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gemma-2-2b-it-IMat-GGUF

Text Generation
GGUF
conversational
quantized
GGUF
quantization
imat
imatrix
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
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imatrix.log ADDED
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+ llama_model_loader: loaded meta data with 35 key-value pairs and 288 tensors from gemma-2-2b-it-IMat-GGUF/gemma-2-2b-it.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 = gemma2
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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 = Gemma 2 2b It
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+ llama_model_loader: - kv 3: general.finetune str = it
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+ llama_model_loader: - kv 4: general.basename str = gemma-2
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+ llama_model_loader: - kv 5: general.size_label str = 2B
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+ llama_model_loader: - kv 6: general.license str = gemma
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+ llama_model_loader: - kv 7: general.tags arr[str,2] = ["conversational", "text-generation"]
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+ llama_model_loader: - kv 8: gemma2.context_length u32 = 8192
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+ llama_model_loader: - kv 9: gemma2.embedding_length u32 = 2304
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+ llama_model_loader: - kv 10: gemma2.block_count u32 = 26
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+ llama_model_loader: - kv 11: gemma2.feed_forward_length u32 = 9216
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+ llama_model_loader: - kv 12: gemma2.attention.head_count u32 = 8
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+ llama_model_loader: - kv 13: gemma2.attention.head_count_kv u32 = 4
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+ llama_model_loader: - kv 14: gemma2.attention.layer_norm_rms_epsilon f32 = 0.000001
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+ llama_model_loader: - kv 15: gemma2.attention.key_length u32 = 256
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+ llama_model_loader: - kv 16: gemma2.attention.value_length u32 = 256
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+ llama_model_loader: - kv 17: general.file_type u32 = 7
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+ llama_model_loader: - kv 18: gemma2.attn_logit_softcapping f32 = 50.000000
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+ llama_model_loader: - kv 19: gemma2.final_logit_softcapping f32 = 30.000000
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+ llama_model_loader: - kv 20: gemma2.attention.sliding_window u32 = 4096
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+ llama_model_loader: - kv 21: tokenizer.ggml.model str = llama
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+ llama_model_loader: - kv 22: tokenizer.ggml.pre str = default
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+ llama_model_loader: - kv 23: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
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+ llama_model_loader: - kv 24: tokenizer.ggml.scores arr[f32,256000] = [-1000.000000, -1000.000000, -1000.00...
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+ llama_model_loader: - kv 25: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
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+ llama_model_loader: - kv 26: tokenizer.ggml.bos_token_id u32 = 2
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+ llama_model_loader: - kv 27: tokenizer.ggml.eos_token_id u32 = 1
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+ llama_model_loader: - kv 28: tokenizer.ggml.unknown_token_id u32 = 3
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+ llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 0
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+ llama_model_loader: - kv 30: tokenizer.ggml.add_bos_token bool = true
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+ llama_model_loader: - kv 31: tokenizer.ggml.add_eos_token bool = false
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+ llama_model_loader: - kv 32: tokenizer.chat_template str = {{ bos_token }}{% if messages[0]['rol...
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+ llama_model_loader: - kv 33: tokenizer.ggml.add_space_prefix bool = false
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+ llama_model_loader: - kv 34: general.quantization_version u32 = 2
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+ llama_model_loader: - type f32: 105 tensors
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+ llama_model_loader: - type q8_0: 183 tensors
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+ llm_load_vocab: special tokens cache size = 249
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+ llm_load_vocab: token to piece cache size = 1.6014 MB
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+ llm_load_print_meta: format = GGUF V3 (latest)
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+ llm_load_print_meta: arch = gemma2
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+ llm_load_print_meta: vocab type = SPM
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+ llm_load_print_meta: n_vocab = 256000
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+ llm_load_print_meta: n_merges = 0
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+ llm_load_print_meta: vocab_only = 0
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+ llm_load_print_meta: n_ctx_train = 8192
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+ llm_load_print_meta: n_embd = 2304
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+ llm_load_print_meta: n_layer = 26
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+ llm_load_print_meta: n_head = 8
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+ llm_load_print_meta: n_head_kv = 4
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+ llm_load_print_meta: n_rot = 256
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+ llm_load_print_meta: n_swa = 4096
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+ llm_load_print_meta: n_embd_head_k = 256
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+ llm_load_print_meta: n_embd_head_v = 256
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+ llm_load_print_meta: n_gqa = 2
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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-06
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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 = 9216
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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 = 2
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+ llm_load_print_meta: rope scaling = linear
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+ llm_load_print_meta: freq_base_train = 10000.0
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+ llm_load_print_meta: freq_scale_train = 1
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+ llm_load_print_meta: n_ctx_orig_yarn = 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 = 2B
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+ llm_load_print_meta: model ftype = Q8_0
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+ llm_load_print_meta: model params = 2.61 B
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+ llm_load_print_meta: model size = 2.59 GiB (8.50 BPW)
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+ llm_load_print_meta: general.name = Gemma 2 2b It
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+ llm_load_print_meta: BOS token = 2 '<bos>'
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+ llm_load_print_meta: EOS token = 1 '<eos>'
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+ llm_load_print_meta: UNK token = 3 '<unk>'
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+ llm_load_print_meta: PAD token = 0 '<pad>'
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+ llm_load_print_meta: LF token = 227 '<0x0A>'
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+ llm_load_print_meta: EOT token = 107 '<end_of_turn>'
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+ llm_load_print_meta: max token length = 48
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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 = 0.26 MiB
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+ llm_load_tensors: offloading 26 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 27/27 layers to GPU
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+ llm_load_tensors: CPU buffer size = 597.66 MiB
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+ llm_load_tensors: CUDA0 buffer size = 2649.78 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 = 10000.0
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+ llama_new_context_with_model: freq_scale = 1
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+ llama_kv_cache_init: CUDA0 KV buffer size = 52.00 MiB
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+ llama_new_context_with_model: KV self size = 52.00 MiB, K (f16): 26.00 MiB, V (f16): 26.00 MiB
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+ llama_new_context_with_model: CUDA_Host output buffer size = 0.98 MiB
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+ llama_new_context_with_model: CUDA0 compute buffer size = 504.50 MiB
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+ llama_new_context_with_model: CUDA_Host compute buffer size = 6.51 MiB
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+ llama_new_context_with_model: graph nodes = 1050
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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 121.079 ms
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+ compute_imatrix: computing over 128 chunks with batch_size 512
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+ compute_imatrix: 0.44 seconds per pass - ETA 0.93 minutes
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+ [1]8.7747,[2]5.9624,[3]5.6926,[4]7.1903,[5]7.4523,[6]6.4662,[7]7.2543,[8]7.6677,[9]7.8766,
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+ save_imatrix: stored collected data after 10 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [10]6.9248,[11]7.0146,[12]7.6306,[13]8.2486,[14]8.4140,[15]8.9828,[16]9.3205,[17]9.4306,[18]9.8774,[19]9.4496,
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+ save_imatrix: stored collected data after 20 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [20]9.5170,[21]9.8598,[22]9.7668,[23]9.9612,[24]10.2602,[25]10.4634,[26]10.2184,[27]10.6180,[28]10.9536,[29]10.9679,
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+ save_imatrix: stored collected data after 30 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [30]10.9801,[31]10.3070,[32]9.9868,[33]9.7875,[34]9.5917,[35]9.4165,[36]9.5329,[37]9.6184,[38]9.6592,[39]9.8440,
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+ save_imatrix: stored collected data after 40 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [40]10.0160,[41]10.2362,[42]10.6853,[43]11.1262,[44]11.5383,[45]11.8299,[46]11.6111,[47]11.6185,[48]11.8422,[49]12.0250,
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+ save_imatrix: stored collected data after 50 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [50]11.7259,[51]11.7196,[52]11.7691,[53]11.9141,[54]12.1620,[55]12.3426,[56]12.3606,[57]12.3162,[58]12.3310,[59]12.1155,
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+ save_imatrix: stored collected data after 60 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [60]11.9746,[61]11.7975,[62]11.7242,[63]11.8352,[64]11.8168,[65]11.7588,[66]11.7814,[67]11.7153,[68]11.6185,[69]11.6467,
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+ save_imatrix: stored collected data after 70 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [70]11.6254,[71]11.5979,[72]11.5952,[73]11.5336,[74]11.4763,[75]11.4137,[76]11.4002,[77]11.4239,[78]11.3978,[79]11.3428,
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+ save_imatrix: stored collected data after 80 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [80]11.4072,[81]11.4531,[82]11.4131,[83]11.4049,[84]11.4603,[85]11.2951,[86]11.2648,[87]11.1973,[88]11.1809,[89]11.1978,
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+ save_imatrix: stored collected data after 90 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
140
+ [90]11.2027,[91]11.0999,[92]10.9946,[93]10.8726,[94]10.7512,[95]10.6614,[96]10.5603,[97]10.4626,[98]10.3759,[99]10.3884,
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+ save_imatrix: stored collected data after 100 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [100]10.3982,[101]10.5283,[102]10.6240,[103]10.7161,[104]10.9227,[105]11.0803,[106]11.1020,[107]11.1251,[108]11.1344,[109]11.1136,
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+ save_imatrix: stored collected data after 110 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
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+ [110]11.0937,[111]11.0223,[112]10.9389,[113]10.9760,[114]10.9900,[115]10.9929,[116]10.9726,[117]11.0118,[118]11.0338,[119]11.0361,
145
+ save_imatrix: stored collected data after 120 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
146
+ [120]11.0343,[121]11.0230,[122]10.9614,[123]11.0533,[124]11.1411,[125]11.2224,[126]11.3357,[127]11.4332,[128]11.5265,
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+ save_imatrix: stored collected data after 128 chunks in gemma-2-2b-it-IMat-GGUF/imatrix.dat
148
+
149
+ llama_print_timings: load time = 1113.21 ms
150
+ 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 = 34310.87 ms / 65536 tokens ( 0.52 ms per token, 1910.07 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 = 36416.67 ms / 65537 tokens
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+
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+ Final estimate: PPL = 11.5265 +/- 0.19215