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Llama-Guard-3-8B-IMat-GGUF

Text Generation
GGUF
PyTorch
English
facebook
meta
llama
llama-3
quantized
GGUF
quantization
imat
imatrix
static
16bit
8bit
6bit
5bit
4bit
3bit
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+ llama_model_loader: loaded meta data with 28 key-value pairs and 291 tensors from Llama-Guard-3-8B-IMat-GGUF/Llama-Guard-3-8B.Q8_0.gguf.hardlink.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.type str = model
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+ llama_model_loader: - kv 2: general.name str = Llama Guard 3 8B
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+ llama_model_loader: - kv 3: general.basename str = Llama-Guard-3
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+ llama_model_loader: - kv 4: general.size_label str = 8B
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+ llama_model_loader: - kv 5: general.license str = llama3.1
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+ llama_model_loader: - kv 6: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
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+ llama_model_loader: - kv 7: general.languages arr[str,1] = ["en"]
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+ llama_model_loader: - kv 8: llama.block_count u32 = 32
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+ llama_model_loader: - kv 9: llama.context_length u32 = 131072
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+ llama_model_loader: - kv 10: llama.embedding_length u32 = 4096
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+ llama_model_loader: - kv 11: llama.feed_forward_length u32 = 14336
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+ llama_model_loader: - kv 12: llama.attention.head_count u32 = 32
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+ llama_model_loader: - kv 13: llama.attention.head_count_kv u32 = 8
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+ llama_model_loader: - kv 14: llama.rope.freq_base f32 = 500000.000000
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+ llama_model_loader: - kv 15: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
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+ llama_model_loader: - kv 16: general.file_type u32 = 7
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+ llama_model_loader: - kv 17: llama.vocab_size u32 = 128256
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+ llama_model_loader: - kv 18: llama.rope.dimension_count u32 = 128
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+ llama_model_loader: - kv 19: tokenizer.ggml.model str = gpt2
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+ llama_model_loader: - kv 20: tokenizer.ggml.pre str = smaug-bpe
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+ llama_model_loader: - kv 21: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
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+ llama_model_loader: - kv 22: 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 23: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
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+ llama_model_loader: - kv 24: tokenizer.ggml.bos_token_id u32 = 128000
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+ llama_model_loader: - kv 25: tokenizer.ggml.eos_token_id u32 = 128009
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+ llama_model_loader: - kv 26: tokenizer.chat_template str = {% if messages|length % 2 == 0 %}{% s...
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+ llama_model_loader: - kv 27: general.quantization_version u32 = 2
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+ llama_model_loader: - type f32: 65 tensors
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+ llama_model_loader: - type q8_0: 226 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 = 0.7999 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: vocab_only = 0
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+ llm_load_print_meta: n_ctx_train = 131072
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+ llm_load_print_meta: n_embd = 4096
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+ llm_load_print_meta: n_layer = 32
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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_rot = 128
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+ llm_load_print_meta: n_swa = 0
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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_ctx_orig_yarn = 131072
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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 = Q8_0
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+ llm_load_print_meta: model params = 8.03 B
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+ llm_load_print_meta: model size = 7.95 GiB (8.50 BPW)
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+ llm_load_print_meta: general.name = Llama Guard 3 8B
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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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+ llm_load_print_meta: max token length = 256
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+ ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
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+ ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
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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 = 0.27 MiB
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+ llm_load_tensors: offloading 32 repeating layers to GPU
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+ llm_load_tensors: offloading non-repeating layers to GPU
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+ llm_load_tensors: offloaded 33/33 layers to GPU
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+ llm_load_tensors: CPU buffer size = 532.31 MiB
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+ llm_load_tensors: CUDA0 buffer size = 7605.33 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: CUDA0 KV buffer size = 64.00 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 = 258.50 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 = 2
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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 125.056 ms
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+ compute_imatrix: computing over 125 chunks with batch_size 512
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+ compute_imatrix: 0.66 seconds per pass - ETA 1.37 minutes
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+ [1]5.3432,[2]4.1562,[3]3.7953,[4]4.7161,[5]4.7904,[6]4.0998,[7]4.3090,[8]4.7293,[9]4.9040,
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+ save_imatrix: stored collected data after 10 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [10]4.5278,[11]4.9219,[12]5.3672,[13]5.7781,[14]6.1509,[15]6.3996,[16]6.6480,[17]6.8118,[18]6.5864,[19]6.3010,
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+ save_imatrix: stored collected data after 20 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [20]6.2957,[21]6.4152,[22]6.3562,[23]6.6290,[24]6.6010,[25]6.8860,[26]6.8511,[27]6.4964,[28]6.3000,[29]6.3145,
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+ save_imatrix: stored collected data after 30 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [30]6.3011,[31]6.0076,[32]5.7383,[33]5.6159,[34]5.5359,[35]5.5935,[36]5.6462,[37]5.6128,[38]5.6744,[39]5.8076,
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+ save_imatrix: stored collected data after 40 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [40]5.8831,[41]5.7495,[42]5.6111,[43]5.5717,[44]5.5436,[45]5.6278,[46]5.5684,[47]5.6790,[48]5.7627,[49]5.8620,
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+ save_imatrix: stored collected data after 50 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [50]5.8012,[51]5.8768,[52]5.9775,[53]6.0589,[54]6.1242,[55]6.1870,[56]6.2435,[57]6.3130,[58]6.3447,[59]6.3548,
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+ save_imatrix: stored collected data after 60 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [60]6.3314,[61]6.3278,[62]6.3735,[63]6.4200,[64]6.3695,[65]6.3481,[66]6.3653,[67]6.3480,[68]6.3508,[69]6.3479,
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+ save_imatrix: stored collected data after 70 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [70]6.3542,[71]6.3595,[72]6.3685,[73]6.3483,[74]6.3141,[75]6.3217,[76]6.3402,[77]6.3215,[78]6.3276,[79]6.3622,
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+ save_imatrix: stored collected data after 80 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [80]6.3860,[81]6.3747,[82]6.3813,[83]6.4068,[84]6.3337,[85]6.3359,[86]6.3536,[87]6.3687,[88]6.3972,[89]6.4013,
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+ save_imatrix: stored collected data after 90 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [90]6.3511,[91]6.2970,[92]6.2497,[93]6.2038,[94]6.1549,[95]6.1112,[96]6.0844,[97]6.0923,[98]6.1380,[99]6.2109,
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+ save_imatrix: stored collected data after 100 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [100]6.2789,[101]6.3236,[102]6.4281,[103]6.4615,[104]6.4972,[105]6.4421,[106]6.4488,[107]6.4278,[108]6.3883,[109]6.3375,
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+ save_imatrix: stored collected data after 110 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [110]6.3785,[111]6.4288,[112]6.4397,[113]6.4427,[114]6.4710,[115]6.5063,[116]6.5213,[117]6.5361,[118]6.5641,[119]6.5299,
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+ save_imatrix: stored collected data after 120 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+ [120]6.4958,[121]6.4541,[122]6.4512,[123]6.4408,[124]6.4396,[125]6.4268,
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+ save_imatrix: stored collected data after 125 chunks in Llama-Guard-3-8B-IMat-GGUF/imatrix.dat
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+
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+ llama_print_timings: load time = 2110.40 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 = 69163.99 ms / 64000 tokens ( 1.08 ms per token, 925.34 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 = 71427.12 ms / 64001 tokens
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+
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+ Final estimate: PPL = 6.4268 +/- 0.08467