submission_id: riverise-feedback-retry-6k_v1
developer_uid: Riverise
best_of: 16
celo_rating: 1256.41
display_name: riverise-feedback-retry-6k_v1
family_friendly_score: 0.0
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}
generation_params: {'temperature': 1.15, 'top_p': 0.95, 'min_p': 0.05, 'top_k': 80, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n'], 'max_input_tokens': 512, 'best_of': 16, 'max_output_tokens': 64}
gpu_counts: {'NVIDIA RTX A5000': 1}
is_internal_developer: False
language_model: Riverise/feedback-retry-6k
latencies: [{'batch_size': 1, 'throughput': 0.8980787350334017, 'latency_mean': 1.113394113779068, 'latency_p50': 1.1121623516082764, 'latency_p90': 1.2395975112915039}, {'batch_size': 4, 'throughput': 1.7547587153006292, 'latency_mean': 2.263807091712952, 'latency_p50': 2.2553728818893433, 'latency_p90': 2.5490774154663085}, {'batch_size': 5, 'throughput': 1.8318566169899286, 'latency_mean': 2.711874258518219, 'latency_p50': 2.7044806480407715, 'latency_p90': 3.0254058122634886}, {'batch_size': 8, 'throughput': 1.975749354385664, 'latency_mean': 4.024229910373688, 'latency_p50': 4.012364029884338, 'latency_p90': 4.5494156837463375}, {'batch_size': 10, 'throughput': 1.9804484973321812, 'latency_mean': 4.999336326122284, 'latency_p50': 5.01066792011261, 'latency_p90': 5.807720255851746}, {'batch_size': 12, 'throughput': 2.0253462646842415, 'latency_mean': 5.8597065711021425, 'latency_p50': 5.9142584800720215, 'latency_p90': 6.807666301727295}, {'batch_size': 15, 'throughput': 1.974804228407627, 'latency_mean': 7.435064491033554, 'latency_p50': 7.510664582252502, 'latency_p90': 8.209447622299194}]
max_input_tokens: 512
max_output_tokens: 64
model_architecture: LlamaForCausalLM
model_group: Riverise/feedback-retry-
model_name: riverise-feedback-retry-6k_v1
model_num_parameters: 8030261248.0
model_repo: Riverise/feedback-retry-6k
model_size: 8B
num_battles: 13444
num_wins: 7013
ranking_group: single
status: torndown
submission_type: basic
throughput_3p7s: 1.96
timestamp: 2024-09-03T04:46:56+00:00
us_pacific_date: 2024-09-02
win_ratio: 0.5216453436477239
Resubmit model
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLizer
Starting job with name riverise-feedback-retry-6k-v1-mkmlizer
Waiting for job on riverise-feedback-retry-6k-v1-mkmlizer to finish
riverise-feedback-retry-6k-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
riverise-feedback-retry-6k-v1-mkmlizer: ║     _____            __           __                                ║
riverise-feedback-retry-6k-v1-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
riverise-feedback-retry-6k-v1-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
riverise-feedback-retry-6k-v1-mkmlizer: ║       /___/                                                         ║
riverise-feedback-retry-6k-v1-mkmlizer: ║                                                                     ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  Version: 0.10.1                                                    ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  https://mk1.ai                                                     ║
riverise-feedback-retry-6k-v1-mkmlizer: ║                                                                     ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  belonging to:                                                      ║
riverise-feedback-retry-6k-v1-mkmlizer: ║                                                                     ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  Chai Research Corp.                                                ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
riverise-feedback-retry-6k-v1-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
riverise-feedback-retry-6k-v1-mkmlizer: ║                                                                     ║
riverise-feedback-retry-6k-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
riverise-feedback-retry-6k-v1-mkmlizer: Downloaded to shared memory in 35.779s
riverise-feedback-retry-6k-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpoyh7edez, device:0
riverise-feedback-retry-6k-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
riverise-feedback-retry-6k-v1-mkmlizer: quantized model in 25.895s
riverise-feedback-retry-6k-v1-mkmlizer: Processed model Riverise/feedback-retry-6k in 61.675s
riverise-feedback-retry-6k-v1-mkmlizer: creating bucket guanaco-mkml-models
riverise-feedback-retry-6k-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
riverise-feedback-retry-6k-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/riverise-feedback-retry-6k-v1
riverise-feedback-retry-6k-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/riverise-feedback-retry-6k-v1/config.json
riverise-feedback-retry-6k-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/riverise-feedback-retry-6k-v1/tokenizer_config.json
riverise-feedback-retry-6k-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/riverise-feedback-retry-6k-v1/special_tokens_map.json
riverise-feedback-retry-6k-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/riverise-feedback-retry-6k-v1/tokenizer.json
riverise-feedback-retry-6k-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/riverise-feedback-retry-6k-v1/flywheel_model.0.safetensors
riverise-feedback-retry-6k-v1-mkmlizer: 
Loading 0:   0%|          | 0/291 [00:00<?, ?it/s]
Loading 0:   2%|▏         | 7/291 [00:00<00:05, 53.43it/s]
Loading 0:   8%|▊         | 22/291 [00:00<00:03, 86.73it/s]
Loading 0:  11%|█         | 32/291 [00:00<00:02, 91.59it/s]
Loading 0:  14%|█▍        | 42/291 [00:00<00:02, 94.39it/s]
Loading 0:  18%|█▊        | 52/291 [00:00<00:03, 76.65it/s]
Loading 0:  21%|██        | 61/291 [00:00<00:02, 79.88it/s]
Loading 0:  24%|██▍       | 70/291 [00:00<00:02, 82.34it/s]
Loading 0:  27%|██▋       | 79/291 [00:00<00:02, 81.47it/s]
Loading 0:  30%|███       | 88/291 [00:02<00:10, 19.09it/s]
Loading 0:  32%|███▏      | 94/291 [00:02<00:08, 22.40it/s]
Loading 0:  34%|███▍      | 100/291 [00:02<00:07, 26.21it/s]
Loading 0:  36%|███▋      | 106/291 [00:02<00:06, 29.88it/s]
Loading 0:  40%|███▉      | 115/291 [00:02<00:04, 38.07it/s]
Loading 0:  43%|████▎     | 124/291 [00:02<00:03, 46.68it/s]
Loading 0:  48%|████▊     | 139/291 [00:02<00:02, 62.47it/s]
Loading 0:  52%|█████▏    | 150/291 [00:03<00:01, 72.24it/s]
Loading 0:  55%|█████▍    | 160/291 [00:03<00:01, 70.86it/s]
Loading 0:  58%|█████▊    | 169/291 [00:03<00:01, 70.76it/s]
Loading 0:  61%|██████    | 178/291 [00:03<00:01, 71.48it/s]
Loading 0:  64%|██████▍   | 187/291 [00:04<00:04, 22.14it/s]
Loading 0:  69%|██████▉   | 202/291 [00:04<00:02, 32.51it/s]
Loading 0:  73%|███████▎  | 211/291 [00:04<00:02, 38.81it/s]
Loading 0:  76%|███████▌  | 220/291 [00:04<00:01, 45.31it/s]
Loading 0:  79%|███████▊  | 229/291 [00:05<00:01, 50.42it/s]
Loading 0:  82%|████████▏ | 238/291 [00:05<00:00, 57.12it/s]
Loading 0:  85%|████████▍ | 247/291 [00:05<00:00, 63.42it/s]
Loading 0:  89%|████████▉ | 259/291 [00:05<00:00, 67.96it/s]
Loading 0:  92%|█████████▏| 268/291 [00:05<00:00, 70.51it/s]
Loading 0:  95%|█████████▌| 277/291 [00:05<00:00, 70.89it/s]
Loading 0:  99%|█████████▊| 287/291 [00:06<00:00, 43.47it/s]
                                                            

Job riverise-feedback-retry-6k-v1-mkmlizer completed after 84.67s with status: succeeded
Stopping job with name riverise-feedback-retry-6k-v1-mkmlizer
Pipeline stage MKMLizer completed in 86.15s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.09s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service riverise-feedback-retry-6k-v1
Waiting for inference service riverise-feedback-retry-6k-v1 to be ready
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Inference service riverise-feedback-retry-6k-v1 ready after 140.6791214942932s
Pipeline stage MKMLDeployer completed in 141.47s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.055616617202759s
Received healthy response to inference request in 1.6112868785858154s
Received healthy response to inference request in 1.5287106037139893s
Received healthy response to inference request in 1.4489333629608154s
Received healthy response to inference request in 1.7458908557891846s
5 requests
0 failed requests
5th percentile: 1.4648888111114502
10th percentile: 1.480844259262085
20th percentile: 1.5127551555633545
30th percentile: 1.5452258586883545
40th percentile: 1.578256368637085
50th percentile: 1.6112868785858154
60th percentile: 1.665128469467163
70th percentile: 1.7189700603485107
80th percentile: 1.8078360080718994
90th percentile: 1.9317263126373292
95th percentile: 1.993671464920044
99th percentile: 2.043227586746216
mean time: 1.6780876636505127
Pipeline stage StressChecker completed in 9.26s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
starting trigger_guanaco_pipeline %s
Pipeline stage TriggerMKMLProfilingPipeline completed in 5.69s
riverise-feedback-retry-6k_v1 status is now deployed due to DeploymentManager action
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.17s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.11s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service riverise-feedback-retry-6k-v1-profiler
Waiting for inference service riverise-feedback-retry-6k-v1-profiler to be ready
Inference service riverise-feedback-retry-6k-v1-profiler ready after 150.40553307533264s
Pipeline stage MKMLProfilerDeployer completed in 150.87s
run pipeline stage %s
Running pipeline stage MKMLProfilerRunner
kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/riverise-feedback-re3b07a1e90680bd42b8e8204a3bd6bca8-deplojg9bg:/code/chaiverse_profiler_1725339252 --namespace tenant-chaiml-guanaco
kubectl exec -it riverise-feedback-re3b07a1e90680bd42b8e8204a3bd6bca8-deplojg9bg --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1725339252 && chmod +x profiles.py && python profiles.py profile --best_of_n 16 --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 512 --output_tokens 64 --summary /code/chaiverse_profiler_1725339252/summary.json'
riverise-feedback-retry-6k_v1 status is now inactive due to auto deactivation removed underperforming models
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Checking if service riverise-feedback-retry-6k-v1-profiler is running
Skipping teardown as no inference service was found
Pipeline stage MKMLProfilerDeleter completed in 1.66s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.10s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service riverise-feedback-retry-6k-v1-profiler
Waiting for inference service riverise-feedback-retry-6k-v1-profiler to be ready
Inference service riverise-feedback-retry-6k-v1-profiler ready after 140.32620549201965s
Pipeline stage MKMLProfilerDeployer completed in 140.62s
run pipeline stage %s
Running pipeline stage MKMLProfilerRunner
kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/riverise-feedback-re3b07a1e90680bd42b8e8204a3bd6bca8-deplof5d2q:/code/chaiverse_profiler_1725502269 --namespace tenant-chaiml-guanaco
kubectl exec -it riverise-feedback-re3b07a1e90680bd42b8e8204a3bd6bca8-deplof5d2q --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1725502269 && python profiles.py profile --best_of_n 16 --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 512 --output_tokens 64 --summary /code/chaiverse_profiler_1725502269/summary.json'
kubectl exec -it riverise-feedback-re3b07a1e90680bd42b8e8204a3bd6bca8-deplof5d2q --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1725502269/summary.json'
Pipeline stage MKMLProfilerRunner completed in 853.46s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Checking if service riverise-feedback-retry-6k-v1-profiler is running
Tearing down inference service riverise-feedback-retry-6k-v1-profiler
Service riverise-feedback-retry-6k-v1-profiler has been torndown
Pipeline stage MKMLProfilerDeleter completed in 1.82s
Shutdown handler de-registered
riverise-feedback-retry-6k_v1 status is now torndown due to DeploymentManager action