developer_uid: zonemercy
submission_id: chaiml-bol-opusd-v7-lr5_65112_v1
model_name: chaiml-bol-opusd-v7-lr5_65112_v1
model_group: ChaiML/bol-opusd-v7-lr5e
status: torndown
timestamp: 2026-02-08T09:12:12+00:00
num_battles: 10840
num_wins: 6234
celo_rating: 1358.62
family_friendly_score: 0.0
family_friendly_standard_error: 0.0
submission_type: basic
model_repo: ChaiML/bol-opusd-v7-lr5e6ep2r64g4b02
model_architecture: Qwen3MoeForCausalLM
model_num_parameters: 18790207488.0
best_of: 8
max_input_tokens: 2048
max_output_tokens: 80
reward_model: default
display_name: chaiml-bol-opusd-v7-lr5_65112_v1
ineligible_reason: max_output_tokens!=64
is_internal_developer: True
language_model: ChaiML/bol-opusd-v7-lr5e6ep2r64g4b02
model_size: 19B
ranking_group: single
us_pacific_date: 2026-02-05
win_ratio: 0.5750922509225093
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 40, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['<|assistant|>', '<|im_end|>', '####', '</s>', '</think>', '<|user|>'], 'max_input_tokens': 2048, 'best_of': 8, 'max_output_tokens': 80}
formatter: {'memory_template': "<|im_start|>system\n{bot_name}'s persona: {memory}<|im_end|>\n", 'prompt_template': '', 'bot_template': '<|im_start|>assistant\n{bot_name}: {message}<|im_end|>\n', 'user_template': '<|im_start|>user\n{message}<|im_end|>\n', 'response_template': '<|im_start|>assistant\n{bot_name}:', 'truncate_by_message': True}
Resubmit model
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Starting job with name chaiml-bol-opusd-v7-lr5-65112-v1-uploader
Waiting for job on chaiml-bol-opusd-v7-lr5-65112-v1-uploader to finish
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Using quantization_mode: w4a16
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Checking if ChaiML/bol-opusd-v7-lr5e6ep2r64g4b02-W4A16 already exists in ChaiML
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Downloading snapshot of ChaiML/bol-opusd-v7-lr5e6ep2r64g4b02...
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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Downloaded in 162.508s
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Applying quantization...
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: 2026-02-04 21:29:33 WARNING modeling_utils.py L4670: `torch_dtype` is deprecated! Use `dtype` instead!
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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: 2026-02-04 21:29:55 INFO base.py L366: using torch.bfloat16 for quantization tuning
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: 2026-02-04 21:29:59 INFO base.py L1145: start to compute imatrix
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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: 0%| | 0/94 [00:00<?, ?it/s] Quantizing model.layers.0: 0%| | 0/94 [00:00<?, ?it/s]/usr/local/lib/python3.12/dist-packages/torch/backends/cuda/__init__.py:131: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: return torch._C._get_cublas_allow_tf32()
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: W0204 21:31:04.636000 7 torch/_dynamo/convert_frame.py:1358] [3/8] torch._dynamo hit config.recompile_limit (8)
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: W0204 21:31:04.636000 7 torch/_dynamo/convert_frame.py:1358] [3/8] function: 'forward' (/usr/local/lib/python3.12/dist-packages/transformers/models/qwen3_moe/modeling_qwen3_moe.py:305)
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: W0204 21:31:04.636000 7 torch/_dynamo/convert_frame.py:1358] [3/8] last reason: 3/7: self._modules['self_attn']._modules['k_proj'].imatrix_cnt == 56 # module.imatrix_cnt += input.shape[0] # auto_round/compressors/base.py:1179 in get_imatrix_hook (HINT: torch.compile considers integer attributes of the nn.Module to be static. If you are observing recompilation, you might want to make this integer dynamic using torch._dynamo.config.allow_unspec_int_on_nn_module = True, or convert this integer into a tensor.)
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: W0204 21:31:04.636000 7 torch/_dynamo/convert_frame.py:1358] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles".
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: W0204 21:31:04.636000 7 torch/_dynamo/convert_frame.py:1358] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: 2026-02-04 21:31:22 WARNING gguf.py L297: please use more data via setting `nsamples` to improve accuracy as calibration activations contain 0
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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Quantizing model.layers.0: 1%| | 1/94 [01:36<2:29:04, 96.17s/it] Quantizing model.layers.1: 1%| | 1/94 [01:36<2:29:04, 96.17s/it] Quantizing model.layers.1: 2%|▏ | 2/94 [02:24<1:44:36, 68.23s/it] Quantizing model.layers.2: 2%|▏ | 2/94 [02:24<1:44:36, 68.23s/it] Quantizing model.layers.2: 3%|▎ | 3/94 [03:14<1:30:16, 59.53s/it] Quantizing model.layers.3: 3%|▎ | 3/94 [03:14<1:30:16, 59.53s/it] Quantizing model.layers.3: 4%|▍ | 4/94 [04:02<1:23:02, 55.36s/it] Quantizing model.layers.4: 4%|▍ | 4/94 [04:02<1:23:02, 55.36s/it] Quantizing model.layers.4: 5%|▌ | 5/94 [04:51<1:18:38, 53.02s/it] Quantizing model.layers.5: 5%|▌ | 5/94 [04:51<1:18:38, 53.02s/it] Quantizing model.layers.5: 6%|▋ | 6/94 [05:40<1:15:39, 51.59s/it] Quantizing model.layers.6: 6%|▋ | 6/94 [05:40<1:15:39, 51.59s/it] Quantizing model.layers.6: 7%|▋ | 7/94 [06:28<1:13:00, 50.35s/it] Quantizing model.layers.7: 7%|▋ | 7/94 [06:28<1:13:00, 50.35s/it] Quantizing model.layers.7: 9%|▊ | 8/94 [07:16<1:11:08, 49.64s/it] Quantizing model.layers.8: 9%|▊ | 8/94 [07:16<1:11:08, 49.64s/it] Quantizing model.layers.8: 10%|▉ | 9/94 [08:04<1:09:42, 49.21s/it] Quantizing model.layers.9: 10%|▉ | 9/94 [08:04<1:09:42, 49.21s/it] Quantizing model.layers.9: 11%|█ | 10/94 [08:53<1:08:36, 49.01s/it] Quantizing model.layers.10: 11%|█ | 10/94 [08:53<1:08:36, 49.01s/it] Quantizing model.layers.10: 12%|█▏ | 11/94 [09:42<1:07:52, 49.07s/it] Quantizing model.layers.11: 12%|█▏ | 11/94 [09:42<1:07:52, 49.07s/it] Quantizing model.layers.11: 13%|█▎ | 12/94 [10:31<1:07:09, 49.13s/it] Quantizing model.layers.12: 13%|█▎ | 12/94 [10:31<1:07:09, 49.13s/it] Quantizing model.layers.12: 14%|█▍ | 13/94 [11:20<1:06:12, 49.05s/it] Quantizing model.layers.13: 14%|█▍ | 13/94 [11:20<1:06:12, 49.05s/it] Quantizing model.layers.13: 15%|█▍ | 14/94 [12:09<1:05:19, 49.00s/it] Quantizing model.layers.14: 15%|█▍ | 14/94 [12:09<1:05:19, 49.00s/it] Quantizing model.layers.14: 16%|█▌ | 15/94 [12:58<1:04:38, 49.10s/it] Quantizing model.layers.15: 16%|█▌ | 15/94 [12:58<1:04:38, 49.10s/it] Quantizing model.layers.15: 17%|█▋ | 16/94 [13:48<1:04:02, 49.27s/it] Quantizing model.layers.16: 17%|█▋ | 16/94 [13:48<1:04:02, 49.27s/it] Quantizing model.layers.16: 18%|█▊ | 17/94 [14:37<1:03:16, 49.31s/it] Quantizing model.layers.17: 18%|█▊ | 17/94 [14:37<1:03:16, 49.31s/it] Quantizing model.layers.17: 19%|█▉ | 18/94 [15:27<1:02:22, 49.24s/it] Quantizing model.layers.18: 19%|█▉ | 18/94 [15:27<1:02:22, 49.24s/it] Quantizing model.layers.18: 20%|██ | 19/94 [16:16<1:01:28, 49.18s/it] Quantizing model.layers.19: 20%|██ | 19/94 [16:16<1:01:28, 49.18s/it] Quantizing model.layers.19: 21%|██▏ | 20/94 [17:04<1:00:28, 49.04s/it] Quantizing model.layers.20: 21%|██▏ | 20/94 [17:04<1:00:28, 49.04s/it] Quantizing model.layers.20: 22%|██▏ | 21/94 [17:53<59:28, 48.88s/it] Quantizing model.layers.21: 22%|██▏ | 21/94 [17:53<59:28, 48.88s/it] Quantizing model.layers.21: 23%|██▎ | 22/94 [18:41<58:33, 48.80s/it] Quantizing model.layers.22: 23%|██▎ | 22/94 [18:41<58:33, 48.80s/it] Quantizing model.layers.22: 24%|██▍ | 23/94 [19:31<57:53, 48.92s/it] Quantizing model.layers.23: 24%|██▍ | 23/94 [19:31<57:53, 48.92s/it] Quantizing model.layers.23: 26%|██▌ | 24/94 [20:20<57:09, 49.00s/it] Quantizing model.layers.24: 26%|██▌ | 24/94 [20:20<57:09, 49.00s/it] Quantizing model.layers.24: 27%|██▋ | 25/94 [21:09<56:20, 48.99s/it] Quantizing model.layers.25: 27%|██▋ | 25/94 [21:09<56:20, 48.99s/it] Quantizing model.layers.25: 28%|██▊ | 26/94 [21:58<55:29, 48.96s/it] Quantizing model.layers.26: 28%|██▊ | 26/94 [21:58<55:29, 48.96s/it] Quantizing model.layers.26: 29%|██▊ | 27/94 [22:47<54:46, 49.06s/it] Quantizing model.layers.27: 29%|██▊ | 27/94 [22:47<54:46, 49.06s/it] Quantizing model.layers.27: 30%|██▉ | 28/94 [23:36<53:49, 48.94s/it] Quantizing model.layers.28: 30%|██▉ | 28/94 [23:36<53:49, 48.94s/it] Quantizing model.layers.28: 31%|███ | 29/94 [24:25<53:03, 48.97s/it] Quantizing model.layers.29: 31%|███ | 29/94 [24:25<53:03, 48.97s/it] Quantizing model.layers.29: 32%|███▏ | 30/94 [25:13<52:04, 48.82s/it] Quantizing model.layers.30: 32%|███▏ | 30/94 [25:13<52:04, 48.82s/it] Quantizing model.layers.30: 33%|███▎ | 31/94 [26:02<51:07, 48.69s/it] Quantizing model.layers.31: 33%|███▎ | 31/94 [26:02<51:07, 48.69s/it] Quantizing model.layers.31: 34%|███▍ | 32/94 [26:51<50:28, 48.84s/it] Quantizing model.layers.32: 34%|███▍ | 32/94 [26:51<50:28, 48.84s/it] Quantizing model.layers.32: 35%|███▌ | 33/94 [27:40<49:48, 48.99s/it] Quantizing model.layers.33: 35%|███▌ | 33/94 [27:40<49:48, 48.99s/it] Quantizing model.layers.33: 36%|███▌ | 34/94 [28:29<48:50, 48.84s/it] Quantizing model.layers.34: 36%|███▌ | 34/94 [28:29<48:50, 48.84s/it] Quantizing model.layers.34: 37%|███▋ | 35/94 [29:18<48:13, 49.05s/it] Quantizing model.layers.35: 37%|███▋ | 35/94 [29:18<48:13, 49.05s/it] Quantizing model.layers.35: 38%|███▊ | 36/94 [30:07<47:29, 49.13s/it] Quantizing model.layers.36: 38%|███▊ | 36/94 [30:07<47:29, 49.13s/it] Quantizing model.layers.36: 39%|███▉ | 37/94 [30:59<47:15, 49.74s/it] Quantizing model.layers.37: 39%|███▉ | 37/94 [30:59<47:15, 49.74s/it] Quantizing model.layers.37: 40%|████ | 38/94 [31:47<45:57, 49.24s/it] Quantizing model.layers.38: 40%|████ | 38/94 [31:47<45:57, 49.24s/it] Quantizing model.layers.38: 41%|████▏ | 39/94 [32:36<45:06, 49.21s/it] Quantizing model.layers.39: 41%|████▏ | 39/94 [32:36<45:06, 49.21s/it] Quantizing model.layers.39: 43%|████▎ | 40/94 [33:25<44:13, 49.14s/it] Quantizing model.layers.40: 43%|████▎ | 40/94 [33:25<44:13, 49.14s/it] Quantizing model.layers.40: 44%|████▎ | 41/94 [34:13<43:15, 48.97s/it] Quantizing model.layers.41: 44%|████▎ | 41/94 [34:13<43:15, 48.97s/it] Quantizing model.layers.41: 45%|████▍ | 42/94 [35:03<42:33, 49.11s/it] Quantizing model.layers.42: 45%|████▍ | 42/94 [35:03<42:33, 49.11s/it] Quantizing model.layers.42: 46%|████▌ | 43/94 [35:52<41:47, 49.18s/it] Quantizing model.layers.43: 46%|████▌ | 43/94 [35:52<41:47, 49.18s/it] Quantizing model.layers.43: 47%|████▋ | 44/94 [36:43<41:17, 49.54s/it] Quantizing model.layers.44: 47%|████▋ | 44/94 [36:43<41:17, 49.54s/it] Quantizing model.layers.44: 48%|████▊ | 45/94 [37:34<40:56, 50.12s/it] Quantizing model.layers.45: 48%|████▊ | 45/94 [37:34<40:56, 50.12s/it] Quantizing model.layers.45: 49%|████▉ | 46/94 [38:24<40:07, 50.16s/it] Quantizing model.layers.46: 49%|████▉ | 46/94 [38:24<40:07, 50.16s/it] Quantizing model.layers.46: 50%|█████ | 47/94 [39:15<39:27, 50.37s/it] Quantizing model.layers.47: 50%|█████ | 47/94 [39:15<39:27, 50.37s/it] Quantizing model.layers.47: 51%|█████ | 48/94 [40:06<38:45, 50.55s/it] Quantizing model.layers.48: 51%|█████ | 48/94 [40:06<38:45, 50.55s/it] Quantizing model.layers.48: 52%|█████▏ | 49/94 [40:58<38:13, 50.97s/it] Quantizing model.layers.49: 52%|█████▏ | 49/94 [40:58<38:13, 50.97s/it] Quantizing model.layers.49: 53%|█████▎ | 50/94 [41:48<37:16, 50.82s/it] Quantizing model.layers.50: 53%|█████▎ | 50/94 [41:48<37:16, 50.82s/it] Quantizing model.layers.50: 54%|█████▍ | 51/94 [42:40<36:32, 51.00s/it] Quantizing model.layers.51: 54%|█████▍ | 51/94 [42:40<36:32, 51.00s/it] Quantizing model.layers.51: 55%|█████▌ | 52/94 [43:32<35:55, 51.32s/it] Quantizing model.layers.52: 55%|█████▌ | 52/94 [43:32<35:55, 51.32s/it] Quantizing model.layers.52: 56%|█████▋ | 53/94 [44:23<35:01, 51.26s/it] Quantizing model.layers.53: 56%|█████▋ | 53/94 [44:23<35:01, 51.26s/it] Quantizing model.layers.53: 57%|█████▋ | 54/94 [45:14<34:06, 51.17s/it] Quantizing model.layers.54: 57%|█████▋ | 54/94 [45:14<34:06, 51.17s/it] Quantizing model.layers.54: 59%|█████▊ | 55/94 [46:05<33:12, 51.09s/it] Quantizing model.layers.55: 59%|█████▊ | 55/94 [46:05<33:12, 51.09s/it] Quantizing model.layers.55: 60%|█████▉ | 56/94 [46:55<32:12, 50.86s/it] Quantizing model.layers.56: 60%|█████▉ | 56/94 [46:55<32:12, 50.86s/it] Quantizing model.layers.56: 61%|██████ | 57/94 [47:46<31:25, 50.95s/it] Quantizing model.layers.57: 61%|██████ | 57/94 [47:46<31:25, 50.95s/it] Quantizing model.layers.57: 62%|██████▏ | 58/94 [48:36<30:18, 50.52s/it] Quantizing model.layers.58: 62%|██████▏ | 58/94 [48:36<30:18, 50.52s/it] Quantizing model.layers.58: 63%|██████▎ | 59/94 [49:27<29:34, 50.70s/it] Quantizing model.layers.59: 63%|██████▎ | 59/94 [49:27<29:34, 50.70s/it] Quantizing model.layers.59: 64%|██████▍ | 60/94 [50:16<28:28, 50.25s/it] Quantizing model.layers.60: 64%|██████▍ | 60/94 [50:16<28:28, 50.25s/it] Quantizing model.layers.60: 65%|██████▍ | 61/94 [51:05<27:24, 49.83s/it] Quantizing model.layers.61: 65%|██████▍ | 61/94 [51:05<27:24, 49.83s/it] Quantizing model.layers.61: 66%|██████▌ | 62/94 [51:54<26:26, 49.59s/it] Quantizing model.layers.62: 66%|██████▌ | 62/94 [51:54<26:26, 49.59s/it] Quantizing model.layers.62: 67%|██████▋ | 63/94 [52:42<25:17, 48.95s/it] Quantizing model.layers.63: 67%|██████▋ | 63/94 [52:42<25:17, 48.95s/it] Quantizing model.layers.63: 68%|██████▊ | 64/94 [53:32<24:42, 49.42s/it] Quantizing model.layers.64: 68%|██████▊ | 64/94 [53:32<24:42, 49.42s/it] Quantizing model.layers.64: 69%|██████▉ | 65/94 [54:22<24:00, 49.68s/it] Quantizing model.layers.65: 69%|██████▉ | 65/94 [54:22<24:00, 49.68s/it] Quantizing model.layers.65: 70%|███████ | 66/94 [55:12<23:13, 49.77s/it] Quantizing model.layers.66: 70%|███████ | 66/94 [55:12<23:13, 49.77s/it] Quantizing model.layers.66: 71%|███████▏ | 67/94 [56:02<22:21, 49.68s/it] Quantizing model.layers.67: 71%|███████▏ | 67/94 [56:02<22:21, 49.68s/it] Quantizing model.layers.67: 72%|███████▏ | 68/94 [56:49<21:13, 48.98s/it] Quantizing model.layers.68: 72%|███████▏ | 68/94 [56:49<21:13, 48.98s/it] Quantizing model.layers.68: 73%|███████▎ | 69/94 [57:38<20:25, 49.02s/it] Quantizing model.layers.69: 73%|███████▎ | 69/94 [57:38<20:25, 49.02s/it] Quantizing model.layers.69: 74%|███████▍ | 70/94 [58:30<19:52, 49.69s/it] Quantizing model.layers.70: 74%|███████▍ | 70/94 [58:30<19:52, 49.69s/it] Quantizing model.layers.70: 76%|███████▌ | 71/94 [59:17<18:47, 49.00s/it] Quantizing model.layers.71: 76%|███████▌ | 71/94 [59:17<18:47, 49.00s/it] Quantizing model.layers.71: 77%|███████▋ | 72/94 [1:00:04<17:47, 48.53s/it] Quantizing model.layers.72: 77%|███████▋ | 72/94 [1:00:04<17:47, 48.53s/it] Quantizing model.layers.72: 78%|███████▊ | 73/94 [1:00:52<16:55, 48.34s/it] Quantizing model.layers.73: 78%|███████▊ | 73/94 [1:00:52<16:55, 48.34s/it] Quantizing model.layers.73: 79%|███████▊ | 74/94 [1:01:41<16:11, 48.58s/it] Quantizing model.layers.74: 79%|███████▊ | 74/94 [1:01:41<16:11, 48.58s/it] Quantizing model.layers.74: 80%|███████▉ | 75/94 [1:02:27<15:03, 47.53s/it] Quantizing model.layers.75: 80%|███████▉ | 75/94 [1:02:27<15:03, 47.53s/it] Quantizing model.layers.75: 81%|████████ | 76/94 [1:03:15<14:20, 47.81s/it] Quantizing model.layers.76: 81%|████████ | 76/94 [1:03:15<14:20, 47.81s/it] Quantizing model.layers.76: 82%|████████▏ | 77/94 [1:04:04<13:40, 48.29s/it] Quantizing model.layers.77: 82%|████████▏ | 77/94 [1:04:04<13:40, 48.29s/it] Quantizing 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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: 0%| | 0/36566 [00:00<?, ?it/s] packing model.layers.0.mlp.gate: 0%| | 0/36566 [00:00<?, ?it/s] packing model.layers.0.mlp.gate: 0%| | 1/36566 [00:01<10:23:34, 1.02s/it] packing model.layers.1.mlp.gate: 0%| | 1/36566 [00:01<10:23:34, 1.02s/it] packing model.layers.2.mlp.gate: 0%| | 2/36566 [00:01<10:23:33, 1.02s/it] packing model.layers.3.mlp.gate: 0%| | 3/36566 [00:01<10:23:32, 1.02s/it] packing model.layers.4.mlp.gate: 0%| | 4/36566 [00:01<10:23:31, 1.02s/it] packing model.layers.5.mlp.gate: 0%| | 5/36566 [00:01<10:23:30, 1.02s/it] packing model.layers.6.mlp.gate: 0%| | 6/36566 [00:01<10:23:29, 1.02s/it] packing model.layers.7.mlp.gate: 0%| | 7/36566 [00:01<10:23:28, 1.02s/it] packing model.layers.8.mlp.gate: 0%| | 8/36566 [00:01<10:23:27, 1.02s/it] packing model.layers.9.mlp.gate: 0%| | 9/36566 [00:01<10:23:26, 1.02s/it] packing model.layers.10.mlp.gate: 0%| | 10/36566 [00:01<10:23:25, 1.02s/it] packing model.layers.11.mlp.gate: 0%| | 11/36566 [00:01<10:23:24, 1.02s/it] packing model.layers.12.mlp.gate: 0%| | 12/36566 [00:01<10:23:23, 1.02s/it] packing model.layers.13.mlp.gate: 0%| | 13/36566 [00:01<10:23:22, 1.02s/it] packing model.layers.14.mlp.gate: 0%| | 14/36566 [00:01<10:23:21, 1.02s/it] packing model.layers.15.mlp.gate: 0%| | 15/36566 [00:01<10:23:20, 1.02s/it] packing model.layers.16.mlp.gate: 0%| | 16/36566 [00:01<10:23:19, 1.02s/it] packing model.layers.17.mlp.gate: 0%| | 17/36566 [00:01<10:23:18, 1.02s/it] packing model.layers.18.mlp.gate: 0%| | 18/36566 [00:01<10:23:17, 1.02s/it] packing model.layers.19.mlp.gate: 0%| | 19/36566 [00:01<10:23:16, 1.02s/it] packing model.layers.20.mlp.gate: 0%| | 20/36566 [00:01<10:23:15, 1.02s/it] packing model.layers.21.mlp.gate: 0%| | 21/36566 [00:01<10:23:14, 1.02s/it] packing model.layers.22.mlp.gate: 0%| | 22/36566 [00:01<10:23:13, 1.02s/it] packing model.layers.23.mlp.gate: 0%| | 23/36566 [00:01<10:23:12, 1.02s/it] packing model.layers.24.mlp.gate: 0%| | 24/36566 [00:01<10:23:11, 1.02s/it] packing model.layers.25.mlp.gate: 0%| | 25/36566 [00:01<10:23:10, 1.02s/it] packing model.layers.26.mlp.gate: 0%| | 26/36566 [00:01<10:23:09, 1.02s/it] packing model.layers.27.mlp.gate: 0%| | 27/36566 [00:01<10:23:08, 1.02s/it] packing model.layers.28.mlp.gate: 0%| | 28/36566 [00:01<10:23:07, 1.02s/it] packing model.layers.29.mlp.gate: 0%| | 29/36566 [00:01<10:23:06, 1.02s/it] packing model.layers.30.mlp.gate: 0%| | 30/36566 [00:01<10:23:05, 1.02s/it] packing model.layers.31.mlp.gate: 0%| | 31/36566 [00:01<10:23:04, 1.02s/it] packing model.layers.32.mlp.gate: 0%| | 32/36566 [00:01<10:23:03, 1.02s/it] packing model.layers.33.mlp.gate: 0%| | 33/36566 [00:01<10:23:02, 1.02s/it] packing model.layers.34.mlp.gate: 0%| | 34/36566 [00:01<10:23:01, 1.02s/it] packing model.layers.35.mlp.gate: 0%| | 35/36566 [00:01<10:23:00, 1.02s/it] packing model.layers.36.mlp.gate: 0%| | 36/36566 [00:01<10:22:59, 1.02s/it] packing model.layers.37.mlp.gate: 0%| | 37/36566 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[00:01<10:22:31, 1.02s/it] packing model.layers.64.mlp.gate: 0%| | 64/36566 [00:01<10:22:30, 1.02s/it] packing model.layers.65.mlp.gate: 0%| | 65/36566 [00:01<10:22:29, 1.02s/it] packing model.layers.66.mlp.gate: 0%| | 66/36566 [00:01<10:22:28, 1.02s/it] packing model.layers.67.mlp.gate: 0%| | 67/36566 [00:01<10:22:27, 1.02s/it] packing model.layers.68.mlp.gate: 0%| | 68/36566 [00:01<10:22:26, 1.02s/it] packing model.layers.69.mlp.gate: 0%| | 69/36566 [00:01<10:22:25, 1.02s/it] packing model.layers.70.mlp.gate: 0%| | 70/36566 [00:01<10:22:24, 1.02s/it] packing model.layers.71.mlp.gate: 0%| | 71/36566 [00:01<10:22:23, 1.02s/it] packing model.layers.72.mlp.gate: 0%| | 72/36566 [00:01<10:22:22, 1.02s/it] packing model.layers.73.mlp.gate: 0%| | 73/36566 [00:01<10:22:21, 1.02s/it] packing model.layers.74.mlp.gate: 0%| | 74/36566 [00:01<10:22:20, 1.02s/it] packing model.layers.75.mlp.gate: 0%| | 75/36566 [00:01<10:22:19, 1.02s/it] packing model.layers.76.mlp.gate: 0%| | 76/36566 [00:01<10:22:18, 1.02s/it] packing model.layers.77.mlp.gate: 0%| | 77/36566 [00:01<10:22:17, 1.02s/it] packing model.layers.78.mlp.gate: 0%| | 78/36566 [00:01<10:22:16, 1.02s/it] packing model.layers.79.mlp.gate: 0%| | 79/36566 [00:01<10:22:15, 1.02s/it] packing model.layers.80.mlp.gate: 0%| | 80/36566 [00:01<10:22:14, 1.02s/it] packing model.layers.81.mlp.gate: 0%| | 81/36566 [00:01<10:22:13, 1.02s/it] packing model.layers.82.mlp.gate: 0%| | 82/36566 [00:01<10:22:12, 1.02s/it] packing model.layers.83.mlp.gate: 0%| | 83/36566 [00:01<10:22:11, 1.02s/it] packing model.layers.84.mlp.gate: 0%| | 84/36566 [00:01<10:22:10, 1.02s/it] packing model.layers.85.mlp.gate: 0%| | 85/36566 [00:01<10:22:09, 1.02s/it] packing model.layers.86.mlp.gate: 0%| | 86/36566 [00:01<10:22:07, 1.02s/it] packing model.layers.87.mlp.gate: 0%| | 87/36566 [00:01<10:22:06, 1.02s/it] packing model.layers.88.mlp.gate: 0%| | 88/36566 [00:01<10:22:05, 1.02s/it] packing model.layers.89.mlp.gate: 0%| | 89/36566 [00:01<10:22:04, 1.02s/it] packing model.layers.90.mlp.gate: 0%| | 90/36566 [00:01<10:22:03, 1.02s/it] packing model.layers.91.mlp.gate: 0%| | 91/36566 [00:01<10:22:02, 1.02s/it] packing model.layers.92.mlp.gate: 0%| | 92/36566 [00:01<10:22:01, 1.02s/it] packing model.layers.93.mlp.gate: 0%| | 93/36566 [00:01<10:22:00, 1.02s/it] packing model.layers.0.self_attn.k_proj: 0%| | 94/36566 [00:01<10:21:59, 1.02s/it] packing model.layers.1.self_attn.k_proj: 0%| | 95/36566 [00:01<10:21:58, 1.02s/it] packing model.layers.2.self_attn.k_proj: 0%| | 96/36566 [00:01<10:21:57, 1.02s/it] packing model.layers.3.self_attn.k_proj: 0%| | 97/36566 [00:01<10:21:56, 1.02s/it] packing model.layers.4.self_attn.k_proj: 0%| | 98/36566 [00:01<10:21:55, 1.02s/it] packing model.layers.5.self_attn.k_proj: 0%| | 99/36566 [00:01<10:21:54, 1.02s/it] packing model.layers.6.self_attn.k_proj: 0%| | 100/36566 [00:01<10:21:53, 1.02s/it] packing model.layers.7.self_attn.k_proj: 0%| | 101/36566 [00:01<10:21:52, 1.02s/it] packing model.layers.8.self_attn.k_proj: 0%| | 102/36566 [00:01<10:21:51, 1.02s/it] packing model.layers.9.self_attn.k_proj: 0%| | 103/36566 [00:01<10:21:50, 1.02s/it] packing model.layers.10.self_attn.k_proj: 0%| | 104/36566 [00:01<10:21:49, 1.02s/it] packing model.layers.11.self_attn.k_proj: 0%| | 105/36566 [00:01<10:21:48, 1.02s/it] packing model.layers.12.self_attn.k_proj: 0%| | 106/36566 [00:01<10:21:47, 1.02s/it] packing model.layers.13.self_attn.k_proj: 0%| | 107/36566 [00:01<10:21:46, 1.02s/it] packing model.layers.14.self_attn.k_proj: 0%| | 108/36566 [00:01<10:21:45, 1.02s/it] packing model.layers.15.self_attn.k_proj: 0%| | 109/36566 [00:01<10:21:44, 1.02s/it] packing model.layers.16.self_attn.k_proj: 0%| | 110/36566 [00:01<10:21:43, 1.02s/it] packing model.layers.17.self_attn.k_proj: 0%| | 111/36566 [00:01<10:21:42, 1.02s/it] packing model.layers.18.self_attn.k_proj: 0%| | 112/36566 [00:01<10:21:41, 1.02s/it] packing model.layers.19.self_attn.k_proj: 0%| | 113/36566 [00:01<10:21:40, 1.02s/it] packing model.layers.19.self_attn.k_proj: 0%| | 114/36566 [00:01<04:21, 139.19it/s] packing model.layers.20.self_attn.k_proj: 0%| | 114/36566 [00:01<04:21, 139.19it/s] packing model.layers.21.self_attn.k_proj: 0%| | 115/36566 [00:01<04:21, 139.19it/s] packing model.layers.22.self_attn.k_proj: 0%| | 116/36566 [00:01<04:21, 139.19it/s] packing model.layers.23.self_attn.k_proj: 0%| | 117/36566 [00:01<04:21, 139.19it/s] packing model.layers.24.self_attn.k_proj: 0%| | 118/36566 [00:01<04:21, 139.19it/s] packing model.layers.25.self_attn.k_proj: 0%| | 119/36566 [00:01<04:21, 139.19it/s] packing model.layers.26.self_attn.k_proj: 0%| | 120/36566 [00:01<04:21, 139.19it/s] packing model.layers.27.self_attn.k_proj: 0%| | 121/36566 [00:01<04:21, 139.19it/s] packing model.layers.28.self_attn.k_proj: 0%| | 122/36566 [00:01<04:21, 139.19it/s] packing model.layers.29.self_attn.k_proj: 0%| | 123/36566 [00:01<04:21, 139.19it/s] packing model.layers.30.self_attn.k_proj: 0%| | 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model.layers.42.self_attn.k_proj: 0%| | 136/36566 [00:01<04:21, 139.19it/s] packing model.layers.43.self_attn.k_proj: 0%| | 137/36566 [00:01<04:21, 139.19it/s] packing model.layers.44.self_attn.k_proj: 0%| | 138/36566 [00:01<04:21, 139.19it/s] packing model.layers.45.self_attn.k_proj: 0%| | 139/36566 [00:01<04:21, 139.19it/s] packing model.layers.46.self_attn.k_proj: 0%| | 140/36566 [00:01<04:21, 139.19it/s] packing model.layers.47.self_attn.k_proj: 0%| | 141/36566 [00:01<04:21, 139.19it/s] packing model.layers.48.self_attn.k_proj: 0%| | 142/36566 [00:01<04:21, 139.19it/s] packing model.layers.49.self_attn.k_proj: 0%| | 143/36566 [00:01<04:21, 139.19it/s] packing model.layers.50.self_attn.k_proj: 0%| | 144/36566 [00:01<04:21, 139.19it/s] packing model.layers.51.self_attn.k_proj: 0%| | 145/36566 [00:01<04:21, 139.19it/s] packing model.layers.52.self_attn.k_proj: 0%| | 146/36566 [00:01<04:21, 139.19it/s] packing model.layers.53.self_attn.k_proj: 0%| | 147/36566 [00:01<04:21, 139.19it/s] 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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Checking if ChaiML/bol-opusd-v7-lr5e6ep2r64g4b02-W4A16 already exists in ChaiML
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Creating repo ChaiML/bol-opusd-v7-lr5e6ep2r64g4b02-W4A16 and uploading /dev/shm/model_output to it
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Recovering from metadata files: 0%| | 0/38 [00:00<?, ?it/s] Recovering from metadata files: 100%|██████████| 38/38 [00:00<00:00, 8323.77it/s]
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: ---------- 2026-02-04 22:50:01 (0:00:00) ----------
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Files: hashed 11/38 (26.1M/131.9G) | pre-uploaded: 0/1 (0.0/131.9G) (+27 unsure) | committed: 0/38 (0.0/131.9G) | ignored: 0
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Workers: hashing: 27 | get upload mode: 0 | pre-uploading: 1 | committing: 0 | waiting: 98
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: ---------------------------------------------------
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: Processing Files (0 / 0) : | | 0.00B / 0.00B
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Unable to record family friendly update due to error: Invalid JSON input: Expecting value: line 1 column 1 (char 0)
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chaiml-bol-opusd-v7-lr5-65112-v1-uploader: ...0011-of-00027.safetensors: 100%|█████████▉| 4.97GB / 4.98GB 
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: cp /dev/shm/model_output/model-00015-of-00027.safetensors s3://guanaco-vllm-models/chaiml-bol-opusd-v7-lr5-65112-v1/model-00015-of-00027.safetensors
chaiml-bol-opusd-v7-lr5-65112-v1-uploader: cp /dev/shm/model_output/model-00001-of-00027.safetensors s3://guanaco-vllm-models/chaiml-bol-opusd-v7-lr5-65112-v1/model-00001-of-00027.safetensors
Unable to record family friendly update due to error: Invalid JSON input: Expecting value: line 1 column 1 (char 0)
Job chaiml-bol-opusd-v7-lr5-65112-v1-uploader completed after 5579.43s with status: succeeded
Stopping job with name chaiml-bol-opusd-v7-lr5-65112-v1-uploader
Pipeline stage VLLMUploader completed in 5581.97s
run pipeline stage %s
Running pipeline stage VLLMTemplater
Pipeline stage VLLMTemplater completed in 0.90s
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Running pipeline stage VLLMDeployer
Creating inference service chaiml-bol-opusd-v7-lr5-65112-v1
Waiting for inference service chaiml-bol-opusd-v7-lr5-65112-v1 to be ready
HTTP Request: %s %s "%s %d %s"
Inference service chaiml-bol-opusd-v7-lr5-65112-v1 ready after 451.7899754047394s
Pipeline stage VLLMDeployer completed in 454.47s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.2095015048980713s
Received healthy response to inference request in 2.2471425533294678s
Received healthy response to inference request in 2.18780517578125s
Received healthy response to inference request in 2.3653221130371094s
Received healthy response to inference request in 2.1759121417999268s
Received healthy response to inference request in 2.201721429824829s
Received healthy response to inference request in 2.4378862380981445s
Received healthy response to inference request in 2.065321445465088s
Received healthy response to inference request in 2.3272945880889893s
Received healthy response to inference request in 2.1463143825531006s
Received healthy response to inference request in 2.3831405639648438s
Received healthy response to inference request in 2.7056713104248047s
Received healthy response to inference request in 2.562537431716919s
Received healthy response to inference request in 2.321882724761963s
Received healthy response to inference request in 2.704110860824585s
Received healthy response to inference request in 2.1077191829681396s
Received healthy response to inference request in 2.7779452800750732s
Received healthy response to inference request in 2.9124975204467773s
Received healthy response to inference request in 2.757107973098755s
Received healthy response to inference request in 2.5156538486480713s
Received healthy response to inference request in 2.3047118186950684s
Received healthy response to inference request in 2.086322784423828s
Received healthy response to inference request in 2.488062858581543s
Received healthy response to inference request in 2.238589286804199s
Received healthy response to inference request in 2.206382989883423s
Received healthy response to inference request in 2.4485714435577393s
Received healthy response to inference request in 2.308953285217285s
Received healthy response to inference request in 2.892205238342285s
Received healthy response to inference request in 2.3222858905792236s
Received healthy response to inference request in 2.6595468521118164s
30 requests
0 failed requests
5th percentile: 2.095951163768768
10th percentile: 2.1424548625946045
20th percentile: 2.198938179016113
30th percentile: 2.2298629522323608
40th percentile: 2.3072566986083984
50th percentile: 2.3247902393341064
60th percentile: 2.405038833618164
70th percentile: 2.4963401556015015
80th percentile: 2.66845965385437
90th percentile: 2.759191703796387
95th percentile: 2.8407882571220395
99th percentile: 2.9066127586364745
mean time: 2.4022706906000773
Pipeline stage StressChecker completed in 97.49s
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Running pipeline stage OfflineFamilyFriendlyTriggerPipeline
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starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage OfflineFamilyFriendlyTriggerPipeline completed in 2.55s
Shutdown handler de-registered
chaiml-bol-opusd-v7-lr5_65112_v1 status is now deployed due to DeploymentManager action
chaiml-bol-opusd-v7-lr5_65112_v1 status is now inactive due to auto deactivation removed underperforming models
chaiml-bol-opusd-v7-lr5_65112_v1 status is now torndown due to DeploymentManager action