albertwang8192-2025-07-05-5

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-05-5_v1

model_name: 2025-07-05_5

model_group: AlbertWang8192/2025-07-0

status: torndown

timestamp: 2025-07-06T01:32:27+00:00

num_battles: 7254

num_wins: 3275

celo_rating: 1253.57

family_friendly_score: 0.5998

family_friendly_standard_error: 0.006928779979188255

submission_type: basic

model_repo: AlbertWang8192/2025-07-05_5

model_architecture: MistralForCausalLM

model_num_parameters: 12772070400.0

best_of: 8

max_input_tokens: 1024

max_output_tokens: 64

reward_model: default

latencies: [{'batch_size': 1, 'throughput': 0.5970525860933799, 'latency_mean': 1.6747920763492585, 'latency_p50': 1.6751127243041992, 'latency_p90': 1.8504797697067261}, {'batch_size': 3, 'throughput': 1.0892359173662527, 'latency_mean': 2.7456427466869355, 'latency_p50': 2.7261911630630493, 'latency_p90': 3.0193012952804565}, {'batch_size': 5, 'throughput': 1.300189176678316, 'latency_mean': 3.82983650803566, 'latency_p50': 3.8408533334732056, 'latency_p90': 4.20578281879425}, {'batch_size': 6, 'throughput': 1.3529198009079917, 'latency_mean': 4.402896904945374, 'latency_p50': 4.425122380256653, 'latency_p90': 4.841753339767456}, {'batch_size': 8, 'throughput': 1.4281786398107774, 'latency_mean': 5.5687548959255215, 'latency_p50': 5.53717577457428, 'latency_p90': 6.2584165096282955}, {'batch_size': 10, 'throughput': 1.4599256745283622, 'latency_mean': 6.79446322798729, 'latency_p50': 6.762546420097351, 'latency_p90': 7.681176090240478}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-05_5

is_internal_developer: False

language_model: AlbertWang8192/2025-07-05_5

model_size: 13B

ranking_group: single

throughput_3p7s: 1.29

us_pacific_date: 2025-07-05

win_ratio: 0.4514750482492418

generation_params: {'temperature': 0.6, 'top_p': 0.95, 'min_p': 0.025, 'top_k': 60, 'presence_penalty': 0.4, 'frequency_penalty': 0.4, 'stopping_words': ['\n', '<|im_end|>', '<|im_start|>'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}

formatter: {'memory_template': "{bot_name}'s Persona: {memory}\n####\n", 'prompt_template': '{prompt}\n<START>\n', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '{bot_name}:', 'truncate_by_message': False}

Resubmit model

Shutdown handler not registered because Python interpreter is not running in the main thread
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLizer
Starting job with name albertwang8192-2025-07-05-5-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-05-5-v1-mkmlizer to finish
albertwang8192-2025-07-05-5-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-05-5-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║   ██████   ██████  █████   ████  ████                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  ░░██████ ██████  ░░███   ███░  ░░███                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║   ░███░█████░███   ░███  ███     ░███                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║   ░███░░███ ░███   ░███████      ░███                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║   ░███ ░░░  ░███   ░███░░███     ░███                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║   ░███      ░███   ░███ ░░███    ░███                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║   █████     █████  █████ ░░████  █████                              ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-05-5-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-05-5-v1-mkmlizer: Downloaded to shared memory in 96.990s
albertwang8192-2025-07-05-5-v1-mkmlizer: Checking if AlbertWang8192/2025-07-05_5 already exists in ChaiML
albertwang8192-2025-07-05-5-v1-mkmlizer: Creating repo ChaiML/2025-07-05_5 and uploading /tmp/tmpda1op23c to it
albertwang8192-2025-07-05-5-v1-mkmlizer: 
  0%|          | 0/11 [00:00<?, ?it/s]
  9%|▉         | 1/11 [00:05<00:58,  5.86s/it]
 18%|█▊        | 2/11 [00:09<00:41,  4.57s/it]
 27%|██▋       | 3/11 [00:13<00:33,  4.17s/it]
 36%|███▋      | 4/11 [00:20<00:36,  5.27s/it]
 45%|████▌     | 5/11 [00:24<00:30,  5.03s/it]
 55%|█████▍    | 6/11 [00:28<00:23,  4.64s/it]
 64%|██████▎   | 7/11 [00:32<00:17,  4.35s/it]
 73%|███████▎  | 8/11 [00:39<00:15,  5.22s/it]
 82%|████████▏ | 9/11 [00:46<00:11,  5.74s/it]
 91%|█████████ | 10/11 [00:53<00:06,  6.07s/it]
100%|██████████| 11/11 [00:54<00:00,  4.55s/it]
100%|██████████| 11/11 [00:54<00:00,  4.93s/it]
albertwang8192-2025-07-05-5-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpda1op23c, device:0
albertwang8192-2025-07-05-5-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-05-5-v1-mkmlizer: quantized model in 38.985s
albertwang8192-2025-07-05-5-v1-mkmlizer: Processed model AlbertWang8192/2025-07-05_5 in 239.151s
albertwang8192-2025-07-05-5-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-05-5-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-05-5-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-05-5-v1/nvidia
albertwang8192-2025-07-05-5-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-05-5-v1/nvidia/config.json
albertwang8192-2025-07-05-5-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-05-5-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-05-5-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-05-5-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-05-5-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-05-5-v1/nvidia/tokenizer.json
albertwang8192-2025-07-05-5-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-05-5-v1/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-05-5-v1-mkmlizer: 
Loading 0:   0%|          | 0/363 [00:00<?, ?it/s]
Loading 0:   1%|▏         | 5/363 [00:00<00:16, 22.05it/s]
Loading 0:   3%|▎         | 10/363 [00:00<00:12, 27.72it/s]
Loading 0:   4%|▍         | 14/363 [00:00<00:14, 24.25it/s]
Loading 0:   5%|▌         | 19/363 [00:00<00:11, 30.45it/s]
Loading 0:   6%|▋         | 23/363 [00:00<00:15, 22.46it/s]
Loading 0:   7%|▋         | 26/363 [00:01<00:16, 20.00it/s]
Loading 0:   9%|▊         | 31/363 [00:01<00:12, 25.62it/s]
Loading 0:  10%|▉         | 35/363 [00:01<00:12, 26.81it/s]
Loading 0:  11%|█         | 39/363 [00:01<00:11, 27.75it/s]
Loading 0:  12%|█▏        | 43/363 [00:01<00:11, 26.90it/s]
Loading 0:  13%|█▎        | 48/363 [00:01<00:10, 29.16it/s]
Loading 0:  14%|█▍        | 52/363 [00:01<00:11, 27.32it/s]
Loading 0:  15%|█▌        | 56/363 [00:02<00:11, 27.24it/s]
Loading 0:  17%|█▋        | 60/363 [00:02<00:10, 29.81it/s]
Loading 0:  18%|█▊        | 64/363 [00:02<00:15, 19.05it/s]
Loading 0:  19%|█▉        | 69/363 [00:02<00:12, 23.94it/s]
Loading 0:  20%|██        | 73/363 [00:02<00:11, 24.34it/s]
Loading 0:  21%|██        | 77/363 [00:03<00:12, 22.58it/s]
Loading 0:  23%|██▎       | 82/363 [00:03<00:10, 27.34it/s]
Loading 0:  24%|██▎       | 86/363 [00:03<00:11, 24.64it/s]
Loading 0:  25%|██▌       | 91/363 [00:03<00:09, 29.10it/s]
Loading 0:  26%|██▌       | 95/363 [00:03<00:10, 25.19it/s]
Loading 0:  28%|██▊       | 100/363 [00:03<00:09, 29.22it/s]
Loading 0:  29%|██▊       | 104/363 [00:04<00:13, 18.99it/s]
Loading 0:  30%|███       | 109/363 [00:04<00:10, 23.54it/s]
Loading 0:  31%|███       | 113/363 [00:04<00:11, 22.13it/s]
Loading 0:  33%|███▎      | 118/363 [00:04<00:09, 26.85it/s]
Loading 0:  34%|███▎      | 122/363 [00:04<00:09, 24.28it/s]
Loading 0:  35%|███▍      | 127/363 [00:05<00:08, 28.98it/s]
Loading 0:  36%|███▌      | 131/363 [00:05<00:09, 25.17it/s]
Loading 0:  37%|███▋      | 136/363 [00:05<00:07, 29.43it/s]
Loading 0:  39%|███▉      | 141/363 [00:05<00:07, 30.57it/s]
Loading 0:  40%|███▉      | 145/363 [00:05<00:10, 21.39it/s]
Loading 0:  41%|████      | 149/363 [00:06<00:10, 21.00it/s]
Loading 0:  42%|████▏     | 154/363 [00:06<00:08, 25.76it/s]
Loading 0:  44%|████▎     | 158/363 [00:06<00:08, 23.77it/s]
Loading 0:  45%|████▍     | 163/363 [00:06<00:06, 28.60it/s]
Loading 0:  46%|████▌     | 167/363 [00:06<00:07, 25.30it/s]
Loading 0:  47%|████▋     | 172/363 [00:06<00:06, 29.93it/s]
Loading 0:  48%|████▊     | 176/363 [00:06<00:07, 26.20it/s]
Loading 0:  50%|████▉     | 181/363 [00:07<00:05, 30.82it/s]
Loading 0:  51%|█████     | 185/363 [00:07<00:08, 20.08it/s]
Loading 0:  52%|█████▏    | 190/363 [00:07<00:06, 24.87it/s]
Loading 0:  53%|█████▎    | 194/363 [00:07<00:07, 23.17it/s]
Loading 0:  55%|█████▍    | 199/363 [00:07<00:05, 28.00it/s]
Loading 0:  56%|█████▌    | 203/363 [00:08<00:06, 24.79it/s]
Loading 0:  57%|█████▋    | 208/363 [00:08<00:05, 29.34it/s]
Loading 0:  58%|█████▊    | 212/363 [00:08<00:05, 25.98it/s]
Loading 0:  60%|█████▉    | 217/363 [00:08<00:04, 30.73it/s]
Loading 0:  61%|██████    | 222/363 [00:08<00:04, 31.17it/s]
Loading 0:  62%|██████▏   | 226/363 [00:08<00:06, 21.71it/s]
Loading 0:  63%|██████▎   | 230/363 [00:09<00:06, 21.02it/s]
Loading 0:  65%|██████▍   | 235/363 [00:09<00:04, 25.83it/s]
Loading 0:  66%|██████▌   | 239/363 [00:09<00:05, 23.79it/s]
Loading 0:  68%|██████▊   | 246/363 [00:09<00:03, 29.79it/s]
Loading 0:  69%|██████▉   | 250/363 [00:09<00:04, 27.62it/s]
Loading 0:  70%|██████▉   | 254/363 [00:09<00:03, 29.81it/s]
Loading 0:  71%|███████   | 258/363 [00:10<00:04, 25.38it/s]
Loading 0:  72%|███████▏  | 262/363 [00:10<00:03, 28.09it/s]
Loading 0:  73%|███████▎  | 266/363 [00:10<00:05, 18.80it/s]
Loading 0:  75%|███████▍  | 271/363 [00:10<00:03, 23.54it/s]
Loading 0:  76%|███████▌  | 275/363 [00:10<00:03, 22.10it/s]
Loading 0:  77%|███████▋  | 280/363 [00:11<00:03, 27.12it/s]
Loading 0:  78%|███████▊  | 284/363 [00:11<00:03, 24.43it/s]
Loading 0:  80%|███████▉  | 289/363 [00:11<00:02, 29.18it/s]
Loading 0:  81%|████████  | 293/363 [00:11<00:02, 25.79it/s]
Loading 0:  82%|████████▏ | 299/363 [00:11<00:02, 29.76it/s]
Loading 0:  83%|████████▎ | 303/363 [00:11<00:01, 31.81it/s]
Loading 0:  85%|████████▍ | 307/363 [00:12<00:02, 21.83it/s]
Loading 0:  86%|████████▌ | 311/363 [00:12<00:02, 21.22it/s]
Loading 0:  88%|████████▊ | 318/363 [00:12<00:01, 27.38it/s]
Loading 0:  89%|████████▊ | 322/363 [00:12<00:01, 26.39it/s]
Loading 0:  90%|████████▉ | 325/363 [00:12<00:01, 26.84it/s]
Loading 0:  91%|█████████ | 329/363 [00:12<00:01, 24.10it/s]
Loading 0:  92%|█████████▏| 334/363 [00:13<00:01, 28.74it/s]
Loading 0:  93%|█████████▎| 338/363 [00:13<00:00, 25.36it/s]
Loading 0:  94%|█████████▍| 343/363 [00:13<00:00, 30.21it/s]
Loading 0:  96%|█████████▌| 347/363 [00:14<00:01, 14.63it/s]
Loading 0:  96%|█████████▋| 350/363 [00:14<00:00, 16.05it/s]
Loading 0:  97%|█████████▋| 353/363 [00:14<00:00, 17.06it/s]
Loading 0:  98%|█████████▊| 357/363 [00:14<00:00, 17.94it/s]
                                                            
Job albertwang8192-2025-07-05-5-v1-mkmlizer completed after 259.91s with status: succeeded
Stopping job with name albertwang8192-2025-07-05-5-v1-mkmlizer
Pipeline stage MKMLizer completed in 260.44s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.18s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-05-5-v1
Waiting for inference service albertwang8192-2025-07-05-5-v1 to be ready
Inference service albertwang8192-2025-07-05-5-v1 ready after 221.31756830215454s
Pipeline stage MKMLDeployer completed in 221.94s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.51843523979187s
Received healthy response to inference request in 1.674586296081543s
Received healthy response to inference request in 2.0921878814697266s
Received healthy response to inference request in 1.3955767154693604s
Received healthy response to inference request in 1.546523094177246s
5 requests
0 failed requests
5th percentile: 1.4257659912109375
10th percentile: 1.4559552669525146
20th percentile: 1.516333818435669
30th percentile: 1.5721357345581055
40th percentile: 1.6233610153198241
50th percentile: 1.674586296081543
60th percentile: 1.8416269302368165
70th percentile: 2.00866756439209
80th percentile: 2.1774373531341555
90th percentile: 2.3479362964630126
95th percentile: 2.433185768127441
99th percentile: 2.5013853454589845
mean time: 1.8454618453979492
Pipeline stage StressChecker completed in 11.02s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyTriggerPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage OfflineFamilyFriendlyTriggerPipeline completed in 0.73s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage TriggerMKMLProfilingPipeline completed in 0.73s
Shutdown handler de-registered
albertwang8192-2025-07-05-5_v1 status is now deployed due to DeploymentManager action
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.11s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.10s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service albertwang8192-2025-07-05-5-v1-profiler
Waiting for inference service albertwang8192-2025-07-05-5-v1-profiler to be ready
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
Pipeline stage OfflineFamilyFriendlyScorer completed in 3182.13s
Shutdown handler de-registered
albertwang8192-2025-07-05-5_v1 status is now inactive due to auto deactivation removed underperforming models
albertwang8192-2025-07-05-5_v1 status is now torndown due to DeploymentManager action