File size: 11,333 Bytes
1223e84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
llama_model_loader: loaded meta data with 34 key-value pairs and 508 tensors from shieldgemma-27b-IMat-GGUF/shieldgemma-27b.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 27b
llama_model_loader: - kv 3: general.basename str = shieldgemma
llama_model_loader: - kv 4: general.size_label str = 27B
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 = 4608
llama_model_loader: - kv 9: gemma2.block_count u32 = 46
llama_model_loader: - kv 10: gemma2.feed_forward_length u32 = 36864
llama_model_loader: - kv 11: gemma2.attention.head_count u32 = 32
llama_model_loader: - kv 12: gemma2.attention.head_count_kv u32 = 16
llama_model_loader: - kv 13: gemma2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 14: gemma2.attention.key_length u32 = 128
llama_model_loader: - kv 15: gemma2.attention.value_length u32 = 128
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: 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: 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 = Shieldgemma 27b
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 / 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 127.59 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 2.04 seconds per pass - ETA 4.35 minutes
[1]6.5529,[2]4.3753,[3]3.8200,[4]4.7426,[5]4.8615,[6]4.2138,[7]4.5153,[8]4.7561,[9]5.0004,
save_imatrix: stored collected data after 10 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[10]4.4829,[11]4.6406,[12]5.0627,[13]5.5336,[14]5.7434,[15]6.1916,[16]6.4807,[17]6.6260,[18]6.9112,[19]6.6039,
save_imatrix: stored collected data after 20 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[20]6.7241,[21]6.8570,[22]6.8188,[23]6.9620,[24]7.0188,[25]7.1975,[26]6.9847,[27]7.1626,[28]7.2851,[29]7.2054,
save_imatrix: stored collected data after 30 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[30]7.2006,[31]6.7962,[32]6.5772,[33]6.4917,[34]6.3837,[35]6.3487,[36]6.3662,[37]6.3802,[38]6.4268,[39]6.5645,
save_imatrix: stored collected data after 40 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[40]6.6992,[41]6.8132,[42]7.0489,[43]7.2844,[44]7.5078,[45]7.6526,[46]7.5486,[47]7.5756,[48]7.7402,[49]7.8650,
save_imatrix: stored collected data after 50 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[50]7.7079,[51]7.7449,[52]7.7805,[53]7.8870,[54]8.0256,[55]8.1275,[56]8.1925,[57]8.2110,[58]8.2431,[59]8.1083,
save_imatrix: stored collected data after 60 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[60]8.0200,[61]7.9049,[62]7.8772,[63]7.9039,[64]7.9035,[65]7.8704,[66]7.8759,[67]7.8261,[68]7.7754,[69]7.7932,
save_imatrix: stored collected data after 70 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[70]7.7601,[71]7.7481,[72]7.7565,[73]7.7407,[74]7.6986,[75]7.6610,[76]7.6540,[77]7.6525,[78]7.6471,[79]7.6015,
save_imatrix: stored collected data after 80 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[80]7.6499,[81]7.6832,[82]7.6675,[83]7.6768,[84]7.7121,[85]7.6041,[86]7.5667,[87]7.5087,[88]7.5209,[89]7.5609,
save_imatrix: stored collected data after 90 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[90]7.5806,[91]7.5265,[92]7.4684,[93]7.3988,[94]7.3356,[95]7.2910,[96]7.2315,[97]7.1779,[98]7.1320,[99]7.1597,
save_imatrix: stored collected data after 100 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[100]7.1820,[101]7.2535,[102]7.3217,[103]7.3924,[104]7.5169,[105]7.6095,[106]7.6266,[107]7.6487,[108]7.6654,[109]7.6394,
save_imatrix: stored collected data after 110 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[110]7.6112,[111]7.5207,[112]7.4226,[113]7.4681,[114]7.4921,[115]7.4919,[116]7.4894,[117]7.5252,[118]7.5394,[119]7.5509,
save_imatrix: stored collected data after 120 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
[120]7.5613,[121]7.5822,[122]7.5593,[123]7.6007,[124]7.6477,[125]7.6850,[126]7.7480,[127]7.7968,[128]7.8351,
save_imatrix: stored collected data after 128 chunks in shieldgemma-27b-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 4902.13 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 = 227177.99 ms / 65536 tokens ( 3.47 ms per token, 288.48 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 = 231584.30 ms / 65537 tokens
Final estimate: PPL = 7.8351 +/- 0.11938
|