developer_uid: RandomForest1024
submission_id: albertwang8192-2025-07-10-4_v1
model_name: 2025-07-10_4
model_group: AlbertWang8192/2025-07-1
status: torndown
timestamp: 2025-07-11T02:53:54+00:00
num_battles: 7835
num_wins: 3747
celo_rating: 1266.42
family_friendly_score: 0.5898
family_friendly_standard_error: 0.006956090281185258
submission_type: basic
model_repo: AlbertWang8192/2025-07-10_4
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': 0.5833761766973166, 'latency_mean': 1.7139709091186524, 'latency_p50': 1.7300487756729126, 'latency_p90': 1.885734224319458}, {'batch_size': 3, 'throughput': 1.0478711939758978, 'latency_mean': 2.8494982147216796, 'latency_p50': 2.8399107456207275, 'latency_p90': 3.088722825050354}, {'batch_size': 5, 'throughput': 1.2426054926410735, 'latency_mean': 4.003643107414246, 'latency_p50': 4.002238035202026, 'latency_p90': 4.463310360908508}, {'batch_size': 6, 'throughput': 1.3093912849639635, 'latency_mean': 4.559229651689529, 'latency_p50': 4.581102967262268, 'latency_p90': 5.027094483375549}, {'batch_size': 8, 'throughput': 1.3715596650530426, 'latency_mean': 5.800987124443054, 'latency_p50': 5.863191246986389, 'latency_p90': 6.484490299224854}, {'batch_size': 10, 'throughput': 1.3772009817319388, 'latency_mean': 7.199929378032684, 'latency_p50': 7.26864767074585, 'latency_p90': 8.171605348587036}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: 2025-07-10_4
is_internal_developer: False
language_model: AlbertWang8192/2025-07-10_4
model_size: 13B
ranking_group: single
throughput_3p7s: 1.21
us_pacific_date: 2025-07-10
win_ratio: 0.4782386726228462
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': ['<|im_end|>', '\n', '<|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 albertwang8192-2025-07-10-4-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-10-4-v1-mkmlizer to finish
albertwang8192-2025-07-10-4-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-10-4-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║   ██████   ██████  █████   ████  ████                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  ░░██████ ██████  ░░███   ███░  ░░███                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║   ░███░█████░███   ░███  ███     ░███                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║   ░███░░███ ░███   ░███████      ░███                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║   ░███ ░░░  ░███   ░███░░███     ░███                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║   ░███      ░███   ░███ ░░███    ░███                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║   █████     █████  █████ ░░████  █████                              ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-4-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-10-4-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-10-4-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-10-4-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-10-4-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-10-4-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-10-4-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-10-4-v1-mkmlizer: Downloaded to shared memory in 45.309s
albertwang8192-2025-07-10-4-v1-mkmlizer: Checking if AlbertWang8192/2025-07-10_4 already exists in ChaiML
albertwang8192-2025-07-10-4-v1-mkmlizer: Creating repo ChaiML/2025-07-10_4 and uploading /tmp/tmpfk7bxm89 to it
albertwang8192-2025-07-10-4-v1-mkmlizer: 
  0%|          | 0/6 [00:00<?, ?it/s]
 17%|█▋        | 1/6 [00:08<00:44,  8.89s/it]
 33%|███▎      | 2/6 [00:13<00:24,  6.10s/it]
 50%|█████     | 3/6 [00:18<00:16,  5.58s/it]
 67%|██████▋   | 4/6 [00:24<00:12,  6.12s/it]
 83%|████████▎ | 5/6 [00:32<00:06,  6.66s/it]
100%|██████████| 6/6 [00:33<00:00,  4.80s/it]
100%|██████████| 6/6 [00:33<00:00,  5.63s/it]
albertwang8192-2025-07-10-4-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpfk7bxm89, device:0
albertwang8192-2025-07-10-4-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-10-4-v1-mkmlizer: quantized model in 30.618s
albertwang8192-2025-07-10-4-v1-mkmlizer: Processed model AlbertWang8192/2025-07-10_4 in 134.915s
albertwang8192-2025-07-10-4-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-10-4-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-10-4-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-10-4-v1/nvidia
albertwang8192-2025-07-10-4-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-4-v1/nvidia/config.json
albertwang8192-2025-07-10-4-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-4-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-10-4-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-4-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-10-4-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-4-v1/nvidia/tokenizer.json
albertwang8192-2025-07-10-4-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-10-4-v1/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-10-4-v1-mkmlizer: 
Loading 0:   0%|          | 0/363 [00:00<?, ?it/s]
Loading 0:   1%|▏         | 5/363 [00:00<00:13, 27.25it/s]
Loading 0:   3%|▎         | 12/363 [00:00<00:08, 43.75it/s]
Loading 0:   5%|▍         | 17/363 [00:00<00:08, 42.65it/s]
Loading 0:   6%|▌         | 22/363 [00:00<00:08, 42.56it/s]
Loading 0:   7%|▋         | 27/363 [00:00<00:07, 44.28it/s]
Loading 0:   9%|▉         | 32/363 [00:00<00:09, 36.22it/s]
Loading 0:  11%|█         | 39/363 [00:00<00:07, 44.42it/s]
Loading 0:  12%|█▏        | 44/363 [00:01<00:07, 43.59it/s]
Loading 0:  13%|█▎        | 49/363 [00:01<00:07, 43.68it/s]
Loading 0:  15%|█▍        | 54/363 [00:01<00:06, 45.17it/s]
Loading 0:  17%|█▋        | 60/363 [00:01<00:07, 41.86it/s]
Loading 0:  18%|█▊        | 65/363 [00:01<00:10, 27.35it/s]
Loading 0:  20%|█▉        | 71/363 [00:01<00:08, 33.10it/s]
Loading 0:  21%|██        | 76/363 [00:01<00:08, 35.09it/s]
Loading 0:  22%|██▏       | 81/363 [00:02<00:07, 36.86it/s]
Loading 0:  24%|██▎       | 86/363 [00:02<00:07, 39.24it/s]
Loading 0:  25%|██▌       | 91/363 [00:02<00:08, 33.98it/s]
Loading 0:  27%|██▋       | 98/363 [00:02<00:06, 40.17it/s]
Loading 0:  28%|██▊       | 103/363 [00:02<00:06, 40.64it/s]
Loading 0:  30%|███       | 109/363 [00:02<00:05, 44.77it/s]
Loading 0:  31%|███▏      | 114/363 [00:02<00:06, 38.12it/s]
Loading 0:  33%|███▎      | 119/363 [00:03<00:06, 38.32it/s]
Loading 0:  34%|███▍      | 125/363 [00:03<00:05, 42.54it/s]
Loading 0:  36%|███▌      | 130/363 [00:03<00:05, 42.57it/s]
Loading 0:  37%|███▋      | 135/363 [00:03<00:05, 42.55it/s]
Loading 0:  39%|███▊      | 140/363 [00:03<00:05, 43.28it/s]
Loading 0:  40%|███▉      | 145/363 [00:03<00:08, 27.07it/s]
Loading 0:  41%|████      | 149/363 [00:04<00:07, 27.79it/s]
Loading 0:  43%|████▎     | 156/363 [00:04<00:05, 35.21it/s]
Loading 0:  44%|████▍     | 161/363 [00:04<00:05, 37.12it/s]
Loading 0:  46%|████▌     | 166/363 [00:04<00:05, 38.83it/s]
Loading 0:  47%|████▋     | 171/363 [00:04<00:04, 41.31it/s]
Loading 0:  48%|████▊     | 176/363 [00:04<00:05, 35.05it/s]
Loading 0:  50%|█████     | 183/363 [00:04<00:04, 41.87it/s]
Loading 0:  52%|█████▏    | 188/363 [00:04<00:04, 42.12it/s]
Loading 0:  53%|█████▎    | 193/363 [00:05<00:04, 41.16it/s]
Loading 0:  55%|█████▍    | 198/363 [00:05<00:03, 42.97it/s]
Loading 0:  56%|█████▌    | 203/363 [00:05<00:04, 35.47it/s]
Loading 0:  58%|█████▊    | 210/363 [00:05<00:03, 41.84it/s]
Loading 0:  59%|█████▉    | 215/363 [00:05<00:03, 40.66it/s]
Loading 0:  61%|██████    | 220/363 [00:05<00:03, 41.57it/s]
Loading 0:  62%|██████▏   | 225/363 [00:06<00:05, 26.14it/s]
Loading 0:  63%|██████▎   | 230/363 [00:06<00:04, 28.84it/s]
Loading 0:  65%|██████▌   | 237/363 [00:06<00:03, 35.63it/s]
Loading 0:  67%|██████▋   | 242/363 [00:06<00:03, 37.19it/s]
Loading 0:  68%|██████▊   | 247/363 [00:06<00:02, 38.72it/s]
Loading 0:  69%|██████▉   | 252/363 [00:06<00:02, 41.17it/s]
Loading 0:  71%|███████   | 257/363 [00:06<00:03, 34.95it/s]
Loading 0:  73%|███████▎  | 264/363 [00:06<00:02, 41.86it/s]
Loading 0:  74%|███████▍  | 269/363 [00:07<00:02, 42.06it/s]
Loading 0:  75%|███████▌  | 274/363 [00:07<00:02, 42.09it/s]
Loading 0:  77%|███████▋  | 279/363 [00:07<00:01, 43.48it/s]
Loading 0:  78%|███████▊  | 284/363 [00:07<00:02, 36.04it/s]
Loading 0:  80%|████████  | 291/363 [00:07<00:01, 42.30it/s]
Loading 0:  82%|████████▏ | 296/363 [00:07<00:01, 41.40it/s]
Loading 0:  83%|████████▎ | 301/363 [00:07<00:01, 43.16it/s]
Loading 0:  84%|████████▍ | 306/363 [00:08<00:02, 23.10it/s]
Loading 0:  85%|████████▌ | 310/363 [00:08<00:02, 24.46it/s]
Loading 0:  87%|████████▋ | 314/363 [00:08<00:01, 26.96it/s]
Loading 0:  88%|████████▊ | 320/363 [00:08<00:01, 32.55it/s]
Loading 0:  90%|████████▉ | 326/363 [00:08<00:01, 34.65it/s]
Loading 0:  91%|█████████ | 330/363 [00:08<00:00, 34.25it/s]
Loading 0:  93%|█████████▎| 338/363 [00:09<00:00, 42.96it/s]
Loading 0:  95%|█████████▍| 344/363 [00:09<00:00, 41.24it/s]
Loading 0:  96%|█████████▌| 349/363 [00:09<00:00, 41.46it/s]
Loading 0:  98%|█████████▊| 355/363 [00:09<00:00, 45.55it/s]
Loading 0:  99%|█████████▉| 360/363 [00:09<00:00, 44.80it/s]
                                                            

Job albertwang8192-2025-07-10-4-v1-mkmlizer completed after 158.16s with status: succeeded
Stopping job with name albertwang8192-2025-07-10-4-v1-mkmlizer
Pipeline stage MKMLizer completed in 158.74s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.16s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-10-4-v1
Waiting for inference service albertwang8192-2025-07-10-4-v1 to be ready
Inference service albertwang8192-2025-07-10-4-v1 ready after 200.843501329422s
Pipeline stage MKMLDeployer completed in 201.32s
run pipeline stage %s
Running pipeline stage StressChecker
HTTPConnectionPool(host='guanaco-submitter.guanaco-backend.k2.chaiverse.com', port=80): Read timed out. (read timeout=20)
Received unhealthy response to inference request!
Received healthy response to inference request in 2.0006744861602783s
Received healthy response to inference request in 1.4775822162628174s
Received healthy response to inference request in 1.7811191082000732s
Received healthy response to inference request in 2.0070767402648926s
5 requests
1 failed requests
5th percentile: 1.5382895946502686
10th percentile: 1.5989969730377198
20th percentile: 1.720411729812622
30th percentile: 1.8250301837921143
40th percentile: 1.9128523349761963
50th percentile: 2.0006744861602783
60th percentile: 2.003235387802124
70th percentile: 2.00579628944397
80th percentile: 5.634261846542362
90th percentile: 12.888632059097292
95th percentile: 16.515817165374752
99th percentile: 19.417565250396727
mean time: 5.4818909645080565
%s, retrying in %s seconds...
Received healthy response to inference request in 1.5612783432006836s
Received healthy response to inference request in 1.4361228942871094s
Received healthy response to inference request in 1.8270866870880127s
Received healthy response to inference request in 1.5827198028564453s
Received healthy response to inference request in 1.2175343036651611s
5 requests
0 failed requests
5th percentile: 1.2612520217895509
10th percentile: 1.3049697399139404
20th percentile: 1.3924051761627196
30th percentile: 1.4611539840698242
40th percentile: 1.511216163635254
50th percentile: 1.5612783432006836
60th percentile: 1.5698549270629882
70th percentile: 1.5784315109252929
80th percentile: 1.6315931797027587
90th percentile: 1.7293399333953858
95th percentile: 1.7782133102416993
99th percentile: 1.81731201171875
mean time: 1.5249484062194825
Pipeline stage StressChecker completed in 38.13s
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 1.02s
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 0.78s
Shutdown handler de-registered
albertwang8192-2025-07-10-4_v1 status is now deployed due to DeploymentManager action
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
Pipeline stage OfflineFamilyFriendlyScorer completed in 4748.44s
Shutdown handler de-registered
albertwang8192-2025-07-10-4_v1 status is now inactive due to auto deactivation removed underperforming models
albertwang8192-2025-07-10-4_v1 status is now torndown due to DeploymentManager action