developer_uid: azuruce
submission_id: chaiml-mistral-nemo-dpo-_1624_v1
model_name: chaiml-mistral-nemo-dpo-_1624_v1
model_group: ChaiML/mistral_nemo_dpo_
status: inactive
timestamp: 2024-12-20T23:15:57+00:00
num_battles: 16638
num_wins: 7816
celo_rating: 1227.8
family_friendly_score: 0.5993999999999999
family_friendly_standard_error: 0.006929929869775018
submission_type: basic
model_repo: ChaiML/mistral_nemo_dpo_round2_albert_20241220_v1-checkpoint-1173
model_architecture: MistralForCausalLM
model_num_parameters: 12772070400.0
best_of: 4
max_input_tokens: 1024
max_output_tokens: 64
latencies: [{'batch_size': 1, 'throughput': 0.6259011459599916, 'latency_mean': 1.5976035106182098, 'latency_p50': 1.6040171384811401, 'latency_p90': 1.7554962635040283}, {'batch_size': 3, 'throughput': 1.265448453069336, 'latency_mean': 2.3677747881412508, 'latency_p50': 2.368301749229431, 'latency_p90': 2.603839087486267}, {'batch_size': 5, 'throughput': 1.582611426427754, 'latency_mean': 3.131751685142517, 'latency_p50': 3.1514732837677, 'latency_p90': 3.530503273010254}, {'batch_size': 6, 'throughput': 1.722058747413812, 'latency_mean': 3.468840472698212, 'latency_p50': 3.4765560626983643, 'latency_p90': 3.9060657024383545}, {'batch_size': 8, 'throughput': 1.8571238330921431, 'latency_mean': 4.278435424566269, 'latency_p50': 4.28927481174469, 'latency_p90': 4.840352177619934}, {'batch_size': 10, 'throughput': 1.9453610687207894, 'latency_mean': 5.097476208209992, 'latency_p50': 5.1406004428863525, 'latency_p90': 5.756405758857727}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: chaiml-mistral-nemo-dpo-_1624_v1
is_internal_developer: True
language_model: ChaiML/mistral_nemo_dpo_round2_albert_20241220_v1-checkpoint-1173
model_size: 13B
ranking_group: single
throughput_3p7s: 1.78
us_pacific_date: 2024-12-20
win_ratio: 0.46976800096165405
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-1624-v1-mkmlizer
Waiting for job on chaiml-mistral-nemo-dpo-1624-v1-mkmlizer to finish
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ _____ __ __ ║
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chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ /___/ ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ Version: 0.11.12 ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ https://mk1.ai ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ The license key for the current software has been verified as ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ belonging to: ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ Chai Research Corp. ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ║ Expiration: 2025-01-15 23:59:59 ║
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chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
Connection pool is full, discarding connection: %s. Connection pool size: %s
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: quantized model in 36.166s
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: Processed model ChaiML/mistral_nemo_dpo_round2_albert_20241220_v1-checkpoint-1173 in 88.893s
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: creating bucket guanaco-mkml-models
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1624-v1
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1624-v1/config.json
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1624-v1/special_tokens_map.json
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1624-v1/tokenizer_config.json
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1624-v1/tokenizer.json
chaiml-mistral-nemo-dpo-1624-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/chaiml-mistral-nemo-dpo-1624-v1/flywheel_model.0.safetensors
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Job chaiml-mistral-nemo-dpo-1624-v1-mkmlizer completed after 114.26s with status: succeeded
Stopping job with name chaiml-mistral-nemo-dpo-1624-v1-mkmlizer
Pipeline stage MKMLizer completed in 114.71s
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Inference service chaiml-mistral-nemo-dpo-1624-v1 ready after 261.577273607254s
Pipeline stage MKMLDeployer completed in 262.10s
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Received healthy response to inference request in 1.1592450141906738s
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5th percentile: 0.9777348518371582
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20th percentile: 1.1138674736022949
30th percentile: 1.1992201328277587
40th percentile: 1.2791703701019288
50th percentile: 1.3591206073760986
60th percentile: 1.5969634532928465
70th percentile: 1.8348062992095946
80th percentile: 2.0133886337280273
90th percentile: 2.1327104568481445
95th percentile: 2.192371368408203
99th percentile: 2.24010009765625
mean time: 1.5312965869903565
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chaiml-mistral-nemo-dpo-_1624_v1 status is now inactive due to auto deactivation removed underperforming models