chaiml-elite-feed-convo-_1137

submission_id: chaiml-elite-feed-convo-_1137_v2

developer_uid: zonemercy

alignment_samples: 10774

alignment_score: 0.07303170023673863

best_of: 8

celo_rating: 1248.34

display_name: chaiml-elite-feed-convo-_1137_v2

formatter: {'memory_template': "Bot's name: {bot_name}\nBot never initiate sex act unless User started first in the conversation\n####\n", 'prompt_template': '', 'bot_template': 'Bot: {message}</s>', 'user_template': 'User: {message}</s>', 'response_template': 'Bot:', 'truncate_by_message': True}

generation_params: {'temperature': 0.9, 'top_p': 1.0, 'min_p': 0.05, 'top_k': 80, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['</s>', 'Bot:', 'User:', 'You:'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}

gpu_counts: {'NVIDIA RTX A5000': 1}

is_internal_developer: True

language_model: ChaiML/Elite-Feed-Convo-v1-1e5ep2

latencies: [{'batch_size': 1, 'throughput': 0.6206651479575337, 'latency_mean': 1.6111121451854706, 'latency_p50': 1.6212410926818848, 'latency_p90': 1.7725836992263795}, {'batch_size': 3, 'throughput': 1.097251274902648, 'latency_mean': 2.731816028356552, 'latency_p50': 2.7353099584579468, 'latency_p90': 2.999479389190674}, {'batch_size': 5, 'throughput': 1.2519621233269302, 'latency_mean': 3.9820503854751585, 'latency_p50': 3.9942893981933594, 'latency_p90': 4.42216260433197}, {'batch_size': 6, 'throughput': 1.2775616829976575, 'latency_mean': 4.678193329572678, 'latency_p50': 4.719180941581726, 'latency_p90': 5.3646446704864506}, {'batch_size': 8, 'throughput': 1.2667983035659045, 'latency_mean': 6.281644717454911, 'latency_p50': 6.361340045928955, 'latency_p90': 7.141405987739563}, {'batch_size': 10, 'throughput': 1.220439517931244, 'latency_mean': 8.146082969903945, 'latency_p50': 8.252281546592712, 'latency_p90': 9.212560963630676}]

max_input_tokens: 1024

max_output_tokens: 64

model_architecture: MistralForCausalLM

model_group: ChaiML/Elite-Feed-Convo-

model_name: chaiml-elite-feed-convo-_1137_v2

model_num_parameters: 12772070400.0

model_repo: ChaiML/Elite-Feed-Convo-v1-1e5ep2

model_size: 13B

num_battles: 10774

num_wins: 5469

propriety_score: 0.7367303609341825

propriety_total_count: 942.0

ranking_group: single

status: inactive

submission_type: basic

throughput_3p7s: 1.23

timestamp: 2024-09-11T21:31:28+00:00

us_pacific_date: 2024-09-11

win_ratio: 0.5076109151661408

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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 chaiml-elite-feed-convo-1137-v2-mkmlizer
Waiting for job on chaiml-elite-feed-convo-1137-v2-mkmlizer to finish
chaiml-elite-feed-convo-1137-v2-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║     _____            __           __                                ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║       /___/                                                         ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║                                                                     ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  Version: 0.10.1                                                    ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  https://mk1.ai                                                     ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║                                                                     ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  The license key for the current software has been verified as      ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  belonging to:                                                      ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║                                                                     ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  Chai Research Corp.                                                ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ║                                                                     ║
chaiml-elite-feed-convo-1137-v2-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
chaiml-elite-feed-convo-1137-v2-mkmlizer: Downloaded to shared memory in 56.469s
chaiml-elite-feed-convo-1137-v2-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmp9jkcl1th, device:0
chaiml-elite-feed-convo-1137-v2-mkmlizer: Saving flywheel model at /dev/shm/model_cache
chaiml-elite-feed-convo-1137-v2-mkmlizer: quantized model in 41.529s
chaiml-elite-feed-convo-1137-v2-mkmlizer: Processed model ChaiML/Elite-Feed-Convo-v1-1e5ep2 in 97.999s
chaiml-elite-feed-convo-1137-v2-mkmlizer: creating bucket guanaco-mkml-models
chaiml-elite-feed-convo-1137-v2-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
chaiml-elite-feed-convo-1137-v2-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/chaiml-elite-feed-convo-1137-v2
chaiml-elite-feed-convo-1137-v2-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/chaiml-elite-feed-convo-1137-v2/config.json
chaiml-elite-feed-convo-1137-v2-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/chaiml-elite-feed-convo-1137-v2/special_tokens_map.json
chaiml-elite-feed-convo-1137-v2-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/chaiml-elite-feed-convo-1137-v2/tokenizer_config.json
chaiml-elite-feed-convo-1137-v2-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/chaiml-elite-feed-convo-1137-v2/tokenizer.json
chaiml-elite-feed-convo-1137-v2-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/chaiml-elite-feed-convo-1137-v2/flywheel_model.0.safetensors
chaiml-elite-feed-convo-1137-v2-mkmlizer: 
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Job chaiml-elite-feed-convo-1137-v2-mkmlizer completed after 118.05s with status: succeeded
Stopping job with name chaiml-elite-feed-convo-1137-v2-mkmlizer
Pipeline stage MKMLizer completed in 119.04s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.11s
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Running pipeline stage MKMLDeployer
Creating inference service chaiml-elite-feed-convo-1137-v2
Waiting for inference service chaiml-elite-feed-convo-1137-v2 to be ready
LLM-Router throws exception AssertionError('LLM-Router predict returns error 504') for ising
Inference service chaiml-elite-feed-convo-1137-v2 ready after 171.24063682556152s
Pipeline stage MKMLDeployer completed in 171.60s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.356912136077881s
Received healthy response to inference request in 2.010777711868286s
Received healthy response to inference request in 2.0307767391204834s
Received healthy response to inference request in 1.555039882659912s
Received healthy response to inference request in 1.5936675071716309s
5 requests
0 failed requests
5th percentile: 1.562765407562256
10th percentile: 1.5704909324645997
20th percentile: 1.585941982269287
30th percentile: 1.677089548110962
40th percentile: 1.843933629989624
50th percentile: 2.010777711868286
60th percentile: 2.018777322769165
70th percentile: 2.026776933670044
80th percentile: 2.096003818511963
90th percentile: 2.226457977294922
95th percentile: 2.291685056686401
99th percentile: 2.343866720199585
mean time: 1.9094347953796387
Pipeline stage StressChecker completed in 10.24s
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Running pipeline stage TriggerMKMLProfilingPipeline
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starting trigger_guanaco_pipeline args=%s
Pipeline stage TriggerMKMLProfilingPipeline completed in 5.97s
Shutdown handler de-registered
chaiml-elite-feed-convo-_1137_v2 status is now deployed due to DeploymentManager action
Shutdown handler registered
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Pipeline stage MKMLProfilerTemplater completed in 0.12s
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Running pipeline stage MKMLProfilerDeployer
Creating inference service chaiml-elite-feed-convo-1137-v2-profiler
Waiting for inference service chaiml-elite-feed-convo-1137-v2-profiler to be ready
Inference service chaiml-elite-feed-convo-1137-v2-profiler ready after 170.42384886741638s
Pipeline stage MKMLProfilerDeployer completed in 170.80s
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Running pipeline stage MKMLProfilerRunner
kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/chaiml-elite-feed-co265039934c646a45604dfa4df449e7fd-deplo2htm2:/code/chaiverse_profiler_1726090805 --namespace tenant-chaiml-guanaco
kubectl exec -it chaiml-elite-feed-co265039934c646a45604dfa4df449e7fd-deplo2htm2 --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1726090805 && python profiles.py profile --best_of_n 8 --auto_batch 5 --batches 1,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,105,110,115,120,125,130,135,140,145,150,155,160,165,170,175,180,185,190,195 --samples 200 --input_tokens 1024 --output_tokens 64 --summary /code/chaiverse_profiler_1726090805/summary.json'
kubectl exec -it chaiml-elite-feed-co265039934c646a45604dfa4df449e7fd-deplo2htm2 --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1726090805/summary.json'
Pipeline stage MKMLProfilerRunner completed in 1150.76s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Checking if service chaiml-elite-feed-convo-1137-v2-profiler is running
Tearing down inference service chaiml-elite-feed-convo-1137-v2-profiler
Service chaiml-elite-feed-convo-1137-v2-profiler has been torndown
Pipeline stage MKMLProfilerDeleter completed in 1.71s
Shutdown handler de-registered
chaiml-elite-feed-convo-_1137_v2 status is now inactive due to auto deactivation removed underperforming models