albertwang8192-2025-07-11-7

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-7_v2

model_name: 2025-07-11_7_v2

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-12T14:29:33+00:00

num_battles: 9609

num_wins: 4612

celo_rating: 1272.92

family_friendly_score: 0.5494

family_friendly_standard_error: 0.007036471274722863

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': 0.5935661743484806, 'latency_mean': 1.6846338272094727, 'latency_p50': 1.689473032951355, 'latency_p90': 1.8466725587844848}, {'batch_size': 3, 'throughput': 1.0792544984934884, 'latency_mean': 2.771898581981659, 'latency_p50': 2.7525196075439453, 'latency_p90': 3.0649533033370973}, {'batch_size': 5, 'throughput': 1.2843399936692812, 'latency_mean': 3.866551525592804, 'latency_p50': 3.8768715858459473, 'latency_p90': 4.2778761148452755}, {'batch_size': 6, 'throughput': 1.3575399931942784, 'latency_mean': 4.405981460809707, 'latency_p50': 4.393373489379883, 'latency_p90': 4.94730384349823}, {'batch_size': 8, 'throughput': 1.4098841510998998, 'latency_mean': 5.635313649177551, 'latency_p50': 5.661107301712036, 'latency_p90': 6.273470497131347}, {'batch_size': 10, 'throughput': 1.4398590059329048, 'latency_mean': 6.88817862033844, 'latency_p50': 6.900687098503113, 'latency_p90': 7.706835436820984}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_7_v2

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_7

model_size: 13B

ranking_group: single

throughput_3p7s: 1.27

us_pacific_date: 2025-07-12

win_ratio: 0.4799666978873972

generation_params: {'temperature': 0.7, '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': 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-v2-mkmlizer
Waiting for job on albertwang8192-2025-07-11-7-v2-mkmlizer to finish
albertwang8192-2025-07-11-7-v2-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
albertwang8192-2025-07-11-7-v2-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-7-v2-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-7-v2-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-7-v2-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-v2-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-v2-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-v2-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-v2-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-v2-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-v2-mkmlizer: Downloaded to shared memory in 30.883s
albertwang8192-2025-07-11-7-v2-mkmlizer: Checking if AlbertWang8192/2025-07-11_7 already exists in ChaiML
albertwang8192-2025-07-11-7-v2-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpihvpup9u, device:0
albertwang8192-2025-07-11-7-v2-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-11-7-v2-mkmlizer: quantized model in 29.970s
albertwang8192-2025-07-11-7-v2-mkmlizer: Processed model AlbertWang8192/2025-07-11_7 in 60.937s
albertwang8192-2025-07-11-7-v2-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-7-v2-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-7-v2-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v2/nvidia
albertwang8192-2025-07-11-7-v2-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v2/nvidia/config.json
albertwang8192-2025-07-11-7-v2-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v2/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-7-v2-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v2/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-7-v2-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v2/nvidia/tokenizer.json
albertwang8192-2025-07-11-7-v2-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-7-v2/nvidia/flywheel_model.0.safetensors
albertwang8192-2025-07-11-7-v2-mkmlizer: 
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Job albertwang8192-2025-07-11-7-v2-mkmlizer completed after 85.3s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-7-v2-mkmlizer
Pipeline stage MKMLizer completed in 85.85s
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Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.18s
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Running pipeline stage MKMLDeployer
Creating inference service albertwang8192-2025-07-11-7-v2
Waiting for inference service albertwang8192-2025-07-11-7-v2 to be ready
Inference service albertwang8192-2025-07-11-7-v2 ready after 210.75785374641418s
Pipeline stage MKMLDeployer completed in 211.63s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.844414234161377s
Received healthy response to inference request in 1.5948050022125244s
Received healthy response to inference request in 1.6160356998443604s
Received healthy response to inference request in 1.6312181949615479s
Received healthy response to inference request in 1.832765817642212s
5 requests
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5th percentile: 1.5990511417388915
10th percentile: 1.6032972812652588
20th percentile: 1.6117895603179933
30th percentile: 1.6190721988677979
40th percentile: 1.6251451969146729
50th percentile: 1.6312181949615479
60th percentile: 1.7118372440338134
70th percentile: 1.792456293106079
80th percentile: 2.035095500946045
90th percentile: 2.439754867553711
95th percentile: 2.6420845508575437
99th percentile: 2.8039482975006105
mean time: 1.9038477897644044
Pipeline stage StressChecker completed in 11.18s
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Pipeline stage OfflineFamilyFriendlyScorer completed in 4786.46s
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