albertwang8192-2025-07-12-2

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-12-2_v1

model_name: 2025-07-12_2

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-13T02:06:05+00:00

num_battles: 9415

num_wins: 3630

celo_rating: 1208.3

family_friendly_score: 0.4302

family_friendly_standard_error: 0.007001827761377739

submission_type: basic

model_repo: AlbertWang8192/2025-07-12_2

model_architecture: MistralForCausalLM

model_num_parameters: 12772090880.0

best_of: 8

max_input_tokens: 1024

max_output_tokens: 64

reward_model: default

latencies: [{'batch_size': 1, 'throughput': 0.596391597245557, 'latency_mean': 1.6766461443901062, 'latency_p50': 1.6626996994018555, 'latency_p90': 1.8720259189605712}, {'batch_size': 3, 'throughput': 1.0734472467172886, 'latency_mean': 2.7861065137386323, 'latency_p50': 2.77853524684906, 'latency_p90': 3.10543200969696}, {'batch_size': 5, 'throughput': 1.2809682885734681, 'latency_mean': 3.8876931965351105, 'latency_p50': 3.883100390434265, 'latency_p90': 4.32555124759674}, {'batch_size': 6, 'throughput': 1.3478139998852, 'latency_mean': 4.42831827878952, 'latency_p50': 4.455717206001282, 'latency_p90': 4.9256432056427}, {'batch_size': 8, 'throughput': 1.4141337089405723, 'latency_mean': 5.627452704906464, 'latency_p50': 5.62130069732666, 'latency_p90': 6.350663542747498}, {'batch_size': 10, 'throughput': 1.4348195321475417, 'latency_mean': 6.912261792421341, 'latency_p50': 6.989753007888794, 'latency_p90': 7.700507664680481}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-12_2

is_internal_developer: False

language_model: AlbertWang8192/2025-07-12_2

model_size: 13B

ranking_group: single

throughput_3p7s: 1.26

us_pacific_date: 2025-07-12

win_ratio: 0.385554965480616

generation_params: {'temperature': 0.6, 'top_p': 0.98, 'min_p': 0.05, 'top_k': 40, 'presence_penalty': 0.4, 'frequency_penalty': 0.4, 'stopping_words': ['\n', '<|im_start|>', '<|im_end|>'], '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
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Running pipeline stage MKMLizer
Starting job with name albertwang8192-2025-07-12-2-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-12-2-v1-mkmlizer to finish
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albertwang8192-2025-07-12-2-v1-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-12-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-12-2-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-12-2-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`
albertwang8192-2025-07-12-2-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`
albertwang8192-2025-07-12-2-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`
albertwang8192-2025-07-12-2-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`
albertwang8192-2025-07-12-2-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`
albertwang8192-2025-07-12-2-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`
albertwang8192-2025-07-12-2-v1-mkmlizer: Downloaded to shared memory in 56.183s
albertwang8192-2025-07-12-2-v1-mkmlizer: Checking if AlbertWang8192/2025-07-12_2 already exists in ChaiML
albertwang8192-2025-07-12-2-v1-mkmlizer: 
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albertwang8192-2025-07-12-2-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmp769juqcb, device:0
albertwang8192-2025-07-12-2-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-12-2-v1-mkmlizer: quantized model in 30.481s
albertwang8192-2025-07-12-2-v1-mkmlizer: Processed model AlbertWang8192/2025-07-12_2 in 130.592s
albertwang8192-2025-07-12-2-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-12-2-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-12-2-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-12-2-v1/nvidia
albertwang8192-2025-07-12-2-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-12-2-v1/nvidia/config.json
albertwang8192-2025-07-12-2-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-12-2-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-12-2-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-12-2-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-12-2-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-12-2-v1/nvidia/tokenizer.json
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Job albertwang8192-2025-07-12-2-v1-mkmlizer completed after 157.42s with status: succeeded
Stopping job with name albertwang8192-2025-07-12-2-v1-mkmlizer
Pipeline stage MKMLizer completed in 157.99s
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Creating inference service albertwang8192-2025-07-12-2-v1
Waiting for inference service albertwang8192-2025-07-12-2-v1 to be ready
Inference service albertwang8192-2025-07-12-2-v1 ready after 220.7501232624054s
Pipeline stage MKMLDeployer completed in 221.36s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.1964175701141357s
Received healthy response to inference request in 1.9349045753479004s
Received healthy response to inference request in 1.5157246589660645s
Received healthy response to inference request in 1.6417698860168457s
Received healthy response to inference request in 1.5404202938079834s
5 requests
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5th percentile: 1.5206637859344483
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