Hugging Face's logo

legraphista
/
xLAM-8x7b-r-IMat-GGUF

Text Generation
GGUF
PyTorch
English
function-calling
LLM Agent
tool-use
mistral
quantized
GGUF
quantization
imat
imatrix
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
Model card Files Community
legraphista commited on
Commit
e2d3fc9
•
1 Parent(s): 0ba4c70

Upload imatrix.log with huggingface_hub

Browse files
Files changed (1) hide show
  1. imatrix.log +159 -0
imatrix.log ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ llama_model_loader: loaded meta data with 36 key-value pairs and 323 tensors from xLAM-8x7b-r-IMat-GGUF/xLAM-8x7b-r.Q8_0.gguf.hardlink.gguf (version GGUF V3 (latest))
2
+ llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
3
+ llama_model_loader: - kv 0: general.architecture str = llama
4
+ llama_model_loader: - kv 1: general.type str = model
5
+ llama_model_loader: - kv 2: general.name str = xLAM 8x7b R
6
+ llama_model_loader: - kv 3: general.finetune str = r
7
+ llama_model_loader: - kv 4: general.basename str = xLAM
8
+ llama_model_loader: - kv 5: general.size_label str = 8x7B
9
+ llama_model_loader: - kv 6: general.license str = cc-by-nc-4.0
10
+ llama_model_loader: - kv 7: general.tags arr[str,6] = ["function-calling", "LLM Agent", "to...
11
+ llama_model_loader: - kv 8: general.languages arr[str,1] = ["en"]
12
+ llama_model_loader: - kv 9: general.datasets arr[str,1] = ["Salesforce/xlam-function-calling-60k"]
13
+ llama_model_loader: - kv 10: llama.block_count u32 = 32
14
+ llama_model_loader: - kv 11: llama.context_length u32 = 32768
15
+ llama_model_loader: - kv 12: llama.embedding_length u32 = 4096
16
+ llama_model_loader: - kv 13: llama.feed_forward_length u32 = 14336
17
+ llama_model_loader: - kv 14: llama.attention.head_count u32 = 32
18
+ llama_model_loader: - kv 15: llama.attention.head_count_kv u32 = 8
19
+ llama_model_loader: - kv 16: llama.rope.freq_base f32 = 1000000.000000
20
+ llama_model_loader: - kv 17: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
21
+ llama_model_loader: - kv 18: llama.expert_count u32 = 8
22
+ llama_model_loader: - kv 19: llama.expert_used_count u32 = 2
23
+ llama_model_loader: - kv 20: general.file_type u32 = 7
24
+ llama_model_loader: - kv 21: llama.vocab_size u32 = 32000
25
+ llama_model_loader: - kv 22: llama.rope.dimension_count u32 = 128
26
+ llama_model_loader: - kv 23: tokenizer.ggml.add_space_prefix bool = false
27
+ llama_model_loader: - kv 24: tokenizer.ggml.model str = llama
28
+ llama_model_loader: - kv 25: tokenizer.ggml.pre str = default
29
+ llama_model_loader: - kv 26: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
30
+ llama_model_loader: - kv 27: tokenizer.ggml.scores arr[f32,32000] = [-1000.000000, -1000.000000, -1000.00...
31
+ llama_model_loader: - kv 28: tokenizer.ggml.token_type arr[i32,32000] = [3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
32
+ llama_model_loader: - kv 29: tokenizer.ggml.bos_token_id u32 = 1
33
+ llama_model_loader: - kv 30: tokenizer.ggml.eos_token_id u32 = 2
34
+ llama_model_loader: - kv 31: tokenizer.ggml.unknown_token_id u32 = 0
35
+ llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = false
36
+ llama_model_loader: - kv 33: tokenizer.ggml.add_eos_token bool = false
37
+ llama_model_loader: - kv 34: tokenizer.chat_template str = {%- if messages[0]['role'] == 'system...
38
+ llama_model_loader: - kv 35: general.quantization_version u32 = 2
39
+ llama_model_loader: - type f32: 97 tensors
40
+ llama_model_loader: - type q8_0: 226 tensors
41
+ llm_load_vocab: special tokens cache size = 3
42
+ llm_load_vocab: token to piece cache size = 0.1637 MB
43
+ llm_load_print_meta: format = GGUF V3 (latest)
44
+ llm_load_print_meta: arch = llama
45
+ llm_load_print_meta: vocab type = SPM
46
+ llm_load_print_meta: n_vocab = 32000
47
+ llm_load_print_meta: n_merges = 0
48
+ llm_load_print_meta: vocab_only = 0
49
+ llm_load_print_meta: n_ctx_train = 32768
50
+ llm_load_print_meta: n_embd = 4096
51
+ llm_load_print_meta: n_layer = 32
52
+ llm_load_print_meta: n_head = 32
53
+ llm_load_print_meta: n_head_kv = 8
54
+ llm_load_print_meta: n_rot = 128
55
+ llm_load_print_meta: n_swa = 0
56
+ llm_load_print_meta: n_embd_head_k = 128
57
+ llm_load_print_meta: n_embd_head_v = 128
58
+ llm_load_print_meta: n_gqa = 4
59
+ llm_load_print_meta: n_embd_k_gqa = 1024
60
+ llm_load_print_meta: n_embd_v_gqa = 1024
61
+ llm_load_print_meta: f_norm_eps = 0.0e+00
62
+ llm_load_print_meta: f_norm_rms_eps = 1.0e-05
63
+ llm_load_print_meta: f_clamp_kqv = 0.0e+00
64
+ llm_load_print_meta: f_max_alibi_bias = 0.0e+00
65
+ llm_load_print_meta: f_logit_scale = 0.0e+00
66
+ llm_load_print_meta: n_ff = 14336
67
+ llm_load_print_meta: n_expert = 8
68
+ llm_load_print_meta: n_expert_used = 2
69
+ llm_load_print_meta: causal attn = 1
70
+ llm_load_print_meta: pooling type = 0
71
+ llm_load_print_meta: rope type = 0
72
+ llm_load_print_meta: rope scaling = linear
73
+ llm_load_print_meta: freq_base_train = 1000000.0
74
+ llm_load_print_meta: freq_scale_train = 1
75
+ llm_load_print_meta: n_ctx_orig_yarn = 32768
76
+ llm_load_print_meta: rope_finetuned = unknown
77
+ llm_load_print_meta: ssm_d_conv = 0
78
+ llm_load_print_meta: ssm_d_inner = 0
79
+ llm_load_print_meta: ssm_d_state = 0
80
+ llm_load_print_meta: ssm_dt_rank = 0
81
+ llm_load_print_meta: ssm_dt_b_c_rms = 0
82
+ llm_load_print_meta: model type = 8x7B
83
+ llm_load_print_meta: model ftype = Q8_0
84
+ llm_load_print_meta: model params = 46.70 B
85
+ llm_load_print_meta: model size = 46.22 GiB (8.50 BPW)
86
+ llm_load_print_meta: general.name = xLAM 8x7b R
87
+ llm_load_print_meta: BOS token = 1 '<s>'
88
+ llm_load_print_meta: EOS token = 2 '</s>'
89
+ llm_load_print_meta: UNK token = 0 '<unk>'
90
+ llm_load_print_meta: LF token = 13 '<0x0A>'
91
+ llm_load_print_meta: max token length = 48
92
+ ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
93
+ ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
94
+ ggml_cuda_init: found 1 CUDA devices:
95
+ Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
96
+ llm_load_tensors: ggml ctx size = 0.29 MiB
97
+ llm_load_tensors: offloading 14 repeating layers to GPU
98
+ llm_load_tensors: offloaded 14/33 layers to GPU
99
+ llm_load_tensors: CPU buffer size = 47326.64 MiB
100
+ llm_load_tensors: CUDA0 buffer size = 20589.19 MiB
101
+ ...................................................................................................
102
+ llama_new_context_with_model: n_ctx = 512
103
+ llama_new_context_with_model: n_batch = 512
104
+ llama_new_context_with_model: n_ubatch = 512
105
+ llama_new_context_with_model: flash_attn = 0
106
+ llama_new_context_with_model: freq_base = 1000000.0
107
+ llama_new_context_with_model: freq_scale = 1
108
+ llama_kv_cache_init: CUDA_Host KV buffer size = 36.00 MiB
109
+ llama_kv_cache_init: CUDA0 KV buffer size = 28.00 MiB
110
+ llama_new_context_with_model: KV self size = 64.00 MiB, K (f16): 32.00 MiB, V (f16): 32.00 MiB
111
+ llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
112
+ llama_new_context_with_model: CUDA0 compute buffer size = 621.00 MiB
113
+ llama_new_context_with_model: CUDA_Host compute buffer size = 9.01 MiB
114
+ llama_new_context_with_model: graph nodes = 1510
115
+ llama_new_context_with_model: graph splits = 220
116
+
117
+ system_info: n_threads = 25 (n_threads_batch = 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 |
118
+ compute_imatrix: tokenizing the input ..
119
+ compute_imatrix: tokenization took 94.132 ms
120
+ compute_imatrix: computing over 148 chunks with batch_size 512
121
+ compute_imatrix: 2.73 seconds per pass - ETA 6.72 minutes
122
+ [1]3.2091,[2]2.5764,[3]2.6670,[4]2.7400,[5]3.0794,[6]3.0442,[7]2.7896,[8]3.1600,[9]3.2348,
123
+ save_imatrix: stored collected data after 10 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
124
+ [10]3.6092,[11]3.7451,[12]3.5304,[13]3.7238,[14]3.9647,[15]4.2845,[16]4.4296,[17]4.5914,[18]4.6927,[19]4.7570,
125
+ save_imatrix: stored collected data after 20 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
126
+ [20]4.8856,[21]4.8333,[22]4.6749,[23]4.8031,[24]4.7572,[25]4.7618,[26]4.6138,[27]4.7839,[28]4.6925,[29]4.7802,
127
+ save_imatrix: stored collected data after 30 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
128
+ [30]4.6565,[31]4.5694,[32]4.4464,[33]4.3287,[34]4.3669,[35]4.3448,[36]4.2097,[37]4.1172,[38]4.0459,[39]3.9975,
129
+ save_imatrix: stored collected data after 40 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
130
+ [40]3.9660,[41]3.9756,[42]3.9421,[43]3.9288,[44]3.8875,[45]3.8719,[46]3.8937,[47]3.8857,[48]3.9613,[49]3.9866,
131
+ save_imatrix: stored collected data after 50 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
132
+ [50]3.9215,[51]3.8327,[52]3.8007,[53]3.8036,[54]3.8267,[55]3.8089,[56]3.8003,[57]3.8687,[58]3.9379,[59]3.9762,
133
+ save_imatrix: stored collected data after 60 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
134
+ [60]3.9515,[61]3.9584,[62]3.9914,[63]4.0286,[64]4.0969,[65]4.1267,[66]4.1606,[67]4.1925,[68]4.2290,[69]4.2514,
135
+ save_imatrix: stored collected data after 70 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
136
+ [70]4.2529,[71]4.2157,[72]4.1911,[73]4.1892,[74]4.2009,[75]4.2373,[76]4.2392,[77]4.2655,[78]4.2845,[79]4.2767,
137
+ save_imatrix: stored collected data after 80 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
138
+ [80]4.2793,[81]4.2705,[82]4.2883,[83]4.3023,[84]4.3108,[85]4.3300,[86]4.3293,[87]4.3292,[88]4.3255,[89]4.3388,
139
+ save_imatrix: stored collected data after 90 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
140
+ [90]4.3318,[91]4.3218,[92]4.3130,[93]4.3091,[94]4.3297,[95]4.3524,[96]4.3495,[97]4.3517,[98]4.3467,[99]4.3760,
141
+ save_imatrix: stored collected data after 100 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
142
+ [100]4.3427,[101]4.3451,[102]4.3358,[103]4.3504,[104]4.3666,[105]4.3633,[106]4.3460,[107]4.3235,[108]4.3017,[109]4.2777,
143
+ save_imatrix: stored collected data after 110 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
144
+ [110]4.2546,[111]4.2344,[112]4.2140,[113]4.1946,[114]4.1741,[115]4.1523,[116]4.1599,[117]4.1781,[118]4.2199,[119]4.2643,
145
+ save_imatrix: stored collected data after 120 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
146
+ [120]4.3022,[121]4.3614,[122]4.4127,[123]4.4204,[124]4.4323,[125]4.4065,[126]4.4017,[127]4.3960,[128]4.3967,[129]4.3648,
147
+ save_imatrix: stored collected data after 130 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
148
+ [130]4.3317,[131]4.3546,[132]4.3780,[133]4.3820,[134]4.3796,[135]4.3935,[136]4.4164,[137]4.4227,[138]4.4354,[139]4.4547,
149
+ save_imatrix: stored collected data after 140 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
150
+ [140]4.4655,[141]4.4630,[142]4.4731,[143]4.4571,[144]4.4257,[145]4.4329,[146]4.4292,[147]4.4230,[148]4.4107,
151
+ save_imatrix: stored collected data after 148 chunks in xLAM-8x7b-r-IMat-GGUF/imatrix.dat
152
+
153
+ llama_print_timings: load time = 18115.96 ms
154
+ llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
155
+ llama_print_timings: prompt eval time = 377344.94 ms / 75776 tokens ( 4.98 ms per token, 200.81 tokens per second)
156
+ llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
157
+ llama_print_timings: total time = 393295.61 ms / 75777 tokens
158
+
159
+ Final estimate: PPL = 4.4107 +/- 0.04800