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Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF

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GGUF
quantized
GGUF
imatrix
quantization
imat
static
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+ main: build = 3058 (30e238b2)
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+ main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
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+ main: seed = 1717180036
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+ llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/Meta-Llama-3-8B-Instruct-abliterated-v3.gguf (version GGUF V3 (latest))
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+ llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
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+ llama_model_loader: - kv 0: general.architecture str = llama
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+ llama_model_loader: - kv 1: general.name str = Meta-Llama-3-8B-Instruct-abliterated-v3
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+ llama_model_loader: - kv 2: llama.block_count u32 = 32
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+ llama_model_loader: - kv 3: llama.context_length u32 = 8192
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+ llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
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+ llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
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+ llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
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+ llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
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+ llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000
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+ llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
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+ llama_model_loader: - kv 10: general.file_type u32 = 0
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+ llama_model_loader: - kv 11: llama.vocab_size u32 = 128256
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+ llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
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+ llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
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+ llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe
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+ llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
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+ llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
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+ llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
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+ llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
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+ llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009
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+ llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
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+ llama_model_loader: - kv 21: general.quantization_version u32 = 2
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+ llama_model_loader: - type f32: 291 tensors
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+ llm_load_vocab: special tokens cache size = 256
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+ llm_load_vocab: token to piece cache size = 1.5928 MB
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+ llm_load_print_meta: format = GGUF V3 (latest)
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+ llm_load_print_meta: arch = llama
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+ llm_load_print_meta: vocab type = BPE
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+ llm_load_print_meta: n_vocab = 128256
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+ llm_load_print_meta: n_merges = 280147
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+ llm_load_print_meta: n_ctx_train = 8192
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+ llm_load_print_meta: n_embd = 4096
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+ llm_load_print_meta: n_head = 32
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+ llm_load_print_meta: n_head_kv = 8
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+ llm_load_print_meta: n_layer = 32
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+ llm_load_print_meta: n_rot = 128
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+ llm_load_print_meta: n_embd_head_k = 128
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+ llm_load_print_meta: n_embd_head_v = 128
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+ llm_load_print_meta: n_gqa = 4
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+ llm_load_print_meta: n_embd_k_gqa = 1024
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+ llm_load_print_meta: n_embd_v_gqa = 1024
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+ llm_load_print_meta: f_norm_eps = 0.0e+00
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+ llm_load_print_meta: f_norm_rms_eps = 1.0e-05
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+ llm_load_print_meta: f_clamp_kqv = 0.0e+00
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+ llm_load_print_meta: f_max_alibi_bias = 0.0e+00
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+ llm_load_print_meta: f_logit_scale = 0.0e+00
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+ llm_load_print_meta: n_ff = 14336
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+ llm_load_print_meta: n_expert = 0
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+ llm_load_print_meta: n_expert_used = 0
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+ llm_load_print_meta: causal attn = 1
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+ llm_load_print_meta: pooling type = 0
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+ llm_load_print_meta: rope type = 0
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+ llm_load_print_meta: rope scaling = linear
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+ llm_load_print_meta: freq_base_train = 500000.0
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+ llm_load_print_meta: freq_scale_train = 1
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+ llm_load_print_meta: n_yarn_orig_ctx = 8192
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+ llm_load_print_meta: rope_finetuned = unknown
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+ llm_load_print_meta: ssm_d_conv = 0
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+ llm_load_print_meta: ssm_d_inner = 0
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+ llm_load_print_meta: ssm_d_state = 0
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+ llm_load_print_meta: ssm_dt_rank = 0
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+ llm_load_print_meta: model type = 8B
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+ llm_load_print_meta: model ftype = all F32
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+ llm_load_print_meta: model params = 8.03 B
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+ llm_load_print_meta: model size = 29.92 GiB (32.00 BPW)
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+ llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct-abliterated-v3
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+ llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
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+ llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
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+ llm_load_print_meta: LF token = 128 'Ä'
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+ llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
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+ ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
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+ ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
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+ ggml_cuda_init: found 1 CUDA devices:
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+ Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
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+ llm_load_tensors: ggml ctx size = 0.30 MiB
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+ llm_load_tensors: offloading 25 repeating layers to GPU
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+ llm_load_tensors: offloaded 25/33 layers to GPU
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+ llm_load_tensors: CPU buffer size = 30633.02 MiB
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+ llm_load_tensors: CUDA0 buffer size = 20800.78 MiB
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+ .........................................................................................
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+ llama_new_context_with_model: n_ctx = 512
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+ llama_new_context_with_model: n_batch = 512
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+ llama_new_context_with_model: n_ubatch = 512
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+ llama_new_context_with_model: flash_attn = 0
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+ llama_new_context_with_model: freq_base = 500000.0
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+ llama_new_context_with_model: freq_scale = 1
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+ llama_kv_cache_init: CUDA_Host KV buffer size = 14.00 MiB
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+ llama_kv_cache_init: CUDA0 KV buffer size = 50.00 MiB
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+ llama_new_context_with_model: KV self size = 64.00 MiB, K (f16): 32.00 MiB, V (f16): 32.00 MiB
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+ llama_new_context_with_model: CUDA_Host output buffer size = 0.49 MiB
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+ llama_new_context_with_model: CUDA0 compute buffer size = 2262.50 MiB
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+ llama_new_context_with_model: CUDA_Host compute buffer size = 9.01 MiB
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+ llama_new_context_with_model: graph nodes = 1030
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+ llama_new_context_with_model: graph splits = 81
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+
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+ 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 |
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+ compute_imatrix: tokenizing the input ..
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+ compute_imatrix: tokenization took 43.286 ms
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+ compute_imatrix: computing over 125 chunks with batch_size 512
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+ compute_imatrix: 0.95 seconds per pass - ETA 1.97 minutes
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+ [1]6.9550,[2]5.3356,[3]4.7552,[4]5.9521,[5]6.1848,[6]5.1251,[7]5.4806,[8]6.0734,[9]6.3479,
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+ save_imatrix: stored collected data after 10 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [10]5.7481,[11]6.2474,[12]6.8020,[13]7.2853,[14]7.7500,[15]8.0496,[16]8.3208,[17]8.4896,[18]8.1779,[19]7.7503,
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+ save_imatrix: stored collected data after 20 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [20]7.7376,[21]7.9144,[22]7.8401,[23]8.1850,[24]8.1189,[25]8.4858,[26]8.5075,[27]8.6020,[28]8.8274,[29]8.8420,
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+ save_imatrix: stored collected data after 30 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [30]8.8338,[31]8.3433,[32]7.8930,[33]7.6610,[34]7.4806,[35]7.5676,[36]7.6583,[37]7.5818,[38]7.6711,[39]7.8510,
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+ save_imatrix: stored collected data after 40 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [40]7.9494,[41]8.0367,[42]8.1563,[43]8.3924,[44]8.4976,[45]8.6365,[46]8.5058,[47]8.6491,[48]8.7266,[49]8.8290,
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+ save_imatrix: stored collected data after 50 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [50]8.7036,[51]8.8046,[52]8.9486,[53]9.0414,[54]9.1195,[55]9.2159,[56]9.2503,[57]9.3136,[58]9.3203,[59]9.3317,
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+ save_imatrix: stored collected data after 60 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [60]9.2757,[61]9.2573,[62]9.2973,[63]9.3396,[64]9.2324,[65]9.1891,[66]9.2019,[67]9.1651,[68]9.1452,[69]9.1161,
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+ save_imatrix: stored collected data after 70 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [70]9.1018,[71]9.0830,[72]9.0810,[73]9.0395,[74]8.9719,[75]8.9643,[76]8.9738,[77]8.9422,[78]8.9248,[79]8.9677,
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+ save_imatrix: stored collected data after 80 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [80]9.0023,[81]8.9934,[82]8.9942,[83]9.0198,[84]8.8886,[85]8.8820,[86]8.8776,[87]8.8927,[88]8.9199,[89]8.9228,
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+ save_imatrix: stored collected data after 90 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [90]8.8580,[91]8.7803,[92]8.7008,[93]8.6376,[94]8.5770,[95]8.5155,[96]8.4698,[97]8.4813,[98]8.5218,[99]8.6150,
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+ save_imatrix: stored collected data after 100 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [100]8.7030,[101]8.7701,[102]8.9084,[103]8.9515,[104]8.9945,[105]8.8945,[106]8.8884,[107]8.8311,[108]8.7640,[109]8.6835,
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+ save_imatrix: stored collected data after 110 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [110]8.7344,[111]8.8007,[112]8.8050,[113]8.8203,[114]8.8743,[115]8.9218,[116]8.9400,[117]8.9711,[118]8.9996,[119]8.9310,
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+ save_imatrix: stored collected data after 120 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+ [120]8.9453,[121]8.9654,[122]8.9965,[123]9.0460,[124]9.0738,[125]9.1103,
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+ save_imatrix: stored collected data after 125 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
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+
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+ llama_print_timings: load time = 2807.25 ms
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+ llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
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+ llama_print_timings: prompt eval time = 109298.58 ms / 64000 tokens ( 1.71 ms per token, 585.55 tokens per second)
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+ llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
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+ llama_print_timings: total time = 111984.69 ms / 64001 tokens
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+
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+ Final estimate: PPL = 9.1103 +/- 0.14568