albertwang8192-2025-07-10-3

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-10-3_v1

model_name: 2025-07-10_3

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-11T00:59:04+00:00

num_battles: 6943

num_wins: 3227

celo_rating: 1253.3

family_friendly_score: 0.6024

family_friendly_standard_error: 0.006921188337272726

submission_type: basic

model_repo: AlbertWang8192/2025-07-10_3

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': 0.5937423902409834, 'latency_mean': 1.6840528166294098, 'latency_p50': 1.6906144618988037, 'latency_p90': 1.8456699132919312}, {'batch_size': 3, 'throughput': 1.0740044529724164, 'latency_mean': 2.791712448596954, 'latency_p50': 2.785088300704956, 'latency_p90': 3.1232415676116942}, {'batch_size': 5, 'throughput': 1.2890578164376258, 'latency_mean': 3.862495193481445, 'latency_p50': 3.8335556983947754, 'latency_p90': 4.346045780181885}, {'batch_size': 6, 'throughput': 1.3444413622761968, 'latency_mean': 4.429687863588333, 'latency_p50': 4.4473572969436646, 'latency_p90': 4.951315021514892}, {'batch_size': 8, 'throughput': 1.4115479532204847, 'latency_mean': 5.638472702503204, 'latency_p50': 5.586591124534607, 'latency_p90': 6.431849408149719}, {'batch_size': 10, 'throughput': 1.437115714834562, 'latency_mean': 6.90334664940834, 'latency_p50': 6.83819580078125, 'latency_p90': 7.784946584701538}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-10_3

is_internal_developer: False

language_model: AlbertWang8192/2025-07-10_3

model_size: 13B

ranking_group: single

throughput_3p7s: 1.27

us_pacific_date: 2025-07-10

win_ratio: 0.4647846752124442

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_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': False}

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 albertwang8192-2025-07-10-3-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-10-3-v1-mkmlizer to finish
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albertwang8192-2025-07-10-3-v1-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-10-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-10-3-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-10-3-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`
albertwang8192-2025-07-10-3-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`
albertwang8192-2025-07-10-3-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`
albertwang8192-2025-07-10-3-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`
albertwang8192-2025-07-10-3-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`
albertwang8192-2025-07-10-3-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`
albertwang8192-2025-07-10-3-v1-mkmlizer: Downloaded to shared memory in 133.421s
albertwang8192-2025-07-10-3-v1-mkmlizer: Checking if AlbertWang8192/2025-07-10_3 already exists in ChaiML
albertwang8192-2025-07-10-3-v1-mkmlizer: Creating repo ChaiML/2025-07-10_3 and uploading /tmp/tmpyisu750_ to it
albertwang8192-2025-07-10-3-v1-mkmlizer: 
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albertwang8192-2025-07-10-3-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpyisu750_, device:0
albertwang8192-2025-07-10-3-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-10-3-v1-mkmlizer: quantized model in 31.376s
albertwang8192-2025-07-10-3-v1-mkmlizer: Processed model AlbertWang8192/2025-07-10_3 in 227.213s
albertwang8192-2025-07-10-3-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-10-3-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-10-3-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-10-3-v1/nvidia
albertwang8192-2025-07-10-3-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-3-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-10-3-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-3-v1/nvidia/config.json
albertwang8192-2025-07-10-3-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-3-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-10-3-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-10-3-v1/nvidia/tokenizer.json
albertwang8192-2025-07-10-3-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-10-3-v1/nvidia/flywheel_model.0.safetensors
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Job albertwang8192-2025-07-10-3-v1-mkmlizer completed after 251.2s with status: succeeded
Stopping job with name albertwang8192-2025-07-10-3-v1-mkmlizer
Pipeline stage MKMLizer completed in 251.97s
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Running pipeline stage MKMLTemplater
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Creating inference service albertwang8192-2025-07-10-3-v1
Waiting for inference service albertwang8192-2025-07-10-3-v1 to be ready
Inference service albertwang8192-2025-07-10-3-v1 ready after 201.46978616714478s
Pipeline stage MKMLDeployer completed in 202.01s
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Running pipeline stage StressChecker
Received healthy response to inference request in 1.959303855895996s
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5 requests
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