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llama_model_loader: loaded meta data with 34 key-value pairs and 291 tensors from xLAM-7b-r-IMat-GGUF/xLAM-7b-r.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              = xLAM 7b R
llama_model_loader: - kv   3:                           general.finetune str              = r
llama_model_loader: - kv   4:                           general.basename str              = xLAM
llama_model_loader: - kv   5:                         general.size_label str              = 7B
llama_model_loader: - kv   6:                            general.license str              = cc-by-nc-4.0
llama_model_loader: - kv   7:                               general.tags arr[str,6]       = ["function-calling", "LLM Agent", "to...
llama_model_loader: - kv   8:                          general.languages arr[str,1]       = ["en"]
llama_model_loader: - kv   9:                           general.datasets arr[str,1]       = ["Salesforce/xlam-function-calling-60k"]
llama_model_loader: - kv  10:                          llama.block_count u32              = 32
llama_model_loader: - kv  11:                       llama.context_length u32              = 32768
llama_model_loader: - kv  12:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv  13:                  llama.feed_forward_length u32              = 14336
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:                          general.file_type u32              = 7
llama_model_loader: - kv  19:                           llama.vocab_size u32              = 32000
llama_model_loader: - kv  20:                 llama.rope.dimension_count u32              = 128
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,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  25:                      tokenizer.ggml.scores arr[f32,32000]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,32000]   = [3, 3, 3, 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              = 2
llama_model_loader: - kv  29:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  30:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  31:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv  33:               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 = 3
llm_load_vocab: token to piece cache size = 0.1637 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          = 32000
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 32768
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_layer          = 32
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             = 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        = 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  = 32768
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     = 7.24 B
llm_load_print_meta: model size       = 7.17 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = xLAM 7b R
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         = 13 '<0x0A>'
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 =   132.81 MiB
llm_load_tensors:      CUDA0 buffer size =  7205.83 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 =    64.00 MiB
llama_new_context_with_model: KV self size  =   64.00 MiB, K (f16):   32.00 MiB, V (f16):   32.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.12 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =    81.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     9.01 MiB
llama_new_context_with_model: graph nodes  = 1030
llama_new_context_with_model: graph splits = 2

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 | 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 99.008 ms
compute_imatrix: computing over 148 chunks with batch_size 512
compute_imatrix: 0.67 seconds per pass - ETA 1.65 minutes
[1]4.0886,[2]2.9452,[3]2.9743,[4]3.1523,[5]3.5458,[6]3.4611,[7]3.1804,[8]3.6540,[9]3.8055,
save_imatrix: stored collected data after 10 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[10]4.2065,[11]4.3688,[12]4.1191,[13]4.3348,[14]4.5837,[15]4.9394,[16]5.0901,[17]5.3093,[18]5.4405,[19]5.5103,
save_imatrix: stored collected data after 20 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[20]5.6458,[21]5.5826,[22]5.3903,[23]5.5240,[24]5.5161,[25]5.5700,[26]5.4276,[27]5.6431,[28]5.5657,[29]5.6590,
save_imatrix: stored collected data after 30 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[30]5.5322,[31]5.6113,[32]5.7234,[33]5.8611,[34]5.8932,[35]5.8244,[36]5.6129,[37]5.4541,[38]5.3355,[39]5.2405,
save_imatrix: stored collected data after 40 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[40]5.1727,[41]5.1787,[42]5.1454,[43]5.1428,[44]5.0991,[45]5.0783,[46]5.0954,[47]5.1514,[48]5.2317,[49]5.2562,
save_imatrix: stored collected data after 50 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[50]5.3921,[51]5.4991,[52]5.6570,[53]5.7809,[54]5.8987,[55]5.8522,[56]5.8086,[57]5.8774,[58]5.9485,[59]5.9688,
save_imatrix: stored collected data after 60 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[60]5.9026,[61]5.8872,[62]5.9106,[63]5.9532,[64]6.0496,[65]6.1234,[66]6.1502,[67]6.1669,[68]6.1966,[69]6.2012,
save_imatrix: stored collected data after 70 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[70]6.1947,[71]6.1265,[72]6.0751,[73]6.0409,[74]6.0502,[75]6.0795,[76]6.0731,[77]6.0922,[78]6.1034,[79]6.0827,
save_imatrix: stored collected data after 80 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[80]6.0714,[81]6.0460,[82]6.0652,[83]6.0723,[84]6.0657,[85]6.0826,[86]6.0641,[87]6.0521,[88]6.0342,[89]6.0420,
save_imatrix: stored collected data after 90 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[90]6.0213,[91]5.9912,[92]5.9652,[93]5.9519,[94]5.9674,[95]5.9917,[96]5.9786,[97]5.9734,[98]5.9635,[99]6.0018,
save_imatrix: stored collected data after 100 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[100]5.9407,[101]5.9387,[102]5.9168,[103]5.9311,[104]5.9391,[105]5.9253,[106]5.8936,[107]5.8519,[108]5.8104,[109]5.7726,
save_imatrix: stored collected data after 110 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[110]5.7306,[111]5.6930,[112]5.6564,[113]5.6222,[114]5.5864,[115]5.5595,[116]5.5623,[117]5.5800,[118]5.6310,[119]5.6801,
save_imatrix: stored collected data after 120 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[120]5.7265,[121]5.7994,[122]5.8640,[123]5.8657,[124]5.8758,[125]5.8380,[126]5.8276,[127]5.8218,[128]5.8240,[129]5.7958,
save_imatrix: stored collected data after 130 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[130]5.7623,[131]5.7831,[132]5.8170,[133]5.8136,[134]5.8086,[135]5.8195,[136]5.8427,[137]5.8450,[138]5.8518,[139]5.8687,
save_imatrix: stored collected data after 140 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat
[140]5.8721,[141]5.8651,[142]5.9060,[143]5.9412,[144]5.9657,[145]6.0045,[146]6.0364,[147]6.0794,[148]6.1121,
save_imatrix: stored collected data after 148 chunks in xLAM-7b-r-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =    2301.03 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 =   86818.00 ms / 75776 tokens (    1.15 ms per token,   872.81 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 =   88858.24 ms / 75777 tokens

Final estimate: PPL = 6.1121 +/- 0.07381