albertwang8192-2025-07-11-8

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-8_v1

model_name: 2025-07-11_8

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-12T00:29:56+00:00

num_battles: 8891

num_wins: 4301

celo_rating: 1271.52

family_friendly_score: 0.5292

family_friendly_standard_error: 0.007058999362515909

submission_type: basic

model_repo: AlbertWang8192/2025-07-11_8

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': 0.5852319580553104, 'latency_mean': 1.708605875968933, 'latency_p50': 1.7076399326324463, 'latency_p90': 1.8745662689208984}, {'batch_size': 3, 'throughput': 1.0479024502210958, 'latency_mean': 2.8538686287403108, 'latency_p50': 2.854184150695801, 'latency_p90': 3.1758473396301268}, {'batch_size': 5, 'throughput': 1.2453071334398245, 'latency_mean': 4.008280619382858, 'latency_p50': 4.036166667938232, 'latency_p90': 4.5594573497772215}, {'batch_size': 6, 'throughput': 1.2992881945490202, 'latency_mean': 4.590874302387237, 'latency_p50': 4.591545462608337, 'latency_p90': 5.066047358512878}, {'batch_size': 8, 'throughput': 1.3636416032003318, 'latency_mean': 5.827459888458252, 'latency_p50': 5.838019847869873, 'latency_p90': 6.515421724319458}, {'batch_size': 10, 'throughput': 1.3903378259470278, 'latency_mean': 7.131646395921707, 'latency_p50': 7.152897000312805, 'latency_p90': 8.13582181930542}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_8

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_8

model_size: 13B

ranking_group: single

throughput_3p7s: 1.21

us_pacific_date: 2025-07-11

win_ratio: 0.4837476099426386

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': ['\n', '<|im_start|>', '<|im_end|>'], '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
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Running pipeline stage MKMLizer
Starting job with name albertwang8192-2025-07-11-8-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-11-8-v1-mkmlizer to finish
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albertwang8192-2025-07-11-8-v1-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-8-v1-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-8-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-8-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`
albertwang8192-2025-07-11-8-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`
albertwang8192-2025-07-11-8-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`
albertwang8192-2025-07-11-8-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`
albertwang8192-2025-07-11-8-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`
albertwang8192-2025-07-11-8-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`
albertwang8192-2025-07-11-8-v1-mkmlizer: Downloaded to shared memory in 59.755s
albertwang8192-2025-07-11-8-v1-mkmlizer: Checking if AlbertWang8192/2025-07-11_8 already exists in ChaiML
albertwang8192-2025-07-11-8-v1-mkmlizer: Creating repo ChaiML/2025-07-11_8 and uploading /tmp/tmpochfghnk to it
albertwang8192-2025-07-11-8-v1-mkmlizer: 
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albertwang8192-2025-07-11-8-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpochfghnk, device:0
albertwang8192-2025-07-11-8-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
Retrying (%r) after connection broken by '%r': %s
albertwang8192-2025-07-11-8-v1-mkmlizer: quantized model in 30.537s
albertwang8192-2025-07-11-8-v1-mkmlizer: Processed model AlbertWang8192/2025-07-11_8 in 146.751s
albertwang8192-2025-07-11-8-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-8-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-8-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-8-v1/nvidia
albertwang8192-2025-07-11-8-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-8-v1/nvidia/config.json
albertwang8192-2025-07-11-8-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-8-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-8-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-8-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-8-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-8-v1/nvidia/tokenizer.json
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Job albertwang8192-2025-07-11-8-v1-mkmlizer completed after 177.65s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-8-v1-mkmlizer
Pipeline stage MKMLizer completed in 178.15s
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Creating inference service albertwang8192-2025-07-11-8-v1
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Inference service albertwang8192-2025-07-11-8-v1 ready after 200.7411241531372s
Pipeline stage MKMLDeployer completed in 201.26s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.2037107944488525s
Received healthy response to inference request in 1.7863743305206299s
Received healthy response to inference request in 1.56742262840271s
Received healthy response to inference request in 1.599421739578247s
Received healthy response to inference request in 1.700962781906128s
5 requests
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