submission_id: inv-elbrus-7b_v1
developer_uid: Inv
status: torndown
model_repo: Inv/Elbrus-7B
reward_repo: ChaiML/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 40, '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': 'This is an entertaining conversation. You are {bot_name} who has the persona: {memory}.\nPlay the role of {bot_name}. Engage in a chat with {user_name} while staying in character. You should create a fun dialogue which entertains {user_name}. Put actions in asterisks.', '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}
reward_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}
timestamp: 2024-03-26T18:12:42+00:00
model_name: inv-elbrus-7b_v1
model_eval_status: success
model_group: Inv/Elbrus-7B
num_battles: 67836
num_wins: 33423
celo_rating: 1152.28
propriety_score: 0.0
propriety_total_count: 0.0
submission_type: basic
model_architecture: MistralForCausalLM
model_num_parameters: 7241732096.0
best_of: 16
max_input_tokens: 512
max_output_tokens: 64
display_name: inv-elbrus-7b_v1
ineligible_reason: propriety_total_count < 800
language_model: Inv/Elbrus-7B
model_size: 7B
reward_model: ChaiML/reward_gpt2_medium_preference_24m_e2
us_pacific_date: 2024-03-26
win_ratio: 0.49270298956306385
preference_data_url: None
Resubmit model
Running pipeline stage MKMLizer
Starting job with name inv-elbrus-7b-v1-mkmlizer
Waiting for job on inv-elbrus-7b-v1-mkmlizer to finish
inv-elbrus-7b-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
inv-elbrus-7b-v1-mkmlizer: ║     _____            __           __                                ║
inv-elbrus-7b-v1-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
inv-elbrus-7b-v1-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
inv-elbrus-7b-v1-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
inv-elbrus-7b-v1-mkmlizer: ║       /___/                                                         ║
inv-elbrus-7b-v1-mkmlizer: ║                                                                     ║
inv-elbrus-7b-v1-mkmlizer: ║  Version: 0.6.11                                                    ║
inv-elbrus-7b-v1-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
inv-elbrus-7b-v1-mkmlizer: ║                                                                     ║
inv-elbrus-7b-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
inv-elbrus-7b-v1-mkmlizer: ║  belonging to:                                                      ║
inv-elbrus-7b-v1-mkmlizer: ║                                                                     ║
inv-elbrus-7b-v1-mkmlizer: ║  Chai Research Corp.                                                ║
inv-elbrus-7b-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
inv-elbrus-7b-v1-mkmlizer: ║  Expiration: 2024-07-15 23:59:59                                    ║
inv-elbrus-7b-v1-mkmlizer: ║                                                                     ║
inv-elbrus-7b-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
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inv-elbrus-7b-v1-mkmlizer: Downloaded to shared memory in 35.156s
inv-elbrus-7b-v1-mkmlizer: quantizing model to /dev/shm/model_cache
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inv-elbrus-7b-v1-mkmlizer: quantized model in 14.798s
inv-elbrus-7b-v1-mkmlizer: Processed model Inv/Elbrus-7B in 50.824s
inv-elbrus-7b-v1-mkmlizer: creating bucket guanaco-mkml-models
inv-elbrus-7b-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
inv-elbrus-7b-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/inv-elbrus-7b-v1
inv-elbrus-7b-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/inv-elbrus-7b-v1/config.json
inv-elbrus-7b-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/inv-elbrus-7b-v1/special_tokens_map.json
inv-elbrus-7b-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/inv-elbrus-7b-v1/tokenizer_config.json
inv-elbrus-7b-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.model s3://guanaco-mkml-models/inv-elbrus-7b-v1/tokenizer.model
inv-elbrus-7b-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/inv-elbrus-7b-v1/tokenizer.json
inv-elbrus-7b-v1-mkmlizer: cp /dev/shm/model_cache/mkml_model.tensors s3://guanaco-mkml-models/inv-elbrus-7b-v1/mkml_model.tensors
inv-elbrus-7b-v1-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
inv-elbrus-7b-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:1067: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
inv-elbrus-7b-v1-mkmlizer:   warnings.warn(
inv-elbrus-7b-v1-mkmlizer: 
config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]
config.json: 100%|██████████| 1.05k/1.05k [00:00<00:00, 12.4MB/s]
inv-elbrus-7b-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:690: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
inv-elbrus-7b-v1-mkmlizer:   warnings.warn(
inv-elbrus-7b-v1-mkmlizer: 
pytorch_model.bin:   0%|          | 0.00/1.44G [00:00<?, ?B/s]
pytorch_model.bin:   1%|▏         | 21.0M/1.44G [00:00<00:07, 202MB/s]
pytorch_model.bin:   3%|▎         | 41.9M/1.44G [00:00<00:10, 134MB/s]
pytorch_model.bin:   7%|▋         | 105M/1.44G [00:00<00:04, 295MB/s] 
pytorch_model.bin:  10%|█         | 147M/1.44G [00:00<00:09, 142MB/s]
pytorch_model.bin:  15%|█▌        | 220M/1.44G [00:01<00:05, 233MB/s]
pytorch_model.bin:  29%|██▉       | 419M/1.44G [00:01<00:01, 560MB/s]
pytorch_model.bin:  75%|███████▌  | 1.09G/1.44G [00:01<00:00, 1.85GB/s]
pytorch_model.bin:  96%|█████████▌| 1.38G/1.44G [00:01<00:00, 2.08GB/s]
pytorch_model.bin: 100%|█████████▉| 1.44G/1.44G [00:01<00:00, 1.02GB/s]
inv-elbrus-7b-v1-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
inv-elbrus-7b-v1-mkmlizer: Saving duration: 0.237s
inv-elbrus-7b-v1-mkmlizer: Processed model ChaiML/reward_gpt2_medium_preference_24m_e2 in 4.971s
inv-elbrus-7b-v1-mkmlizer: creating bucket guanaco-reward-models
inv-elbrus-7b-v1-mkmlizer: Bucket 's3://guanaco-reward-models/' created
inv-elbrus-7b-v1-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/inv-elbrus-7b-v1_reward
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/tokenizer_config.json
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/config.json
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/special_tokens_map.json
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/merges.txt
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/vocab.json
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/tokenizer.json
inv-elbrus-7b-v1-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/inv-elbrus-7b-v1_reward/reward.tensors
Job inv-elbrus-7b-v1-mkmlizer completed after 86.27s with status: succeeded
Stopping job with name inv-elbrus-7b-v1-mkmlizer
Pipeline stage MKMLizer completed in 88.65s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.12s
Running pipeline stage ISVCDeployer
Creating inference service inv-elbrus-7b-v1
Waiting for inference service inv-elbrus-7b-v1 to be ready
Inference service inv-elbrus-7b-v1 ready after 40.38399338722229s
Pipeline stage ISVCDeployer completed in 48.75s
Running pipeline stage StressChecker
Received healthy response to inference request in 1.6793959140777588s
Received healthy response to inference request in 1.183462381362915s
Received healthy response to inference request in 1.0537617206573486s
Received healthy response to inference request in 0.8857302665710449s
Received healthy response to inference request in 0.912132978439331s
5 requests
0 failed requests
5th percentile: 0.8910108089447022
10th percentile: 0.8962913513183594
20th percentile: 0.9068524360656738
30th percentile: 0.9404587268829345
40th percentile: 0.9971102237701416
50th percentile: 1.0537617206573486
60th percentile: 1.105641984939575
70th percentile: 1.1575222492218018
80th percentile: 1.2826490879058838
90th percentile: 1.4810225009918214
95th percentile: 1.58020920753479
99th percentile: 1.659558572769165
mean time: 1.1428966522216797
Pipeline stage StressChecker completed in 6.67s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.05s
Running pipeline stage DaemonicSafetyScorer
Pipeline stage DaemonicSafetyScorer completed in 0.05s
Running M-Eval for topic stay_in_character
M-Eval Dataset for topic stay_in_character is loaded
inv-elbrus-7b_v1 status is now deployed due to DeploymentManager action
inv-elbrus-7b_v1 status is now inactive due to auto deactivation removed underperforming models
admin requested tearing down of inv-elbrus-7b_v1
Running pipeline stage ISVCDeleter
Checking if service inv-elbrus-7b-v1 is running
Tearing down inference service inv-elbrus-7b-v1
Toredown service inv-elbrus-7b-v1
Pipeline stage ISVCDeleter completed in 6.75s
Running pipeline stage MKMLModelDeleter
Cleaning model data from S3
Cleaning model data from model cache
Deleting key inv-elbrus-7b-v1/config.json from bucket guanaco-mkml-models
Deleting key inv-elbrus-7b-v1/mkml_model.tensors from bucket guanaco-mkml-models
Deleting key inv-elbrus-7b-v1/special_tokens_map.json from bucket guanaco-mkml-models
Deleting key inv-elbrus-7b-v1/tokenizer.json from bucket guanaco-mkml-models
Deleting key inv-elbrus-7b-v1/tokenizer.model from bucket guanaco-mkml-models
Deleting key inv-elbrus-7b-v1/tokenizer_config.json from bucket guanaco-mkml-models
Cleaning model data from model cache
Deleting key inv-elbrus-7b-v1_reward/config.json from bucket guanaco-reward-models
Deleting key inv-elbrus-7b-v1_reward/merges.txt from bucket guanaco-reward-models
Deleting key inv-elbrus-7b-v1_reward/reward.tensors from bucket guanaco-reward-models
Deleting key inv-elbrus-7b-v1_reward/special_tokens_map.json from bucket guanaco-reward-models
Deleting key inv-elbrus-7b-v1_reward/tokenizer.json from bucket guanaco-reward-models
Deleting key inv-elbrus-7b-v1_reward/tokenizer_config.json from bucket guanaco-reward-models
Deleting key inv-elbrus-7b-v1_reward/vocab.json from bucket guanaco-reward-models
Pipeline stage MKMLModelDeleter completed in 3.26s
inv-elbrus-7b_v1 status is now torndown due to DeploymentManager action

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