.5
llama_model_loader: - kv   5:                         general.size_label str              = mini
llama_model_loader: - kv   6:                            general.license str              = mit
llama_model_loader: - kv   7:                       general.license.link str              = https://huggingface.co/microsoft/Phi-...
llama_model_loader: - kv   8:                               general.tags arr[str,4]       = ["unsloth", "transformers", "phi3", "...
llama_model_loader: - kv   9:                          general.languages arr[str,1]       = ["multilingual"]
llama_model_loader: - kv  10:                          llama.block_count u32              = 32
llama_model_loader: - kv  11:                       llama.context_length u32              = 131072
llama_model_loader: - kv  12:                     llama.embedding_length u32              = 3072
llama_model_loader: - kv  13:                  llama.feed_forward_length u32              = 8192
llama_model_loader: - kv  14:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  15:              llama.attention.head_count_kv u32              = 32
llama_model_loader: - kv  16:                       llama.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  17:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  18:                          general.file_type u32              = 7
llama_model_loader: - kv  19:                           llama.vocab_size u32              = 32064
llama_model_loader: - kv  20:                 llama.rope.dimension_count u32              = 96
llama_model_loader: - kv  21:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  22:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  23:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  24:                      tokenizer.ggml.tokens arr[str,32064]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  25:                      tokenizer.ggml.scores arr[f32,32064]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,32064]   = [3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  27:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 32000
llama_model_loader: - kv  29:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 32009
llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  32:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  33:                    tokenizer.chat_template str              = {% for message in messages %}{% if me...
llama_model_loader: - kv  34:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type q8_0:  226 tensors
llm_load_vocab: special tokens cache size = 14
llm_load_vocab: token to piece cache size = 0.1685 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 32064
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 131072
llm_load_print_meta: n_embd           = 3072
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 32
llm_load_print_meta: n_rot            = 96
llm_load_print_meta: n_swa            = 0
llm_load_print_meta: n_embd_head_k    = 96
llm_load_print_meta: n_embd_head_v    = 96
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 3072
llm_load_print_meta: n_embd_v_gqa     = 3072
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             = 8192
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 3.82 B
llm_load_print_meta: model size       = 3.78 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = Unsloth Phi 3.5 Mini Instruct
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 32000 '<|endoftext|>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: PAD token        = 32009 '<|placeholder6|>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_print_meta: EOT token        = 32007 '<|end|>'
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 =    0.27 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:        CPU buffer size =    99.81 MiB
llm_load_tensors:      CUDA0 buffer size =  3772.57 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 =   192.00 MiB
llama_new_context_with_model: KV self size  =  192.00 MiB, K (f16):   96.00 MiB, V (f16):   96.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.12 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =    68.62 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     7.01 MiB
llama_new_context_with_model: graph nodes  = 1030
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 143.583 ms
compute_imatrix: computing over 151 chunks with batch_size 512
compute_imatrix: 0.42 seconds per pass - ETA 1.05 minutes
[1]5.6455,[2]4.2496,[3]4.2246,[4]4.7837,[5]5.2227,[6]5.3658,[7]4.8447,[8]5.3686,[9]5.5685,
save_imatrix: stored collected data after 10 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[10]5.9472,[11]5.9353,[12]5.4576,[13]5.3625,[14]5.6370,[15]6.0624,[16]6.1669,[17]6.4863,[18]6.6621,[19]6.8056,
save_imatrix: stored collected data after 20 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[20]6.9293,[21]7.1482,[22]6.8526,[23]6.5285,[24]6.5929,[25]6.6662,[26]6.5827,[27]6.4753,[28]6.5536,[29]6.7352,
save_imatrix: stored collected data after 30 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[30]6.8579,[31]6.8264,[32]6.9704,[33]7.0894,[34]7.2737,[35]7.2872,[36]7.2740,[37]6.9656,[38]6.7646,[39]6.6571,
save_imatrix: stored collected data after 40 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[40]6.5504,[41]6.4638,[42]6.3819,[43]6.2510,[44]6.1741,[45]6.0896,[46]6.0434,[47]6.0518,[48]6.1210,[49]6.2249,
save_imatrix: stored collected data after 50 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[50]6.2443,[51]6.4326,[52]6.6097,[53]6.8114,[54]7.0049,[55]7.1141,[56]7.0518,[57]6.9795,[58]6.9947,[59]7.0529,
save_imatrix: stored collected data after 60 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[60]7.1530,[61]7.0435,[62]7.0612,[63]7.1083,[64]7.1889,[65]7.2617,[66]7.2995,[67]7.3482,[68]7.4029,[69]7.3956,
save_imatrix: stored collected data after 70 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[70]7.4210,[71]7.4304,[72]7.4420,[73]7.3864,[74]7.3039,[75]7.2846,[76]7.3311,[77]7.3356,[78]7.2983,[79]7.2867,
save_imatrix: stored collected data after 80 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[80]7.2890,[81]7.2540,[82]7.2330,[83]7.2053,[84]7.2096,[85]7.2169,[86]7.2051,[87]7.2006,[88]7.1903,[89]7.1772,
save_imatrix: stored collected data after 90 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[90]7.1623,[91]7.1720,[92]7.1281,[93]7.1245,[94]7.0971,[95]7.0535,[96]7.0674,[97]7.0479,[98]7.0504,[99]7.0249,
save_imatrix: stored collected data after 100 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[100]7.0132,[101]7.0250,[102]6.9877,[103]6.9490,[104]6.9416,[105]6.9636,[106]6.9712,[107]6.9971,[108]7.0300,[109]6.9885,
save_imatrix: stored collected data after 110 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[110]6.9476,[111]6.9077,[112]6.8681,[113]6.8215,[114]6.7730,[115]6.7377,[116]6.6973,[117]6.6657,[118]6.6775,[119]6.6842,
save_imatrix: stored collected data after 120 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[120]6.7383,[121]6.7896,[122]6.8503,[123]6.9010,[124]6.9832,[125]7.0567,[126]7.0667,[127]7.0719,[128]7.0068,[129]7.0001,
save_imatrix: stored collected data after 130 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[130]6.9700,[131]6.9500,[132]6.9093,[133]6.8680,[134]6.8811,[135]6.9009,[136]6.8981,[137]6.8963,[138]6.9030,[139]6.9162,
save_imatrix: stored collected data after 140 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[140]6.9331,[141]6.9349,[142]6.9355,[143]6.9334,[144]6.9116,[145]6.9291,[146]6.9632,[147]7.0090,[148]7.0513,[149]7.0970,
save_imatrix: stored collected data after 150 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[150]7.1404,[151]7.1870,
save_imatrix: stored collected data after 151 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =    1420.67 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 =   55358.52 ms / 77312 tokens (    0.72 ms per token,  1396.57 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 =   56810.47 ms / 77313 tokens

Final estimate: PPL = 7.1870 +/- 0.09220