albertwang8192-2025-07-11-3

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-3_v1

model_name: 2025-07-11_3

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-11T17:42:32+00:00

num_battles: 8160

num_wins: 3885

celo_rating: 1267.39

family_friendly_score: 0.5327999999999999

family_friendly_standard_error: 0.007055836732804976

submission_type: basic

model_repo: AlbertWang8192/2025-07-11_3

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': 0.5918602334038532, 'latency_mean': 1.6894819176197051, 'latency_p50': 1.681453824043274, 'latency_p90': 1.8499937772750854}, {'batch_size': 3, 'throughput': 1.0757526021749806, 'latency_mean': 2.7773764979839326, 'latency_p50': 2.76109516620636, 'latency_p90': 3.0633333683013917}, {'batch_size': 5, 'throughput': 1.2840881305492824, 'latency_mean': 3.8629465174674986, 'latency_p50': 3.865797519683838, 'latency_p90': 4.330061244964599}, {'batch_size': 6, 'throughput': 1.3410252298998573, 'latency_mean': 4.4471726334095, 'latency_p50': 4.466311812400818, 'latency_p90': 4.918674755096435}, {'batch_size': 8, 'throughput': 1.405067152794492, 'latency_mean': 5.65400584101677, 'latency_p50': 5.639032244682312, 'latency_p90': 6.368279147148132}, {'batch_size': 10, 'throughput': 1.4379414668593429, 'latency_mean': 6.897549080848694, 'latency_p50': 6.916011929512024, 'latency_p90': 7.660184717178344}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_3

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_3

model_size: 13B

ranking_group: single

throughput_3p7s: 1.27

us_pacific_date: 2025-07-11

win_ratio: 0.47610294117647056

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_start|>', '\n', '<|im_end|>'], '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': True}

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 albertwang8192-2025-07-11-3-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-11-3-v1-mkmlizer to finish
albertwang8192-2025-07-11-3-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-11-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║   ██████   ██████  █████   ████  ████                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  ░░██████ ██████  ░░███   ███░  ░░███                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║   ░███░█████░███   ░███  ███     ░███                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║   ░███░░███ ░███   ░███████      ░███                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║   ░███ ░░░  ░███   ░███░░███     ░███                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║   ░███      ░███   ░███ ░░███    ░███                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║   █████     █████  █████ ░░████  █████                              ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-3-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-3-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`
albertwang8192-2025-07-11-3-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`
albertwang8192-2025-07-11-3-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`
albertwang8192-2025-07-11-3-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`
albertwang8192-2025-07-11-3-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`
albertwang8192-2025-07-11-3-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`
albertwang8192-2025-07-11-3-v1-mkmlizer: Downloaded to shared memory in 50.887s
albertwang8192-2025-07-11-3-v1-mkmlizer: Checking if AlbertWang8192/2025-07-11_3 already exists in ChaiML
albertwang8192-2025-07-11-3-v1-mkmlizer: Creating repo ChaiML/2025-07-11_3 and uploading /tmp/tmpa2das6fd to it
albertwang8192-2025-07-11-3-v1-mkmlizer: 
  0%|          | 0/6 [00:00<?, ?it/s]
 17%|█▋        | 1/6 [00:07<00:36,  7.39s/it]
 33%|███▎      | 2/6 [00:11<00:20,  5.17s/it]
 50%|█████     | 3/6 [00:14<00:13,  4.59s/it]
 67%|██████▋   | 4/6 [00:18<00:08,  4.16s/it]
 83%|████████▎ | 5/6 [00:23<00:04,  4.34s/it]
100%|██████████| 6/6 [00:24<00:00,  3.28s/it]
100%|██████████| 6/6 [00:24<00:00,  4.05s/it]
albertwang8192-2025-07-11-3-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpa2das6fd, device:0
albertwang8192-2025-07-11-3-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
Failed to get response for submission chaiml-gojo-satoru-hurt_60780_v1: HTTPConnectionPool(host='chaiml-gojo-satoru-hurt-60780-v1-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
albertwang8192-2025-07-11-3-v1-mkmlizer: quantized model in 31.302s
albertwang8192-2025-07-11-3-v1-mkmlizer: Processed model AlbertWang8192/2025-07-11_3 in 131.963s
albertwang8192-2025-07-11-3-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-3-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-3-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-3-v1/nvidia
albertwang8192-2025-07-11-3-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-3-v1/nvidia/config.json
albertwang8192-2025-07-11-3-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-3-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-3-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-3-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-3-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-3-v1/nvidia/tokenizer.json
albertwang8192-2025-07-11-3-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-3-v1/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-11-3-v1-mkmlizer: 
Loading 0:   0%|          | 0/363 [00:00<?, ?it/s]
Loading 0:   1%|▏         | 5/363 [00:00<00:10, 33.43it/s]
Loading 0:   4%|▎         | 13/363 [00:00<00:06, 52.43it/s]
Loading 0:   5%|▌         | 19/363 [00:00<00:07, 45.90it/s]
Loading 0:   7%|▋         | 24/363 [00:00<00:07, 43.80it/s]
Loading 0:   9%|▊         | 31/363 [00:00<00:06, 49.74it/s]
Loading 0:  10%|█         | 37/363 [00:00<00:07, 45.38it/s]
Loading 0:  12%|█▏        | 42/363 [00:00<00:07, 43.60it/s]
Loading 0:  13%|█▎        | 49/363 [00:01<00:06, 48.31it/s]
Loading 0:  15%|█▌        | 55/363 [00:01<00:06, 44.74it/s]
Loading 0:  17%|█▋        | 61/363 [00:01<00:08, 34.52it/s]
Loading 0:  18%|█▊        | 65/363 [00:01<00:08, 33.95it/s]
Loading 0:  20%|█▉        | 72/363 [00:01<00:07, 40.34it/s]
Loading 0:  21%|██        | 77/363 [00:01<00:06, 42.39it/s]
Loading 0:  23%|██▎       | 82/363 [00:02<00:07, 36.15it/s]
Loading 0:  25%|██▍       | 89/363 [00:02<00:06, 42.60it/s]
Loading 0:  26%|██▌       | 94/363 [00:02<00:06, 41.95it/s]
Loading 0:  27%|██▋       | 99/363 [00:02<00:06, 42.53it/s]
Loading 0:  29%|██▉       | 105/363 [00:02<00:06, 40.84it/s]
Loading 0:  30%|███       | 110/363 [00:02<00:05, 42.35it/s]
Loading 0:  32%|███▏      | 115/363 [00:02<00:05, 42.91it/s]
Loading 0:  33%|███▎      | 120/363 [00:02<00:05, 41.29it/s]
Loading 0:  35%|███▍      | 126/363 [00:02<00:05, 43.79it/s]
Loading 0:  36%|███▋      | 132/363 [00:03<00:05, 41.35it/s]
Loading 0:  38%|███▊      | 137/363 [00:03<00:05, 40.66it/s]
Loading 0:  39%|███▉      | 142/363 [00:03<00:07, 31.11it/s]
Loading 0:  40%|████      | 146/363 [00:03<00:06, 32.19it/s]
Loading 0:  41%|████▏     | 150/363 [00:03<00:06, 31.78it/s]
Loading 0:  43%|████▎     | 156/363 [00:03<00:05, 36.83it/s]
Loading 0:  44%|████▍     | 160/363 [00:04<00:05, 36.34it/s]
Loading 0:  45%|████▌     | 165/363 [00:04<00:05, 38.91it/s]
Loading 0:  47%|████▋     | 170/363 [00:04<00:04, 40.33it/s]
Loading 0:  48%|████▊     | 175/363 [00:04<00:04, 41.11it/s]
Loading 0:  50%|████▉     | 180/363 [00:04<00:04, 43.21it/s]
Loading 0:  51%|█████     | 185/363 [00:04<00:04, 35.69it/s]
Loading 0:  53%|█████▎    | 192/363 [00:04<00:04, 42.54it/s]
Loading 0:  54%|█████▍    | 197/363 [00:04<00:03, 42.01it/s]
Loading 0:  56%|█████▌    | 202/363 [00:04<00:03, 42.23it/s]
Loading 0:  57%|█████▋    | 207/363 [00:05<00:03, 44.03it/s]
Loading 0:  58%|█████▊    | 212/363 [00:05<00:04, 36.32it/s]
Loading 0:  60%|██████    | 218/363 [00:05<00:03, 40.67it/s]
Loading 0:  61%|██████▏   | 223/363 [00:05<00:04, 32.04it/s]
Loading 0:  63%|██████▎   | 227/363 [00:05<00:04, 33.01it/s]
Loading 0:  64%|██████▎   | 231/363 [00:05<00:04, 31.44it/s]
Loading 0:  65%|██████▌   | 237/363 [00:06<00:03, 35.91it/s]
Loading 0:  66%|██████▋   | 241/363 [00:06<00:03, 35.73it/s]
Loading 0:  68%|██████▊   | 246/363 [00:06<00:03, 38.19it/s]
Loading 0:  69%|██████▉   | 250/363 [00:06<00:02, 37.84it/s]
Loading 0:  70%|███████   | 255/363 [00:06<00:02, 40.83it/s]
Loading 0:  72%|███████▏  | 260/363 [00:06<00:02, 41.33it/s]
Loading 0:  73%|███████▎  | 265/363 [00:06<00:02, 41.28it/s]
Loading 0:  74%|███████▍  | 270/363 [00:06<00:02, 43.21it/s]
Loading 0:  76%|███████▌  | 275/363 [00:07<00:02, 35.18it/s]
Loading 0:  78%|███████▊  | 282/363 [00:07<00:01, 41.91it/s]
Loading 0:  79%|███████▉  | 287/363 [00:07<00:01, 42.18it/s]
Loading 0:  80%|████████  | 292/363 [00:07<00:01, 42.43it/s]
Loading 0:  82%|████████▏ | 297/363 [00:07<00:01, 43.57it/s]
Loading 0:  83%|████████▎ | 302/363 [00:07<00:01, 44.20it/s]
Loading 0:  85%|████████▍ | 307/363 [00:08<00:02, 21.03it/s]
Loading 0:  86%|████████▌ | 312/363 [00:08<00:02, 23.34it/s]
Loading 0:  88%|████████▊ | 320/363 [00:08<00:01, 31.68it/s]
Loading 0:  90%|████████▉ | 326/363 [00:08<00:01, 33.24it/s]
Loading 0:  91%|█████████ | 331/363 [00:08<00:00, 33.94it/s]
Loading 0:  93%|█████████▎| 337/363 [00:08<00:00, 38.84it/s]
Loading 0:  94%|█████████▍| 342/363 [00:08<00:00, 39.61it/s]
Loading 0:  96%|█████████▌| 347/363 [00:09<00:00, 40.80it/s]
Loading 0:  97%|█████████▋| 352/363 [00:09<00:00, 42.54it/s]
Loading 0:  98%|█████████▊| 357/363 [00:09<00:00, 35.75it/s]
                                                            
Job albertwang8192-2025-07-11-3-v1-mkmlizer completed after 157.5s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-3-v1-mkmlizer
Pipeline stage MKMLizer completed in 158.16s
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 albertwang8192-2025-07-11-3-v1
Waiting for inference service albertwang8192-2025-07-11-3-v1 to be ready
Inference service albertwang8192-2025-07-11-3-v1 ready after 200.79567766189575s
Pipeline stage MKMLDeployer completed in 201.37s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.4799752235412598s
Received healthy response to inference request in 1.6825320720672607s
Received healthy response to inference request in 1.5905179977416992s
Received healthy response to inference request in 1.546872854232788s
Received healthy response to inference request in 1.9901573657989502s
5 requests
0 failed requests
5th percentile: 1.5556018829345704
10th percentile: 1.5643309116363526
20th percentile: 1.581788969039917
30th percentile: 1.6089208126068115
40th percentile: 1.6457264423370361
50th percentile: 1.6825320720672607
60th percentile: 1.8055821895599364
70th percentile: 1.9286323070526123
80th percentile: 2.088120937347412
90th percentile: 2.284048080444336
95th percentile: 2.382011651992798
99th percentile: 2.460382509231567
mean time: 1.8580111026763917
Pipeline stage StressChecker completed in 10.55s
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 0.69s
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 0.71s
Shutdown handler de-registered
albertwang8192-2025-07-11-3_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 0.12s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.09s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service albertwang8192-2025-07-11-3-v1-profiler
Waiting for inference service albertwang8192-2025-07-11-3-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 3286.42s
Shutdown handler de-registered
albertwang8192-2025-07-11-3_v1 status is now inactive due to auto deactivation removed underperforming models
albertwang8192-2025-07-11-3_v1 status is now torndown due to DeploymentManager action