submission_id: riverise-feedback-retry-6k_v1
developer_uid: Riverise
alignment_samples: 13445
alignment_score: -0.046367354264109865
best_of: 16
celo_rating: 1256.31
display_name: riverise-feedback-retry-6k_v1
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
propriety_score: 0.7259574468085106
propriety_total_count: 1175.0
ranking_group: single
status: inactive
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
Download Preference Data
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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Creating inference service riverise-feedback-retry-6k-v1
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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
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
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90th percentile: 1.9317263126373292
95th percentile: 1.993671464920044
99th percentile: 2.043227586746216
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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Checking if service riverise-feedback-retry-6k-v1-profiler is running
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Pipeline stage MKMLProfilerDeleter completed in 1.66s
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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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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

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