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imatrix.log

@@ -1,4 +1,4 @@
-llama_model_loader: loaded meta data with 28 key-value pairs and 464 tensors from Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf.hardlink.gguf (version GGUF V3 (latest))
+llama_model_loader: loaded meta data with 29 key-value pairs and 464 tensors from Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/Gemma-2-9B-It-SPPO-Iter3.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.name str              = Gemma-2-9B-It-SPPO-Iter3
@@ -14,35 +14,38 @@ llama_model_loader: - kv  10:              gemma2.attention.value_length u32
 llama_model_loader: - kv  11:                          general.file_type u32              = 7
 llama_model_loader: - kv  12:              gemma2.attn_logit_softcapping f32              = 50.000000
 llama_model_loader: - kv  13:             gemma2.final_logit_softcapping f32              = 30.000000
-llama_model_loader: - kv  14:                       tokenizer.ggml.model str              = llama
-llama_model_loader: - kv  15:                         tokenizer.ggml.pre str              = default
-llama_model_loader: - kv  16:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
-llama_model_loader: - kv  17:                      tokenizer.ggml.scores arr[f32,256000]  = [-1000.000000, -1000.000000, -1000.00...
-llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, ...
-llama_model_loader: - kv  19:                tokenizer.ggml.bos_token_id u32              = 2
-llama_model_loader: - kv  20:                tokenizer.ggml.eos_token_id u32              = 1
-llama_model_loader: - kv  21:            tokenizer.ggml.unknown_token_id u32              = 3
-llama_model_loader: - kv  22:            tokenizer.ggml.padding_token_id u32              = 0
-llama_model_loader: - kv  23:               tokenizer.ggml.add_bos_token bool             = true
-llama_model_loader: - kv  24:               tokenizer.ggml.add_eos_token bool             = false
-llama_model_loader: - kv  25:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...
-llama_model_loader: - kv  26:            tokenizer.ggml.add_space_prefix bool             = false
-llama_model_loader: - kv  27:               general.quantization_version u32              = 2
+llama_model_loader: - kv  14:            gemma2.attention.sliding_window u32              = 4096
+llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  16:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  17:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
+llama_model_loader: - kv  18:                      tokenizer.ggml.scores arr[f32,256000]  = [0.000000, 0.000000, 0.000000, 0.0000...
+llama_model_loader: - kv  19:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
+llama_model_loader: - kv  20:                tokenizer.ggml.bos_token_id u32              = 2
+llama_model_loader: - kv  21:                tokenizer.ggml.eos_token_id u32              = 1
+llama_model_loader: - kv  22:            tokenizer.ggml.unknown_token_id u32              = 3
+llama_model_loader: - kv  23:            tokenizer.ggml.padding_token_id u32              = 0
+llama_model_loader: - kv  24:               tokenizer.ggml.add_bos_token bool             = true
+llama_model_loader: - kv  25:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  26:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...
+llama_model_loader: - kv  27:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  28:               general.quantization_version u32              = 2
 llama_model_loader: - type  f32:  169 tensors
 llama_model_loader: - type q8_0:  295 tensors
-llm_load_vocab: special tokens cache size = 261
+llm_load_vocab: special tokens cache size = 364
 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           = 3584
+llm_load_print_meta: n_layer          = 42
 llm_load_print_meta: n_head           = 16
 llm_load_print_meta: n_head_kv        = 8
-llm_load_print_meta: n_layer          = 42
 llm_load_print_meta: n_rot            = 224
+llm_load_print_meta: n_swa            = 4096
 llm_load_print_meta: n_embd_head_k    = 256
 llm_load_print_meta: n_embd_head_v    = 256
 llm_load_print_meta: n_gqa            = 2
@@ -101,46 +104,46 @@ llama_kv_cache_init:      CUDA0 KV buffer size =   168.00 MiB
 llama_new_context_with_model: KV self size  =  168.00 MiB, K (f16):   84.00 MiB, V (f16):   84.00 MiB
 llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
 llama_new_context_with_model:      CUDA0 compute buffer size =   507.00 MiB
-llama_new_context_with_model:  CUDA_Host compute buffer size =     8.01 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     9.01 MiB
 llama_new_context_with_model: graph nodes  = 1690
 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 93.278 ms
+compute_imatrix: tokenization took 119.102 ms
 compute_imatrix: computing over 128 chunks with batch_size 512
-compute_imatrix: 0.84 seconds per pass - ETA 1.78 minutes
-[1]16.6552,[2]8.0903,[3]6.9962,[4]8.3971,[5]9.2464,[6]9.7300,[7]10.7022,[8]11.6187,[9]11.9992,
+compute_imatrix: 0.85 seconds per pass - ETA 1.82 minutes
+[1]8.2870,[2]5.5692,[3]4.8574,[4]6.1032,[5]6.2583,[6]5.2453,[7]5.7902,[8]6.1617,[9]6.4089,
 save_imatrix: stored collected data after 10 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[10]10.4973,[11]10.2809,[12]11.3377,[13]11.9631,[14]12.0849,[15]12.9207,[16]13.0206,[17]13.0855,[18]13.5418,[19]13.4242,
+[10]5.6400,[11]5.7908,[12]6.3911,[13]6.9578,[14]7.1997,[15]7.8125,[16]8.1619,[17]8.3215,[18]8.6900,[19]8.3100,
 save_imatrix: stored collected data after 20 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[20]13.7130,[21]14.8192,[22]14.8997,[23]14.7292,[24]14.9818,[25]14.8718,[26]14.5962,[27]14.8364,[28]15.0628,[29]15.0534,
+[20]8.5375,[21]8.6913,[22]8.6501,[23]8.8311,[24]8.9421,[25]9.1331,[26]8.8236,[27]9.0735,[28]9.2551,[29]9.1570,
 save_imatrix: stored collected data after 30 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[30]15.2443,[31]14.0970,[32]13.4552,[33]13.0165,[34]12.6321,[35]12.3731,[36]12.5212,[37]12.8425,[38]13.0140,[39]13.1960,
+[30]9.0664,[31]8.5109,[32]8.2338,[33]8.1731,[34]8.0490,[35]8.0060,[36]8.0236,[37]8.0200,[38]8.1009,[39]8.2803,
 save_imatrix: stored collected data after 40 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[40]13.3016,[41]13.3337,[42]13.9154,[43]14.2830,[44]14.6994,[45]14.9396,[46]14.6625,[47]14.4206,[48]14.6417,[49]14.8669,
+[40]8.4557,[41]8.6042,[42]8.8991,[43]9.2142,[44]9.4949,[45]9.6524,[46]9.4993,[47]9.5265,[48]9.7377,[49]9.8946,
 save_imatrix: stored collected data after 50 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[50]14.6774,[51]14.5402,[52]14.6070,[53]14.8337,[54]15.1521,[55]15.4017,[56]15.5381,[57]15.5412,[58]15.5433,[59]15.3203,
+[50]9.6778,[51]9.7231,[52]9.7711,[53]9.9173,[54]10.1400,[55]10.2482,[56]10.3176,[57]10.3220,[58]10.3506,[59]10.1951,
 save_imatrix: stored collected data after 60 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[60]15.1412,[61]14.9436,[62]14.7716,[63]14.8956,[64]15.0538,[65]14.9025,[66]14.9251,[67]14.8897,[68]14.8316,[69]14.7527,
+[60]10.0656,[61]9.9265,[62]9.8784,[63]9.9241,[64]9.9184,[65]9.8962,[66]9.9283,[67]9.8701,[68]9.7891,[69]9.8077,
 save_imatrix: stored collected data after 70 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[70]14.6922,[71]14.6700,[72]14.6271,[73]14.6927,[74]14.6179,[75]14.4837,[76]14.4578,[77]14.4619,[78]14.4124,[79]14.3156,
+[70]9.7677,[71]9.7514,[72]9.7605,[73]9.7345,[74]9.6684,[75]9.6283,[76]9.6233,[77]9.6320,[78]9.6199,[79]9.5661,
 save_imatrix: stored collected data after 80 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[80]14.3808,[81]14.4619,[82]14.4841,[83]14.5843,[84]14.6263,[85]14.3919,[86]14.3208,[87]14.1543,[88]14.1997,[89]14.1722,
+[80]9.6315,[81]9.6843,[82]9.6604,[83]9.6603,[84]9.7184,[85]9.5761,[86]9.5323,[87]9.4649,[88]9.4783,[89]9.5114,
 save_imatrix: stored collected data after 90 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[90]14.2455,[91]14.1862,[92]14.0935,[93]14.0125,[94]13.8942,[95]13.8216,[96]13.7275,[97]13.6539,[98]13.5585,[99]13.6079,
+[90]9.5357,[91]9.4612,[92]9.3816,[93]9.2879,[94]9.1958,[95]9.1295,[96]9.0453,[97]8.9720,[98]8.9035,[99]8.9543,
 save_imatrix: stored collected data after 100 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[100]13.6242,[101]13.7690,[102]13.8623,[103]13.9214,[104]14.1043,[105]14.2414,[106]14.2558,[107]14.2652,[108]14.1996,[109]14.2351,
+[100]8.9876,[101]9.0807,[102]9.1655,[103]9.2443,[104]9.4136,[105]9.5366,[106]9.5596,[107]9.5914,[108]9.6093,[109]9.5883,
 save_imatrix: stored collected data after 110 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[110]14.1081,[111]13.9695,[112]13.7990,[113]13.8842,[114]13.9366,[115]13.9254,[116]13.8930,[117]13.9520,[118]13.9874,[119]14.0052,
+[110]9.5676,[111]9.4936,[112]9.4150,[113]9.4655,[114]9.4851,[115]9.4909,[116]9.4830,[117]9.5297,[118]9.5509,[119]9.5557,
 save_imatrix: stored collected data after 120 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
-[120]13.9951,[121]14.0036,[122]13.9634,[123]13.9903,[124]14.0884,[125]14.1808,[126]14.2925,[127]14.3374,[128]14.3945,
+[120]9.5669,[121]9.6127,[122]9.5730,[123]9.6293,[124]9.6857,[125]9.7251,[126]9.7980,[127]9.8556,[128]9.9097,
 save_imatrix: stored collected data after 128 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
 
-llama_print_timings:        load time =    6383.63 ms
+llama_print_timings:        load time =   14925.71 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 =   91484.26 ms / 65536 tokens (    1.40 ms per token,   716.36 tokens per second)
+llama_print_timings: prompt eval time =   96055.84 ms / 65536 tokens (    1.47 ms per token,   682.27 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 =   98451.28 ms / 65537 tokens
+llama_print_timings:       total time =  111564.87 ms / 65537 tokens
 
-Final estimate: PPL = 14.3945 +/- 0.28244
+Final estimate: PPL = 9.9097 +/- 0.16369