junhua024-chai-06-full_30622

developer_uid: junhua024
submission_id: junhua024-chai-06-full_30622_v25
model_name: junhua024-chai-06-full_30622_v25
model_group: junhua024/chai_06_full_0
status: torndown
timestamp: 2025-07-19T11:23:31+00:00
num_battles: 9104
num_wins: 4427
celo_rating: 1278.62
family_friendly_score: 0.5546
family_friendly_standard_error: 0.007028781402206217
submission_type: basic
model_repo: junhua024/chai_06_full_02102_1925
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': 0.5897738953901085, 'latency_mean': 1.6954521191120149, 'latency_p50': 1.6968069076538086, 'latency_p90': 1.8646584033966065}, {'batch_size': 3, 'throughput': 1.0625575681300583, 'latency_mean': 2.8109187579154966, 'latency_p50': 2.810524582862854, 'latency_p90': 3.108424520492554}, {'batch_size': 5, 'throughput': 1.2640839017703376, 'latency_mean': 3.9365435707569123, 'latency_p50': 3.9789410829544067, 'latency_p90': 4.33406593799591}, {'batch_size': 6, 'throughput': 1.326763558715951, 'latency_mean': 4.486967004537583, 'latency_p50': 4.512117028236389, 'latency_p90': 4.960978245735168}, {'batch_size': 8, 'throughput': 1.3847833868280228, 'latency_mean': 5.734934451580048, 'latency_p50': 5.702515006065369, 'latency_p90': 6.526673626899719}, {'batch_size': 10, 'throughput': 1.4122232694610573, 'latency_mean': 7.019515520334243, 'latency_p50': 7.1142356395721436, 'latency_p90': 7.995497679710388}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: junhua024-chai-06-full_30622_v25
is_internal_developer: False
language_model: junhua024/chai_06_full_02102_1925
model_size: 13B
ranking_group: single
throughput_3p7s: 1.24
us_pacific_date: 2025-07-19
win_ratio: 0.48626977152899825
generation_params: {'temperature': 1.0, 'top_p': 0.88, 'min_p': 0.0, 'top_k': 10, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n'], '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 junhua024-chai-06-full-30622-v25-mkmlizer
Waiting for job on junhua024-chai-06-full-30622-v25-mkmlizer to finish
junhua024-chai-06-full-30622-v25-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
junhua024-chai-06-full-30622-v25-mkmlizer: ║                                                                     ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║   ██████   ██████  █████   ████  ████                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  ░░██████ ██████  ░░███   ███░  ░░███                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║   ░███░█████░███   ░███  ███     ░███                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║   ░███░░███ ░███   ░███████      ░███                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║   ░███ ░░░  ░███   ░███░░███     ░███                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║   ░███      ░███   ░███ ░░███    ░███                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║   █████     █████  █████ ░░████  █████                              ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║                                                                     ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  Version: 0.29.15                                                   ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  https://mk1.ai                                                     ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║                                                                     ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  The license key for the current software has been verified as      ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  belonging to:                                                      ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║                                                                     ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  Chai Research Corp.                                                ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
junhua024-chai-06-full-30622-v25-mkmlizer: ║                                                                     ║
junhua024-chai-06-full-30622-v25-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-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`
junhua024-chai-06-full-30622-v25-mkmlizer: Downloaded to shared memory in 75.898s
junhua024-chai-06-full-30622-v25-mkmlizer: Checking if junhua024/chai_06_full_02102_1925 already exists in ChaiML
junhua024-chai-06-full-30622-v25-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpowncs84t, device:0
junhua024-chai-06-full-30622-v25-mkmlizer: Saving flywheel model at /dev/shm/model_cache
junhua024-chai-06-full-30622-v25-mkmlizer: quantized model in 31.931s
junhua024-chai-06-full-30622-v25-mkmlizer: Processed model junhua024/chai_06_full_02102_1925 in 107.923s
junhua024-chai-06-full-30622-v25-mkmlizer: creating bucket guanaco-mkml-models
junhua024-chai-06-full-30622-v25-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
junhua024-chai-06-full-30622-v25-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/junhua024-chai-06-full-30622-v25/nvidia
junhua024-chai-06-full-30622-v25-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/junhua024-chai-06-full-30622-v25/nvidia/config.json
junhua024-chai-06-full-30622-v25-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/junhua024-chai-06-full-30622-v25/nvidia/special_tokens_map.json
junhua024-chai-06-full-30622-v25-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/junhua024-chai-06-full-30622-v25/nvidia/tokenizer_config.json
junhua024-chai-06-full-30622-v25-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/junhua024-chai-06-full-30622-v25/nvidia/tokenizer.json
junhua024-chai-06-full-30622-v25-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/junhua024-chai-06-full-30622-v25/nvidia/flywheel_model.0.safetensors
junhua024-chai-06-full-30622-v25-mkmlizer: 
Loading 0:   0%|          | 0/363 [00:00<?, ?it/s]
Loading 0:   1%|          | 2/363 [00:00<00:22, 15.93it/s]
Loading 0:   1%|▏         | 5/363 [00:00<00:20, 17.68it/s]
Loading 0:   3%|▎         | 12/363 [00:00<00:12, 28.10it/s]
Loading 0:   5%|▍         | 17/363 [00:00<00:11, 29.29it/s]
Loading 0:   6%|▋         | 23/363 [00:00<00:10, 31.95it/s]
Loading 0:   9%|▊         | 31/363 [00:00<00:07, 43.34it/s]
Loading 0:  10%|▉         | 36/363 [00:01<00:10, 31.76it/s]
Loading 0:  11%|█▏        | 41/363 [00:01<00:09, 33.22it/s]
Loading 0:  14%|█▍        | 50/363 [00:01<00:07, 41.61it/s]
Loading 0:  15%|█▌        | 55/363 [00:01<00:08, 35.00it/s]
Loading 0:  16%|█▋        | 59/363 [00:01<00:09, 33.74it/s]
Loading 0:  18%|█▊        | 65/363 [00:01<00:08, 34.48it/s]
Loading 0:  19%|█▉        | 69/363 [00:02<00:08, 32.67it/s]
Loading 0:  21%|██        | 75/363 [00:02<00:08, 33.47it/s]
Loading 0:  22%|██▏       | 80/363 [00:02<00:08, 31.80it/s]
Loading 0:  24%|██▎       | 86/363 [00:02<00:07, 35.42it/s]
Loading 0:  25%|██▌       | 91/363 [00:02<00:07, 34.20it/s]
Loading 0:  26%|██▋       | 96/363 [00:02<00:07, 37.58it/s]
Loading 0:  28%|██▊       | 100/363 [00:03<00:09, 29.16it/s]
Loading 0:  29%|██▊       | 104/363 [00:03<00:09, 28.26it/s]
Loading 0:  31%|███       | 112/363 [00:03<00:06, 38.84it/s]
Loading 0:  32%|███▏      | 117/363 [00:03<00:08, 28.60it/s]
Loading 0:  34%|███▎      | 122/363 [00:03<00:08, 29.50it/s]
Loading 0:  35%|███▌      | 128/363 [00:03<00:07, 31.04it/s]
Loading 0:  36%|███▋      | 132/363 [00:04<00:07, 30.33it/s]
Loading 0:  38%|███▊      | 137/363 [00:04<00:06, 34.26it/s]
Loading 0:  39%|███▉      | 141/363 [00:04<00:06, 35.11it/s]
Loading 0:  40%|███▉      | 145/363 [00:04<00:07, 30.98it/s]
Loading 0:  41%|████      | 149/363 [00:04<00:07, 29.11it/s]
Loading 0:  42%|████▏     | 154/363 [00:04<00:06, 32.98it/s]
Loading 0:  44%|████▍     | 159/363 [00:04<00:06, 33.95it/s]
Loading 0:  45%|████▍     | 163/363 [00:05<00:07, 27.85it/s]
Loading 0:  46%|████▌     | 167/363 [00:05<00:07, 27.83it/s]
Loading 0:  48%|████▊     | 176/363 [00:05<00:04, 37.65it/s]
Loading 0:  50%|████▉     | 180/363 [00:05<00:05, 30.51it/s]
Loading 0:  51%|█████     | 185/363 [00:05<00:05, 32.50it/s]
Loading 0:  53%|█████▎    | 191/363 [00:05<00:05, 33.70it/s]
Loading 0:  54%|█████▎    | 195/363 [00:06<00:05, 31.83it/s]
Loading 0:  55%|█████▌    | 201/363 [00:06<00:04, 32.46it/s]
Loading 0:  57%|█████▋    | 206/363 [00:06<00:04, 32.52it/s]
Loading 0:  58%|█████▊    | 212/363 [00:06<00:04, 33.55it/s]
Loading 0:  60%|██████    | 219/363 [00:06<00:03, 41.06it/s]
Loading 0:  62%|██████▏   | 224/363 [00:06<00:03, 36.89it/s]
Loading 0:  63%|██████▎   | 228/363 [00:06<00:03, 35.36it/s]
Loading 0:  64%|██████▍   | 232/363 [00:07<00:03, 34.24it/s]
Loading 0:  66%|██████▌   | 239/363 [00:07<00:03, 37.25it/s]
Loading 0:  67%|██████▋   | 243/363 [00:07<00:04, 29.33it/s]
Loading 0:  68%|██████▊   | 248/363 [00:07<00:03, 31.58it/s]
Loading 0:  70%|██████▉   | 254/363 [00:07<00:03, 33.77it/s]
Loading 0:  71%|███████   | 258/363 [00:07<00:03, 32.41it/s]
Loading 0:  73%|███████▎  | 264/363 [00:08<00:02, 33.68it/s]
Loading 0:  74%|███████▍  | 269/363 [00:08<00:02, 33.85it/s]
Loading 0:  76%|███████▌  | 275/363 [00:08<00:02, 34.72it/s]
Loading 0:  77%|███████▋  | 281/363 [00:08<00:02, 39.73it/s]
Loading 0:  79%|███████▉  | 286/363 [00:08<00:02, 37.73it/s]
Loading 0:  80%|███████▉  | 290/363 [00:08<00:01, 37.42it/s]
Loading 0:  81%|████████  | 294/363 [00:08<00:01, 34.91it/s]
Loading 0:  83%|████████▎ | 302/363 [00:08<00:01, 41.48it/s]
Loading 0:  85%|████████▍ | 307/363 [00:09<00:01, 33.96it/s]
Loading 0:  86%|████████▌ | 311/363 [00:09<00:01, 32.64it/s]
Loading 0:  87%|████████▋ | 317/363 [00:09<00:01, 34.40it/s]
Loading 0:  88%|████████▊ | 321/363 [00:09<00:01, 33.00it/s]
Loading 0:  90%|████████▉ | 326/363 [00:09<00:01, 36.51it/s]
Loading 0:  91%|█████████ | 330/363 [00:09<00:00, 36.96it/s]
Loading 0:  92%|█████████▏| 334/363 [00:09<00:00, 32.88it/s]
Loading 0:  93%|█████████▎| 338/363 [00:10<00:00, 31.53it/s]
Loading 0:  95%|█████████▌| 346/363 [00:10<00:00, 42.53it/s]
Loading 0:  97%|█████████▋| 351/363 [00:10<00:00, 23.21it/s]
Loading 0:  98%|█████████▊| 355/363 [00:10<00:00, 25.26it/s]
Loading 0:  99%|█████████▉| 359/363 [00:10<00:00, 27.09it/s]
                                                            
Job junhua024-chai-06-full-30622-v25-mkmlizer completed after 130.88s with status: succeeded
Stopping job with name junhua024-chai-06-full-30622-v25-mkmlizer
Pipeline stage MKMLizer completed in 131.44s
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 junhua024-chai-06-full-30622-v25
Waiting for inference service junhua024-chai-06-full-30622-v25 to be ready
Failed to get response for submission chaiml-nis-qwen32b-sim_98336_v34: HTTPConnectionPool(host='chaiml-nis-qwen32b-sim-98336-v34-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
Inference service junhua024-chai-06-full-30622-v25 ready after 333.3926067352295s
Pipeline stage MKMLDeployer completed in 334.14s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.568297863006592s
Received healthy response to inference request in 1.6416871547698975s
Received healthy response to inference request in 1.6388981342315674s
Received healthy response to inference request in 1.8652658462524414s
Received healthy response to inference request in 1.6695005893707275s
5 requests
0 failed requests
5th percentile: 1.6394559383392333
10th percentile: 1.6400137424468995
20th percentile: 1.6411293506622315
30th percentile: 1.6472498416900634
40th percentile: 1.6583752155303955
50th percentile: 1.6695005893707275
60th percentile: 1.747806692123413
70th percentile: 1.8261127948760987
80th percentile: 2.0058722496032715
90th percentile: 2.2870850563049316
95th percentile: 2.4276914596557617
99th percentile: 2.540176582336426
mean time: 1.876729917526245
Pipeline stage StressChecker completed in 11.36s
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 0.75s
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 0.77s
Shutdown handler de-registered
junhua024-chai-06-full_30622_v25 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 0.15s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.13s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service junhua024-chai-06-full-30622-v25-profiler
Waiting for inference service junhua024-chai-06-full-30622-v25-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
Pipeline stage OfflineFamilyFriendlyScorer completed in 4667.09s
Shutdown handler de-registered
junhua024-chai-06-full_30622_v25 status is now inactive due to auto deactivation removed underperforming models
junhua024-chai-06-full_30622_v25 status is now torndown due to DeploymentManager action