developer_uid: RandomForest1024
submission_id: albertwang8192-2025-07-10-7_v1
model_name: 2025-07-10_7
model_group: AlbertWang8192/2025-07-1
status: torndown
timestamp: 2025-07-11T10:53:24+00:00
num_battles: 9020
num_wins: 4129
celo_rating: 1259.17
family_friendly_score: 0.5871999999999999
family_friendly_standard_error: 0.006962702923434261
submission_type: basic
model_repo: AlbertWang8192/2025-07-10_7
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': 0.5920178527673683, 'latency_mean': 1.6890002465248108, 'latency_p50': 1.6945346593856812, 'latency_p90': 1.8665497541427611}, {'batch_size': 3, 'throughput': 1.0619044835056766, 'latency_mean': 2.8218746829032897, 'latency_p50': 2.818920135498047, 'latency_p90': 3.0622676849365233}, {'batch_size': 5, 'throughput': 1.273308959208137, 'latency_mean': 3.9136815536022187, 'latency_p50': 3.918639898300171, 'latency_p90': 4.444105625152588}, {'batch_size': 6, 'throughput': 1.335860553298817, 'latency_mean': 4.465867065191269, 'latency_p50': 4.465542197227478, 'latency_p90': 4.9675088882446286}, {'batch_size': 8, 'throughput': 1.39453895104365, 'latency_mean': 5.6991093122959136, 'latency_p50': 5.758219242095947, 'latency_p90': 6.380070352554322}, {'batch_size': 10, 'throughput': 1.4255459389991247, 'latency_mean': 6.958080224990844, 'latency_p50': 7.03489351272583, 'latency_p90': 7.744207739830017}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: 2025-07-10_7
is_internal_developer: False
language_model: AlbertWang8192/2025-07-10_7
model_size: 13B
ranking_group: single
throughput_3p7s: 1.25
us_pacific_date: 2025-07-11
win_ratio: 0.45776053215077606
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': 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 albertwang8192-2025-07-10-7-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-10-7-v1-mkmlizer to finish
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albertwang8192-2025-07-10-7-v1-mkmlizer: ║ ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ Version: 0.29.15 ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ Features: FLYWHEEL, CUDA ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ Copyright 2023-2025 MK ONE TECHNOLOGIES Inc. ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ https://mk1.ai ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ The license key for the current software has been verified as ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ belonging to: ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ Chai Research Corp. ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ Expiration: 2028-03-31 23:59:59 ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ║ ║
albertwang8192-2025-07-10-7-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-10-7-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`
albertwang8192-2025-07-10-7-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`
albertwang8192-2025-07-10-7-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`
albertwang8192-2025-07-10-7-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`
albertwang8192-2025-07-10-7-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`
albertwang8192-2025-07-10-7-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`
albertwang8192-2025-07-10-7-v1-mkmlizer: Downloaded to shared memory in 49.294s
albertwang8192-2025-07-10-7-v1-mkmlizer: Checking if AlbertWang8192/2025-07-10_7 already exists in ChaiML
albertwang8192-2025-07-10-7-v1-mkmlizer: Creating repo ChaiML/2025-07-10_7 and uploading /tmp/tmphzkr45uu to it
albertwang8192-2025-07-10-7-v1-mkmlizer: 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:08<00:41, 8.25s/it] 33%|███▎ | 2/6 [00:16<00:32, 8.14s/it] 50%|█████ | 3/6 [00:23<00:22, 7.49s/it] 67%|██████▋ | 4/6 [00:27<00:12, 6.16s/it] 83%|████████▎ | 5/6 [00:30<00:05, 5.19s/it] 100%|██████████| 6/6 [00:31<00:00, 3.80s/it] 100%|██████████| 6/6 [00:31<00:00, 5.29s/it]
albertwang8192-2025-07-10-7-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmphzkr45uu, device:0
albertwang8192-2025-07-10-7-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-10-7-v1-mkmlizer: quantized model in 31.187s
albertwang8192-2025-07-10-7-v1-mkmlizer: Processed model AlbertWang8192/2025-07-10_7 in 138.474s
albertwang8192-2025-07-10-7-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-10-7-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-7-v1/nvidia/config.json
albertwang8192-2025-07-10-7-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-7-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-10-7-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-7-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-10-7-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-7-v1/nvidia/tokenizer.json
albertwang8192-2025-07-10-7-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-10-7-v1/nvidia/flywheel_model.0.safetensors
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Job albertwang8192-2025-07-10-7-v1-mkmlizer completed after 168.86s with status: succeeded
Stopping job with name albertwang8192-2025-07-10-7-v1-mkmlizer
Pipeline stage MKMLizer completed in 169.56s
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Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.18s
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Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-10-7-v1
Waiting for inference service albertwang8192-2025-07-10-7-v1 to be ready
Inference service albertwang8192-2025-07-10-7-v1 ready after 211.33326315879822s
Pipeline stage MKMLDeployer completed in 212.02s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.340782403945923s
Received healthy response to inference request in 1.3163807392120361s
Received healthy response to inference request in 2.0234458446502686s
Received healthy response to inference request in 1.7333807945251465s
Received healthy response to inference request in 1.5192368030548096s
5 requests
0 failed requests
5th percentile: 1.3569519519805908
10th percentile: 1.3975231647491455
20th percentile: 1.4786655902862549
30th percentile: 1.562065601348877
40th percentile: 1.6477231979370117
50th percentile: 1.7333807945251465
60th percentile: 1.8494068145751954
70th percentile: 1.965432834625244
80th percentile: 2.0869131565093997
90th percentile: 2.213847780227661
95th percentile: 2.2773150920867917
99th percentile: 2.3280889415740966
mean time: 1.7866453170776366
Pipeline stage StressChecker completed in 10.27s
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Pipeline stage OfflineFamilyFriendlyScorer completed in 4777.37s
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