albertwang8192-2025-07-11-7

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-7_v12

model_name: 2025-07-11_7_V12

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-13T17:33:56+00:00

num_battles: 11083

num_wins: 5229

celo_rating: 1266.72

family_friendly_score: 0.5482

family_friendly_standard_error: 0.007038135548566822

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': 0.5917399026336746, 'latency_mean': 1.6897552525997162, 'latency_p50': 1.6971747875213623, 'latency_p90': 1.8498147249221801}, {'batch_size': 3, 'throughput': 1.0693525223377962, 'latency_mean': 2.801103210449219, 'latency_p50': 2.7971802949905396, 'latency_p90': 3.1104392290115355}, {'batch_size': 5, 'throughput': 1.2735501571536259, 'latency_mean': 3.9029284703731535, 'latency_p50': 3.925580859184265, 'latency_p90': 4.382415509223938}, {'batch_size': 6, 'throughput': 1.3336294280577163, 'latency_mean': 4.4776989626884465, 'latency_p50': 4.4809410572052, 'latency_p90': 4.951620745658874}, {'batch_size': 8, 'throughput': 1.4069154263382975, 'latency_mean': 5.64522234916687, 'latency_p50': 5.684951424598694, 'latency_p90': 6.344553136825561}, {'batch_size': 10, 'throughput': 1.4308083751397842, 'latency_mean': 6.926118828058243, 'latency_p50': 6.921970844268799, 'latency_p90': 7.798496246337891}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_7_V12

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_7

model_size: 13B

ranking_group: single

throughput_3p7s: 1.25

us_pacific_date: 2025-07-13

win_ratio: 0.4718036632680682

generation_params: {'temperature': 0.6, 'top_p': 0.98, 'min_p': 0.05, 'top_k': 35, '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': '<|im_start|>system\n{memory}<|im_end|>\n', 'prompt_template': '<|im_start|>user\n{prompt}<|im_end|>\n', 'bot_template': '<|im_start|>assistant\n{bot_name}: {message}<|im_end|>\n', 'user_template': '<|im_start|>user\n{user_name}: {message}<|im_end|>\n', 'response_template': '<|im_start|>assistant\n{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-v12-mkmlizer
Waiting for job on albertwang8192-2025-07-11-7-v12-mkmlizer to finish
albertwang8192-2025-07-11-7-v12-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-11-7-v12-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-7-v12-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v12-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-7-v12-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-v12-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-v12-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-v12-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-v12-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-v12-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-v12-mkmlizer: Downloaded to shared memory in 37.506s
albertwang8192-2025-07-11-7-v12-mkmlizer: Checking if AlbertWang8192/2025-07-11_7 already exists in ChaiML
albertwang8192-2025-07-11-7-v12-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpuf3iea8f, device:0
albertwang8192-2025-07-11-7-v12-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-11-7-v12-mkmlizer: quantized model in 36.547s
albertwang8192-2025-07-11-7-v12-mkmlizer: Processed model AlbertWang8192/2025-07-11_7 in 74.146s
albertwang8192-2025-07-11-7-v12-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-7-v12-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-7-v12-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v12/nvidia
albertwang8192-2025-07-11-7-v12-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v12/nvidia/config.json
albertwang8192-2025-07-11-7-v12-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v12/nvidia/tokenizer.json
albertwang8192-2025-07-11-7-v12-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v12/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-11-7-v12-mkmlizer: 
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Job albertwang8192-2025-07-11-7-v12-mkmlizer completed after 106.27s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-7-v12-mkmlizer
Pipeline stage MKMLizer completed in 106.81s
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Waiting for inference service albertwang8192-2025-07-11-7-v12 to be ready
Inference service albertwang8192-2025-07-11-7-v12 ready after 221.0014772415161s
Pipeline stage MKMLDeployer completed in 221.53s
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Running pipeline stage StressChecker
Received healthy response to inference request in 3.006869077682495s
Received healthy response to inference request in 1.6365158557891846s
Received healthy response to inference request in 1.9245319366455078s
Received healthy response to inference request in 1.98667311668396s
Received healthy response to inference request in 1.5131330490112305s
5 requests
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5th percentile: 1.5378096103668213
10th percentile: 1.562486171722412
20th percentile: 1.6118392944335938
30th percentile: 1.6941190719604493
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60th percentile: 1.9493884086608886
70th percentile: 1.9742448806762696
80th percentile: 2.190712308883667
90th percentile: 2.5987906932830813
95th percentile: 2.802829885482788
99th percentile: 2.9660612392425536
mean time: 2.0135446071624754
Pipeline stage StressChecker completed in 11.38s
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