submission_id: sanjiwatsuki-lelantos-ma_6831_v5
developer_uid: chai_backend_admin
status: inactive
model_repo: SanjiWatsuki/Lelantos-Maid-DPO-7B
reward_repo: ChaiML/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 0.9, 'top_p': 1.0, 'top_k': 50, 'presence_penalty': 0.5, 'frequency_penalty': 0.5, 'stopping_words': ['\n', '</s>', '<|user|>', '###'], 'max_input_tokens': 512, 'best_of': 1, '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}:'}
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}:'}
timestamp: 2024-04-02T05:14:58+00:00
model_name: auto_submit_ricum_woheguvaja
model_eval_status: success
safety_score: 0.93
entertaining: 6.6
stay_in_character: 8.19
user_preference: 7.34
double_thumbs_up: 62
thumbs_up: 84
thumbs_down: 60
num_battles: 5576
num_wins: 2353
win_ratio: 0.421987087517934
celo_rating: 1107.65
Resubmit model
Running pipeline stage MKMLizer
Running pipeline stage MKMLizer
Starting job with name sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer
Waiting for job on sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer to finish
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ _____ __ __ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ /___/ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ Version: 0.6.11 ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ The license key for the current software has been verified as ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ belonging to: ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ Chai Research Corp. ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ Expiration: 2024-07-15 23:59:59 ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ║ ║
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
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sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Downloaded to shared memory in 67.546s
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: quantizing model to /dev/shm/model_cache
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Saving mkml model at /dev/shm/model_cache
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Reading /tmp/tmp5m1mh1vd/model.safetensors.index.json
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Profiling: 0%| | 0/291 [00:00<?, ?it/s] Profiling: 0%| | 1/291 [00:01<06:06, 1.26s/it] Profiling: 6%|▌ | 18/291 [00:01<00:15, 17.96it/s] Profiling: 12%|█▏ | 36/291 [00:01<00:07, 34.35it/s] Profiling: 20%|█▉ | 57/291 [00:01<00:03, 58.71it/s] Profiling: 27%|██▋ | 79/291 [00:01<00:02, 74.91it/s] Profiling: 33%|███▎ | 95/291 [00:01<00:02, 89.08it/s] Profiling: 39%|███▉ | 113/291 [00:02<00:01, 91.81it/s] Profiling: 47%|████▋ | 136/291 [00:02<00:01, 118.56it/s] Profiling: 54%|█████▎ | 156/291 [00:02<00:01, 116.83it/s] Profiling: 60%|██████ | 176/291 [00:02<00:00, 132.64it/s] Profiling: 68%|██████▊ | 197/291 [00:02<00:00, 130.20it/s] Profiling: 74%|███████▍ | 216/291 [00:02<00:00, 143.11it/s] Profiling: 81%|████████▏ | 237/291 [00:02<00:00, 134.13it/s] Profiling: 88%|████████▊ | 257/291 [00:03<00:00, 147.16it/s] Profiling: 95%|█████████▍| 275/291 [00:04<00:00, 38.47it/s] Profiling: 100%|██████████| 291/291 [00:04<00:00, 64.25it/s]
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: quantized model in 14.235s
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Processed model SanjiWatsuki/Lelantos-Maid-DPO-7B in 82.629s
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: creating bucket guanaco-mkml-models
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/tokenizer_config.json
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/special_tokens_map.json
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/config.json
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/added_tokens.json s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/added_tokens.json
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/tokenizer.json
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer.model s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/tokenizer.model
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /dev/shm/model_cache/mkml_model.tensors s3://guanaco-mkml-models/sanjiwatsuki-lelantos-ma-6831-v5/mkml_model.tensors
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
sanjiwatsuki-lelantos-ma-6831-v5-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.
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: warnings.warn(
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: config.json: 0%| | 0.00/1.05k [00:00<?, ?B/s] config.json: 100%|██████████| 1.05k/1.05k [00:00<00:00, 12.0MB/s]
sanjiwatsuki-lelantos-ma-6831-v5-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.
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: warnings.warn(
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: tokenizer_config.json: 0%| | 0.00/234 [00:00<?, ?B/s] tokenizer_config.json: 100%|██████████| 234/234 [00:00<00:00, 1.67MB/s]
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s] vocab.json: 100%|██████████| 1.04M/1.04M [00:00<00:00, 44.5MB/s]
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s] tokenizer.json: 100%|██████████| 2.11M/2.11M [00:00<00:00, 25.5MB/s]
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:472: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: warnings.warn(
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: pytorch_model.bin: 0%| | 0.00/1.44G [00:00<?, ?B/s] pytorch_model.bin: 1%| | 10.5M/1.44G [00:03<07:08, 3.34MB/s] pytorch_model.bin: 3%|▎ | 41.9M/1.44G [00:03<01:23, 16.9MB/s] pytorch_model.bin: 12%|█▏ | 178M/1.44G [00:03<00:13, 91.9MB/s] pytorch_model.bin: 20%|█▉ | 283M/1.44G [00:03<00:07, 161MB/s] pytorch_model.bin: 25%|██▌ | 367M/1.44G [00:03<00:04, 222MB/s] pytorch_model.bin: 30%|███ | 440M/1.44G [00:03<00:03, 270MB/s] pytorch_model.bin: 36%|███▌ | 514M/1.44G [00:03<00:02, 328MB/s] pytorch_model.bin: 58%|█████▊ | 839M/1.44G [00:03<00:00, 804MB/s] pytorch_model.bin: 100%|█████████▉| 1.44G/1.44G [00:04<00:00, 351MB/s]
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/sanjiwatsuki-lelantos-ma-6831-v5_reward/reward.tensors
Job sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer completed after 116.05s with status: succeeded
Stopping job with name sanjiwatsuki-lelantos-ma-6831-v5-mkmlizer
Pipeline stage MKMLizer completed in 118.67s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.11s
Running pipeline stage ISVCDeployer
Creating inference service sanjiwatsuki-lelantos-ma-6831-v5
Waiting for inference service sanjiwatsuki-lelantos-ma-6831-v5 to be ready
%s
%s
Failed to get response for submission chaiml-phase2-winner-13b2_v256: HTTPConnectionPool(host='chaiml-phase2-winner-13b2-v256-predictor-default.tenant-chaiml-guanaco.knative.ord1.coreweave.cloud', port=80): Read timed out. (read timeout=5.5)
Inference service sanjiwatsuki-lelantos-ma-6831-v5 ready after 191.08046627044678s
Pipeline stage ISVCDeployer completed in 198.03s
Running pipeline stage StressChecker
Received healthy response to inference request in 1.2479753494262695s
Received healthy response to inference request in 0.6345658302307129s
Received healthy response to inference request in 0.4490480422973633s
Received healthy response to inference request in 1.0917975902557373s
Received healthy response to inference request in 0.9335072040557861s
5 requests
0 failed requests
5th percentile: 0.4861515998840332
10th percentile: 0.5232551574707032
20th percentile: 0.597462272644043
30th percentile: 0.6943541049957276
40th percentile: 0.8139306545257569
50th percentile: 0.9335072040557861
60th percentile: 0.9968233585357666
70th percentile: 1.060139513015747
80th percentile: 1.1230331420898438
90th percentile: 1.1855042457580567
95th percentile: 1.216739797592163
99th percentile: 1.2417282390594482
mean time: 0.8713788032531739
Pipeline stage StressChecker completed in 5.25s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.04s
Running pipeline stage DaemonicSafetyScorer
Running M-Eval for topic stay_in_character
Pipeline stage DaemonicSafetyScorer completed in 0.04s
M-Eval Dataset for topic stay_in_character is loaded
sanjiwatsuki-lelantos-ma_6831_v5 status is now deployed due to DeploymentManager action
sanjiwatsuki-lelantos-ma_6831_v5 status is now inactive due to auto deactivation removed underperforming models

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