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llama_model_loader: loaded meta data with 30 key-value pairs and 291 tensors from internlm2_5-7b-chat-IMat-GGUF/internlm2_5-7b-chat.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 = internlm2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Internlm2_5 7b Chat
llama_model_loader: - kv 3: general.finetune str = chat
llama_model_loader: - kv 4: general.basename str = internlm2_5
llama_model_loader: - kv 5: general.size_label str = 7B
llama_model_loader: - kv 6: general.license str = other
llama_model_loader: - kv 7: general.tags arr[str,1] = ["text-generation"]
llama_model_loader: - kv 8: internlm2.context_length u32 = 32768
llama_model_loader: - kv 9: internlm2.block_count u32 = 32
llama_model_loader: - kv 10: internlm2.embedding_length u32 = 4096
llama_model_loader: - kv 11: internlm2.feed_forward_length u32 = 14336
llama_model_loader: - kv 12: internlm2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: internlm2.attention.head_count u32 = 32
llama_model_loader: - kv 14: internlm2.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 15: internlm2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 16: general.file_type u32 = 7
llama_model_loader: - kv 17: tokenizer.ggml.model str = llama
llama_model_loader: - kv 18: tokenizer.ggml.pre str = default
llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,92544] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 20: tokenizer.ggml.scores arr[f32,92544] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 21: tokenizer.ggml.token_type arr[i32,92544] = [3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 22: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 23: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 24: tokenizer.ggml.eos_token_id u32 = 92542
llama_model_loader: - kv 25: tokenizer.ggml.padding_token_id u32 = 2
llama_model_loader: - kv 26: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 27: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 28: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 29: 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 = 9
llm_load_vocab: token to piece cache size = 0.5508 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = internlm2
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 92544
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: model type = 7B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 7.74 B
llm_load_print_meta: model size = 7.66 GiB (8.50 BPW)
llm_load_print_meta: general.name = Internlm2_5 7b Chat
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 92542 '<|im_end|>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: PAD token = 2 '</s>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_print_meta: EOT token = 92542 '<|im_end|>'
llm_load_print_meta: max token length = 384
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 = 384.09 MiB
llm_load_tensors: CUDA0 buffer size = 7457.11 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 = 0.35 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 188.75 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 / 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 101.295 ms
compute_imatrix: computing over 136 chunks with batch_size 512
compute_imatrix: 0.59 seconds per pass - ETA 1.32 minutes
[1]5.3769,[2]3.9691,[3]3.7820,[4]4.3833,[5]4.2913,[6]3.9200,[7]4.5687,[8]4.6228,[9]5.0904,
save_imatrix: stored collected data after 10 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[10]5.2358,[11]4.8009,[12]5.1499,[13]5.7993,[14]6.1283,[15]6.6242,[16]6.8570,[17]6.5016,[18]6.7125,[19]7.0479,
save_imatrix: stored collected data after 20 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[20]6.8082,[21]6.8744,[22]7.0183,[23]7.0556,[24]7.0649,[25]7.2574,[26]7.4557,[27]7.6284,[28]7.6740,[29]7.8141,
save_imatrix: stored collected data after 30 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[30]8.0461,[31]8.1025,[32]7.8055,[33]7.4904,[34]7.1984,[35]6.9328,[36]6.7832,[37]6.6670,[38]6.5876,[39]6.5080,
save_imatrix: stored collected data after 40 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[40]6.4166,[41]6.3603,[42]6.2501,[43]6.2315,[44]6.2919,[45]6.3633,[46]6.4997,[47]6.4768,[48]6.7117,[49]6.8956,
save_imatrix: stored collected data after 50 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[50]7.0720,[51]7.2003,[52]7.3871,[53]7.2634,[54]7.3487,[55]7.4317,[56]7.5508,[57]7.4196,[58]7.4276,[59]7.4583,
save_imatrix: stored collected data after 60 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[60]7.5612,[61]7.6982,[62]7.8157,[63]7.8791,[64]7.8829,[65]7.8916,[66]7.8631,[67]7.8273,[68]7.7453,[69]7.7176,
save_imatrix: stored collected data after 70 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[70]7.7884,[71]7.8087,[72]7.7333,[73]7.6979,[74]7.7003,[75]7.6571,[76]7.6477,[77]7.6271,[78]7.6449,[79]7.5887,
save_imatrix: stored collected data after 80 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[80]7.5901,[81]7.5365,[82]7.5011,[83]7.4550,[84]7.4343,[85]7.3733,[86]7.3403,[87]7.3036,[88]7.3420,[89]7.3542,
save_imatrix: stored collected data after 90 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[90]7.3164,[91]7.3376,[92]7.3530,[93]7.3026,[94]7.2918,[95]7.2765,[96]7.3050,[97]7.3023,[98]7.3053,[99]7.2564,
save_imatrix: stored collected data after 100 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[100]7.2231,[101]7.1735,[102]7.1225,[103]7.0841,[104]7.0417,[105]7.0015,[106]6.9774,[107]6.9807,[108]7.0191,[109]7.0920,
save_imatrix: stored collected data after 110 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[110]7.1641,[111]7.2266,[112]7.3301,[113]7.3983,[114]7.4227,[115]7.4135,[116]7.4296,[117]7.4244,[118]7.4193,[119]7.3785,
save_imatrix: stored collected data after 120 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[120]7.3659,[121]7.3952,[122]7.3990,[123]7.4023,[124]7.4202,[125]7.4587,[126]7.4862,[127]7.4988,[128]7.5187,[129]7.5457,
save_imatrix: stored collected data after 130 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
[130]7.5014,[131]7.5428,[132]7.6069,[133]7.6466,[134]7.7078,[135]7.7539,[136]7.8028,
save_imatrix: stored collected data after 136 chunks in internlm2_5-7b-chat-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 2096.79 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 = 70631.81 ms / 69632 tokens ( 1.01 ms per token, 985.84 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 = 72872.89 ms / 69633 tokens
Final estimate: PPL = 7.8028 +/- 0.10727