albertwang8192-2025-07-11-7

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-7_v4

model_name: 2025-07-11_7_v4

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-12T14:31:28+00:00

num_battles: 8665

num_wins: 4176

celo_rating: 1275.4

family_friendly_score: 0.5504

family_friendly_standard_error: 0.007035052807193419

submission_type: basic

model_repo: AlbertWang8192/2025-07-11_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': 0.5991508485093857, 'latency_mean': 1.6689071476459503, 'latency_p50': 1.6648443937301636, 'latency_p90': 1.8236177444458008}, {'batch_size': 3, 'throughput': 1.0725693440493025, 'latency_mean': 2.7926451444625853, 'latency_p50': 2.8061797618865967, 'latency_p90': 3.065313959121704}, {'batch_size': 5, 'throughput': 1.3063846421327483, 'latency_mean': 3.8110953629016877, 'latency_p50': 3.7998005151748657, 'latency_p90': 4.304117584228516}, {'batch_size': 6, 'throughput': 1.368704219699207, 'latency_mean': 4.362125278711319, 'latency_p50': 4.3522303104400635, 'latency_p90': 4.898418807983399}, {'batch_size': 8, 'throughput': 1.4310343908150955, 'latency_mean': 5.545626586675644, 'latency_p50': 5.515828967094421, 'latency_p90': 6.276576805114746}, {'batch_size': 10, 'throughput': 1.4440651422008595, 'latency_mean': 6.871736663579941, 'latency_p50': 6.951283097267151, 'latency_p90': 7.618246006965637}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_7_v4

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_7

model_size: 13B

ranking_group: single

throughput_3p7s: 1.29

us_pacific_date: 2025-07-12

win_ratio: 0.4819388343912291

generation_params: {'temperature': 0.6, 'top_p': 0.98, 'min_p': 0.05, 'top_k': 30, 'presence_penalty': 0.4, 'frequency_penalty': 0.4, 'stopping_words': ['<|im_end|>', '<|im_start|>', '\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': 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 albertwang8192-2025-07-11-7-v4-mkmlizer
Waiting for job on albertwang8192-2025-07-11-7-v4-mkmlizer to finish
albertwang8192-2025-07-11-7-v4-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-11-7-v4-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-7-v4-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v4-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-7-v4-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`
albertwang8192-2025-07-11-7-v4-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`
albertwang8192-2025-07-11-7-v4-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`
albertwang8192-2025-07-11-7-v4-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`
albertwang8192-2025-07-11-7-v4-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`
albertwang8192-2025-07-11-7-v4-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`
albertwang8192-2025-07-11-7-v4-mkmlizer: Downloaded to shared memory in 29.957s
albertwang8192-2025-07-11-7-v4-mkmlizer: Checking if AlbertWang8192/2025-07-11_7 already exists in ChaiML
albertwang8192-2025-07-11-7-v4-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpmkadvhnv, device:0
albertwang8192-2025-07-11-7-v4-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-11-7-v4-mkmlizer: quantized model in 30.525s
albertwang8192-2025-07-11-7-v4-mkmlizer: Processed model AlbertWang8192/2025-07-11_7 in 60.564s
albertwang8192-2025-07-11-7-v4-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-7-v4-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-7-v4-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v4/nvidia
albertwang8192-2025-07-11-7-v4-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v4/nvidia/config.json
albertwang8192-2025-07-11-7-v4-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v4/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-7-v4-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v4/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-7-v4-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v4/nvidia/tokenizer.json
albertwang8192-2025-07-11-7-v4-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v4/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-11-7-v4-mkmlizer: 
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Job albertwang8192-2025-07-11-7-v4-mkmlizer completed after 85.31s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-7-v4-mkmlizer
Pipeline stage MKMLizer completed in 86.14s
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Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.16s
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Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-11-7-v4
Waiting for inference service albertwang8192-2025-07-11-7-v4 to be ready
Inference service albertwang8192-2025-07-11-7-v4 ready after 210.77856421470642s
Pipeline stage MKMLDeployer completed in 211.31s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.578176736831665s
Received healthy response to inference request in 1.513458490371704s
Received healthy response to inference request in 1.533811092376709s
Received healthy response to inference request in 1.9237408638000488s
Received healthy response to inference request in 1.8877346515655518s
5 requests
0 failed requests
5th percentile: 1.517529010772705
10th percentile: 1.521599531173706
20th percentile: 1.529740571975708
30th percentile: 1.6045958042144775
40th percentile: 1.7461652278900146
50th percentile: 1.8877346515655518
60th percentile: 1.9021371364593507
70th percentile: 1.9165396213531494
80th percentile: 2.054628038406372
90th percentile: 2.3164023876190187
95th percentile: 2.447289562225342
99th percentile: 2.5519993019104006
mean time: 1.8873843669891357
Pipeline stage StressChecker completed in 10.92s
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Running pipeline stage OfflineFamilyFriendlyTriggerPipeline
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Pipeline stage TriggerMKMLProfilingPipeline completed in 0.81s
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Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
Pipeline stage OfflineFamilyFriendlyScorer completed in 3091.26s
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albertwang8192-2025-07-11-7_v4 status is now inactive due to auto deactivation removed underperforming models
albertwang8192-2025-07-11-7_v4 status is now torndown due to DeploymentManager action