albertwang8192-2025-07-11-2

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-2_v5

model_name: 2025-07-11_2_v5

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-12T04:53:43+00:00

num_battles: 7158

num_wins: 3448

celo_rating: 1267.1

family_friendly_score: 0.5264

family_friendly_standard_error: 0.007061204429840564

submission_type: basic

model_repo: AlbertWang8192/2025-07-11_2

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': 0.5921203494819917, 'latency_mean': 1.6887040495872498, 'latency_p50': 1.6779998540878296, 'latency_p90': 1.8736905574798584}, {'batch_size': 3, 'throughput': 1.0659397806176392, 'latency_mean': 2.810324727296829, 'latency_p50': 2.8029510974884033, 'latency_p90': 3.0993011951446534}, {'batch_size': 5, 'throughput': 1.2636125669411171, 'latency_mean': 3.9441790735721587, 'latency_p50': 3.9737948179244995, 'latency_p90': 4.329230427742004}, {'batch_size': 6, 'throughput': 1.3282006227219756, 'latency_mean': 4.500087858438492, 'latency_p50': 4.419920444488525, 'latency_p90': 5.082148504257202}, {'batch_size': 8, 'throughput': 1.3900151564631853, 'latency_mean': 5.7226068305969235, 'latency_p50': 5.682400345802307, 'latency_p90': 6.513508343696595}, {'batch_size': 10, 'throughput': 1.4249368266039266, 'latency_mean': 6.958661015033722, 'latency_p50': 6.951913118362427, 'latency_p90': 7.957208871841431}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_2_v5

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_2

model_size: 13B

ranking_group: single

throughput_3p7s: 1.23

us_pacific_date: 2025-07-11

win_ratio: 0.4816987985470802

generation_params: {'temperature': 0.6, 'top_p': 0.9, 'min_p': 0.01, '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': 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 albertwang8192-2025-07-11-2-v5-mkmlizer
Waiting for job on albertwang8192-2025-07-11-2-v5-mkmlizer to finish
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albertwang8192-2025-07-11-2-v5-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-2-v5-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v5-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-2-v5-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`
albertwang8192-2025-07-11-2-v5-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`
albertwang8192-2025-07-11-2-v5-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`
albertwang8192-2025-07-11-2-v5-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`
albertwang8192-2025-07-11-2-v5-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`
albertwang8192-2025-07-11-2-v5-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`
albertwang8192-2025-07-11-2-v5-mkmlizer: Downloaded to shared memory in 30.285s
albertwang8192-2025-07-11-2-v5-mkmlizer: Checking if AlbertWang8192/2025-07-11_2 already exists in ChaiML
albertwang8192-2025-07-11-2-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpsxcbpzl0, device:0
albertwang8192-2025-07-11-2-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-11-2-v5-mkmlizer: quantized model in 30.917s
albertwang8192-2025-07-11-2-v5-mkmlizer: Processed model AlbertWang8192/2025-07-11_2 in 61.296s
albertwang8192-2025-07-11-2-v5-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-2-v5-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-2-v5-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v5/nvidia
albertwang8192-2025-07-11-2-v5-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v5/nvidia/config.json
albertwang8192-2025-07-11-2-v5-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v5/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-2-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v5/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-2-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v5/nvidia/tokenizer.json
albertwang8192-2025-07-11-2-v5-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v5/nvidia/flywheel_model.0.safetensors
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Job albertwang8192-2025-07-11-2-v5-mkmlizer completed after 84.45s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-2-v5-mkmlizer
Pipeline stage MKMLizer completed in 84.90s
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Running pipeline stage MKMLTemplater
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Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-11-2-v5
Waiting for inference service albertwang8192-2025-07-11-2-v5 to be ready
Inference service albertwang8192-2025-07-11-2-v5 ready after 200.94206023216248s
Pipeline stage MKMLDeployer completed in 201.45s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.464247226715088s
Received healthy response to inference request in 1.5576159954071045s
Received healthy response to inference request in 1.785679578781128s
Received healthy response to inference request in 1.5558264255523682s
Received healthy response to inference request in 1.581268310546875s
5 requests
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5th percentile: 1.5561843395233155
10th percentile: 1.5565422534942628
20th percentile: 1.5572580814361572
30th percentile: 1.5623464584350586
40th percentile: 1.5718073844909668
50th percentile: 1.581268310546875
60th percentile: 1.6630328178405762
70th percentile: 1.7447973251342772
80th percentile: 1.92139310836792
90th percentile: 2.192820167541504
95th percentile: 2.3285336971282957
99th percentile: 2.4371045207977295
mean time: 1.7889275074005127
Pipeline stage StressChecker completed in 10.09s
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