riverise-feedback-retry-6k

developer_uid: Riverise

submission_id: riverise-feedback-retry-6k_v1

model_name: riverise-feedback-retry-6k_v1

model_group: Riverise/feedback-retry-

status: torndown

timestamp: 2024-09-03T04:46:56+00:00

num_battles: 13444

num_wins: 7013

celo_rating: 1256.41

family_friendly_score: 0.0

submission_type: basic

model_repo: Riverise/feedback-retry-6k

model_architecture: LlamaForCausalLM

model_num_parameters: 8030261248.0

best_of: 16

max_input_tokens: 512

max_output_tokens: 64

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}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: riverise-feedback-retry-6k_v1

is_internal_developer: False

language_model: Riverise/feedback-retry-6k

model_size: 8B

ranking_group: single

throughput_3p7s: 1.96

us_pacific_date: 2024-09-02

win_ratio: 0.5216453436477239

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}

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

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: 
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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
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Pipeline stage MKMLTemplater completed in 0.09s
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Creating inference service riverise-feedback-retry-6k-v1
Waiting for inference service riverise-feedback-retry-6k-v1 to be ready
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Inference service riverise-feedback-retry-6k-v1 ready after 140.6791214942932s
Pipeline stage MKMLDeployer completed in 141.47s
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Received healthy response to inference request in 2.055616617202759s
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Received healthy response to inference request in 1.4489333629608154s
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mean time: 1.6780876636505127
Pipeline stage StressChecker completed in 9.26s
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riverise-feedback-retry-6k_v1 status is now deployed due to DeploymentManager action
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Inference service riverise-feedback-retry-6k-v1-profiler ready after 150.40553307533264s
Pipeline stage MKMLProfilerDeployer completed in 150.87s
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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
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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
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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
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Checking if service riverise-feedback-retry-6k-v1-profiler is running
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Service riverise-feedback-retry-6k-v1-profiler has been torndown
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Shutdown handler de-registered
riverise-feedback-retry-6k_v1 status is now torndown due to DeploymentManager action