chaiml-mistral-nemo-dpo-_1412

developer_uid: azuruce

submission_id: chaiml-mistral-nemo-dpo-_1412_v1

model_name: chaiml-mistral-nemo-dpo-_1412_v1

model_group: ChaiML/mistral_nemo_dpo_

status: inactive

timestamp: 2024-12-19T09:19:18+00:00

num_battles: 10978

num_wins: 5449

celo_rating: 1260.67

family_friendly_score: 0.5740000000000001

family_friendly_standard_error: 0.006993196693930466

submission_type: basic

model_repo: ChaiML/mistral_nemo_dpo_241217_albert_v1-checkpoint-4992

model_architecture: MistralForCausalLM

model_num_parameters: 12772070400.0

best_of: 4

max_input_tokens: 1024

max_output_tokens: 64

latencies: [{'batch_size': 1, 'throughput': 0.6361136142935732, 'latency_mean': 1.5719868195056916, 'latency_p50': 1.587268352508545, 'latency_p90': 1.7229485988616944}, {'batch_size': 4, 'throughput': 1.4908015926919536, 'latency_mean': 2.67411395072937, 'latency_p50': 2.6823049783706665, 'latency_p90': 3.037242555618286}, {'batch_size': 5, 'throughput': 1.6192845451919124, 'latency_mean': 3.0648150098323823, 'latency_p50': 3.070492148399353, 'latency_p90': 3.4578431844711304}, {'batch_size': 8, 'throughput': 1.9039793095574225, 'latency_mean': 4.166520988941192, 'latency_p50': 4.16307258605957, 'latency_p90': 4.731259393692016}, {'batch_size': 10, 'throughput': 1.994859931506973, 'latency_mean': 4.971020897626877, 'latency_p50': 4.977362513542175, 'latency_p90': 5.630859160423279}, {'batch_size': 12, 'throughput': 2.050775300796409, 'latency_mean': 5.78318354845047, 'latency_p50': 5.810768961906433, 'latency_p90': 6.542755341529846}, {'batch_size': 15, 'throughput': 2.11542792221983, 'latency_mean': 7.021050828695297, 'latency_p50': 7.065522193908691, 'latency_p90': 7.930132579803467}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: chaiml-mistral-nemo-dpo-_1412_v1

is_internal_developer: True

language_model: ChaiML/mistral_nemo_dpo_241217_albert_v1-checkpoint-4992

model_size: 13B

ranking_group: single

throughput_3p7s: 1.82

us_pacific_date: 2024-12-19

win_ratio: 0.4963563490617599

generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 100, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n', '</s>', '####', 'Bot:', 'User:', 'You:', '<|im_end|>', '<|eot_id|>'], 'max_input_tokens': 1024, 'best_of': 4, 'max_output_tokens': 64}

formatter: {'memory_template': '', 'prompt_template': '', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '{bot_name}:', 'truncate_by_message': False}

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 chaiml-mistral-nemo-dpo-1412-v1-mkmlizer
Waiting for job on chaiml-mistral-nemo-dpo-1412-v1-mkmlizer to finish
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║     _____            __           __                                ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║       /___/                                                         ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║                                                                     ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  Version: 0.11.12                                                   ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  https://mk1.ai                                                     ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║                                                                     ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  belonging to:                                                      ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║                                                                     ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  Chai Research Corp.                                                ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║  Expiration: 2025-01-15 23:59:59                                    ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ║                                                                     ║
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: Downloaded to shared memory in 42.044s
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpqzbgflk6, device:0
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: quantized model in 36.017s
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: Processed model ChaiML/mistral_nemo_dpo_241217_albert_v1-checkpoint-4992 in 78.062s
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: creating bucket guanaco-mkml-models
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1412-v1
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1412-v1/config.json
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1412-v1/special_tokens_map.json
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1412-v1/tokenizer_config.json
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1412-v1/tokenizer.json
chaiml-mistral-nemo-dpo-1412-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1412-v1/flywheel_model.0.safetensors
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Job chaiml-mistral-nemo-dpo-1412-v1-mkmlizer completed after 104.86s with status: succeeded
Stopping job with name chaiml-mistral-nemo-dpo-1412-v1-mkmlizer
Pipeline stage MKMLizer completed in 105.42s
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Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.19s
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Running pipeline stage MKMLDeployer
Creating inference service chaiml-mistral-nemo-dpo-1412-v1
Waiting for inference service chaiml-mistral-nemo-dpo-1412-v1 to be ready
Inference service chaiml-mistral-nemo-dpo-1412-v1 ready after 250.93353533744812s
Pipeline stage MKMLDeployer completed in 251.56s
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Running pipeline stage StressChecker
Received healthy response to inference request in 1.4392249584197998s
Received healthy response to inference request in 1.436182975769043s
Received healthy response to inference request in 1.2616033554077148s
Received healthy response to inference request in 1.1189606189727783s
Received healthy response to inference request in 0.6595988273620605s
5 requests
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5th percentile: 0.7514711856842041
10th percentile: 0.8433435440063477
20th percentile: 1.0270882606506349
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70th percentile: 1.4012670516967773
80th percentile: 1.4367913722991943
90th percentile: 1.4380081653594972
95th percentile: 1.4386165618896485
99th percentile: 1.4391032791137695
mean time: 1.1831141471862794
Pipeline stage StressChecker completed in 7.44s
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Evaluating %s Family Friendly Score with %s threads
Pipeline stage OfflineFamilyFriendlyScorer completed in 2910.95s
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chaiml-mistral-nemo-dpo-_1412_v1 status is now inactive due to auto deactivation removed underperforming models