developer_uid: RandomForest1024
submission_id: albertwang8192-2025-07-14-0_v1
model_name: 2025-07-14_0
model_group: AlbertWang8192/2025-07-1
status: torndown
timestamp: 2025-07-14T16:39:16+00:00
num_battles: 6555
num_wins: 2885
celo_rating: 1241.86
family_friendly_score: 0.0
family_friendly_standard_error: 0.0
submission_type: basic
model_repo: AlbertWang8192/2025-07-14_0
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': 0.5906530007288903, 'latency_mean': 1.692920128107071, 'latency_p50': 1.696547508239746, 'latency_p90': 1.8622580528259276}, {'batch_size': 3, 'throughput': 1.051373499865309, 'latency_mean': 2.8464453339576723, 'latency_p50': 2.869174599647522, 'latency_p90': 3.08851535320282}, {'batch_size': 5, 'throughput': 1.2564309384040464, 'latency_mean': 3.962499930858612, 'latency_p50': 3.9314523935317993, 'latency_p90': 4.433927345275879}, {'batch_size': 6, 'throughput': 1.31965173906882, 'latency_mean': 4.51699901342392, 'latency_p50': 4.539092302322388, 'latency_p90': 5.018379330635071}, {'batch_size': 8, 'throughput': 1.3675954639976795, 'latency_mean': 5.804484782218933, 'latency_p50': 5.798969745635986, 'latency_p90': 6.543311429023743}, {'batch_size': 10, 'throughput': 1.404228360813219, 'latency_mean': 7.0619551050663, 'latency_p50': 7.042122006416321, 'latency_p90': 7.86295096874237}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: 2025-07-14_0
is_internal_developer: False
language_model: AlbertWang8192/2025-07-14_0
model_size: 13B
ranking_group: single
throughput_3p7s: 1.22
us_pacific_date: 2025-07-14
win_ratio: 0.4401220442410374
generation_params: {'temperature': 0.9, 'top_p': 1.0, 'min_p': 0.05, 'top_k': 80, 'presence_penalty': 0.1, 'frequency_penalty': 0.1, 'stopping_words': ['\n', '<|im_start|>', '<|im_end|>'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}
formatter: {'memory_template': '<|system|>{bot_name}memory: {memory}\n', 'prompt_template': '<|im_start|>user\n{prompt}<|im_end|>\n', 'bot_template': '<|im_start|>assistant\n{message}<|im_end|>\n', 'user_template': '<|im_start|>user\nYou:{message}<|im_end|>\n', 'response_template': '<|im_start|>assistant\n', '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 albertwang8192-2025-07-14-0-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-14-0-v1-mkmlizer to finish
albertwang8192-2025-07-14-0-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ██████ ██████ █████ ████ ████ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ░░██████ ██████ ░░███ ███░ ░░███ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ░███░█████░███ ░███ ███ ░███ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ░███░░███ ░███ ░███████ ░███ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ░███ ░░░ ░███ ░███░░███ ░███ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ░███ ░███ ░███ ░░███ ░███ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ █████ █████ █████ ░░████ █████ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ░░░░░ ░░░░░ ░░░░░ ░░░░ ░░░░░ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ Version: 0.29.15 ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ Features: FLYWHEEL, CUDA ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ Copyright 2023-2025 MK ONE TECHNOLOGIES Inc. ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ https://mk1.ai ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ The license key for the current software has been verified as ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ belonging to: ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ Chai Research Corp. ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ Expiration: 2028-03-31 23:59:59 ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ║ ║
albertwang8192-2025-07-14-0-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-14-0-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`
albertwang8192-2025-07-14-0-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`
albertwang8192-2025-07-14-0-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`
albertwang8192-2025-07-14-0-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`
albertwang8192-2025-07-14-0-v1-mkmlizer: Downloaded to shared memory in 55.249s
albertwang8192-2025-07-14-0-v1-mkmlizer: Checking if AlbertWang8192/2025-07-14_0 already exists in ChaiML
albertwang8192-2025-07-14-0-v1-mkmlizer: Creating repo ChaiML/2025-07-14_0 and uploading /tmp/tmp81acjsb4 to it
albertwang8192-2025-07-14-0-v1-mkmlizer: 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:04<00:24, 4.87s/it] 33%|███▎ | 2/6 [00:09<00:18, 4.63s/it] 50%|█████ | 3/6 [00:13<00:12, 4.23s/it] 67%|██████▋ | 4/6 [00:16<00:08, 4.03s/it] 83%|████████▎ | 5/6 [00:21<00:04, 4.32s/it] 100%|██████████| 6/6 [00:22<00:00, 3.28s/it] 100%|██████████| 6/6 [00:22<00:00, 3.82s/it]
albertwang8192-2025-07-14-0-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmp81acjsb4, device:0
albertwang8192-2025-07-14-0-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-14-0-v1-mkmlizer: quantized model in 29.993s
albertwang8192-2025-07-14-0-v1-mkmlizer: Processed model AlbertWang8192/2025-07-14_0 in 133.311s
albertwang8192-2025-07-14-0-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-14-0-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-14-0-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-14-0-v1/nvidia
albertwang8192-2025-07-14-0-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-14-0-v1/nvidia/config.json
albertwang8192-2025-07-14-0-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-14-0-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-14-0-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-14-0-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-14-0-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-14-0-v1/nvidia/tokenizer.json
albertwang8192-2025-07-14-0-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-14-0-v1/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-14-0-v1-mkmlizer: Loading 0: 0%| | 0/363 [00:00<?, ?it/s] Loading 0: 1%|▏ | 5/363 [00:00<00:11, 31.96it/s] Loading 0: 4%|▎ | 13/363 [00:00<00:06, 51.48it/s] Loading 0: 5%|▌ | 19/363 [00:00<00:07, 45.91it/s] Loading 0: 7%|▋ | 24/363 [00:00<00:07, 44.13it/s] Loading 0: 9%|▊ | 31/363 [00:00<00:06, 49.67it/s] Loading 0: 10%|█ | 37/363 [00:00<00:07, 45.87it/s] Loading 0: 12%|█▏ | 42/363 [00:00<00:07, 44.26it/s] Loading 0: 13%|█▎ | 49/363 [00:01<00:06, 48.81it/s] Loading 0: 15%|█▌ | 55/363 [00:01<00:06, 45.76it/s] Loading 0: 17%|█▋ | 61/363 [00:01<00:09, 33.50it/s] Loading 0: 18%|█▊ | 65/363 [00:01<00:09, 32.34it/s] Loading 0: 20%|█▉ | 71/363 [00:01<00:07, 37.34it/s] Loading 0: 21%|██ | 76/363 [00:01<00:07, 37.52it/s] Loading 0: 22%|██▏ | 81/363 [00:01<00:07, 38.76it/s] Loading 0: 24%|██▎ | 86/363 [00:02<00:06, 40.48it/s] Loading 0: 25%|██▌ | 91/363 [00:02<00:07, 34.22it/s] Loading 0: 27%|██▋ | 98/363 [00:02<00:06, 41.81it/s] Loading 0: 28%|██▊ | 103/363 [00:02<00:06, 42.29it/s] Loading 0: 30%|███ | 109/363 [00:02<00:05, 46.15it/s] Loading 0: 31%|███▏ | 114/363 [00:02<00:06, 39.34it/s] Loading 0: 33%|███▎ | 119/363 [00:02<00:06, 39.60it/s] Loading 0: 34%|███▍ | 125/363 [00:03<00:05, 44.16it/s] Loading 0: 36%|███▌ | 130/363 [00:03<00:05, 43.59it/s] Loading 0: 37%|███▋ | 135/363 [00:03<00:05, 43.44it/s] Loading 0: 39%|███▉ | 141/363 [00:03<00:05, 41.55it/s] Loading 0: 40%|████ | 146/363 [00:03<00:07, 29.41it/s] Loading 0: 41%|████▏ | 150/363 [00:03<00:07, 29.40it/s] Loading 0: 43%|████▎ | 156/363 [00:03<00:05, 34.96it/s] Loading 0: 44%|████▍ | 160/363 [00:04<00:05, 34.85it/s] Loading 0: 45%|████▌ | 165/363 [00:04<00:05, 37.41it/s] Loading 0: 47%|████▋ | 170/363 [00:04<00:04, 39.00it/s] Loading 0: 48%|████▊ | 175/363 [00:04<00:04, 40.40it/s] Loading 0: 50%|████▉ | 181/363 [00:04<00:04, 39.36it/s] Loading 0: 51%|█████ | 186/363 [00:04<00:04, 38.82it/s] Loading 0: 53%|█████▎ | 192/363 [00:04<00:03, 43.17it/s] Loading 0: 54%|█████▍ | 197/363 [00:04<00:03, 43.59it/s] Loading 0: 56%|█████▌ | 202/363 [00:05<00:03, 43.38it/s] Loading 0: 57%|█████▋ | 207/363 [00:05<00:03, 44.57it/s] Loading 0: 58%|█████▊ | 212/363 [00:05<00:04, 36.63it/s] Loading 0: 60%|██████ | 218/363 [00:05<00:03, 41.35it/s] Loading 0: 61%|██████▏ | 223/363 [00:05<00:04, 31.19it/s] Loading 0: 63%|██████▎ | 227/363 [00:05<00:04, 32.17it/s] Loading 0: 64%|██████▎ | 231/363 [00:05<00:04, 31.89it/s] Loading 0: 65%|██████▌ | 237/363 [00:06<00:03, 36.53it/s] Loading 0: 66%|██████▋ | 241/363 [00:06<00:03, 35.78it/s] Loading 0: 68%|██████▊ | 246/363 [00:06<00:03, 38.84it/s] Loading 0: 69%|██████▉ | 251/363 [00:06<00:02, 40.50it/s] Loading 0: 71%|███████ | 256/363 [00:06<00:02, 41.36it/s] Loading 0: 72%|███████▏ | 261/363 [00:06<00:02, 43.25it/s] Loading 0: 73%|███████▎ | 266/363 [00:06<00:02, 35.43it/s] Loading 0: 75%|███████▌ | 273/363 [00:06<00:02, 43.04it/s] Loading 0: 77%|███████▋ | 278/363 [00:07<00:01, 43.25it/s] Loading 0: 78%|███████▊ | 283/363 [00:07<00:01, 41.76it/s] Loading 0: 79%|███████▉ | 288/363 [00:07<00:01, 42.63it/s] Loading 0: 81%|████████ | 293/363 [00:07<00:01, 36.04it/s] Loading 0: 82%|████████▏ | 299/363 [00:07<00:01, 40.80it/s] Loading 0: 84%|████████▎ | 304/363 [00:08<00:02, 23.47it/s] Loading 0: 85%|████████▍ | 308/363 [00:08<00:02, 26.06it/s] Loading 0: 86%|████████▌ | 312/363 [00:08<00:01, 26.41it/s] Loading 0: 88%|████████▊ | 320/363 [00:08<00:01, 35.64it/s] Loading 0: 90%|████████▉ | 325/363 [00:08<00:00, 38.68it/s] Loading 0: 91%|█████████ | 330/363 [00:08<00:00, 34.74it/s] Loading 0: 93%|█████████▎| 338/363 [00:08<00:00, 42.75it/s] Loading 0: 94%|█████████▍| 343/363 [00:08<00:00, 44.39it/s] Loading 0: 96%|█████████▌| 348/363 [00:09<00:00, 37.98it/s] Loading 0: 98%|█████████▊| 356/363 [00:09<00:00, 45.53it/s] Loading 0: 100%|█████████▉| 362/363 [00:09<00:00, 43.31it/s]
Job albertwang8192-2025-07-14-0-v1-mkmlizer completed after 159.69s with status: succeeded
Stopping job with name albertwang8192-2025-07-14-0-v1-mkmlizer
Pipeline stage MKMLizer completed in 160.33s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.19s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-14-0-v1
Waiting for inference service albertwang8192-2025-07-14-0-v1 to be ready
Inference service albertwang8192-2025-07-14-0-v1 ready after 231.9625849723816s
Pipeline stage MKMLDeployer completed in 232.74s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.328486919403076s
Received healthy response to inference request in 1.5735177993774414s
Received healthy response to inference request in 1.9150264263153076s
Received healthy response to inference request in 1.722386360168457s
Received healthy response to inference request in 1.5840120315551758s
5 requests
0 failed requests
5th percentile: 1.5756166458129883
10th percentile: 1.5777154922485352
20th percentile: 1.5819131851196289
30th percentile: 1.611686897277832
40th percentile: 1.6670366287231446
50th percentile: 1.722386360168457
60th percentile: 1.7994423866271974
70th percentile: 1.8764984130859375
80th percentile: 1.9977185249328615
90th percentile: 2.163102722167969
95th percentile: 2.2457948207855223
99th percentile: 2.3119484996795654
mean time: 1.8246859073638917
Pipeline stage StressChecker completed in 10.69s
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 0.72s
Shutdown handler de-registered
albertwang8192-2025-07-14-0_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 0.13s
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 albertwang8192-2025-07-14-0-v1-profiler
Waiting for inference service albertwang8192-2025-07-14-0-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
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
clean up pipeline due to error=DeploymentChecksError('None: None')
Shutdown handler de-registered
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
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
clean up pipeline due to error=DeploymentChecksError('None: None')
Shutdown handler de-registered
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
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
clean up pipeline due to error=DeploymentChecksError('None: None')
Shutdown handler de-registered
albertwang8192-2025-07-14-0_v1 status is now inactive due to auto deactivation removed underperforming models
albertwang8192-2025-07-14-0_v1 status is now torndown due to DeploymentManager action