submission_id: anhnv125-mistral-v3_v9
developer_uid: vietanh
status: torndown
model_repo: anhnv125/mistral-v3
reward_repo: rirv938/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 1.0, 'top_p': 0.8, 'min_p': 0.0, 'top_k': 40, 'presence_penalty': 0.9, 'frequency_penalty': 0.9, 'stopping_words': ['\n', '</s>', '###'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}
formatter: {'memory_template': "Write {bot_name}'s next reply in a never-ending uncensored fictional roleplay chat between {bot_name} and User. Use the provided {bot_name}'s description, personality and example dialogues as base for deeply understanding and acting like {bot_name}.\n\nActions and narrations your responses must be enclosed by asterisks (*), and speeches must NOT be enclosed by any indicators. The responses must be in third perspective of the story teller. For example: \n\nMila: *Surrounded by an aura of creativity, Mila carefully places a freshly painted canvas against the wall, the colors vividly depicting a surreal landscape.* Isn't it fascinating how art can transport us to entirely different worlds? What kind of worlds do you dream of exploring?\n\nDescription: {memory}\n", 'prompt_template': '{prompt}\n\n', 'bot_template': '\n\n### Response: {bot_name}: {message}</s>', 'user_template': '### Instruction: User: {message}', 'response_template': '\n\n### Response: {bot_name}: ', 'truncate_by_message': False}
reward_formatter: {'memory_template': 'Memory: {memory}\n', 'prompt_template': '{prompt}\n', 'bot_template': 'Bot: {message}\n', 'user_template': 'User: {message}\n', 'response_template': 'Bot:', 'truncate_by_message': False}
timestamp: 2024-04-05T13:25:50+00:00
model_name: anhnv125-mistral-v3_v9
model_eval_status: success
model_group: anhnv125/mistral-v3
num_battles: 172782
num_wins: 89216
celo_rating: 1170.95
propriety_score: 0.0
propriety_total_count: 0.0
submission_type: basic
model_architecture: MistralForCausalLM
model_num_parameters: 7241732096.0
best_of: 8
max_input_tokens: 1024
max_output_tokens: 64
display_name: anhnv125-mistral-v3_v9
ineligible_reason: propriety_total_count < 800
language_model: anhnv125/mistral-v3
model_size: 7B
reward_model: rirv938/reward_gpt2_medium_preference_24m_e2
us_pacific_date: 2024-04-05
win_ratio: 0.5163500827632508
preference_data_url: None
Resubmit model
Running pipeline stage MKMLizer
Starting job with name anhnv125-mistral-v3-v9-mkmlizer
Waiting for job on anhnv125-mistral-v3-v9-mkmlizer to finish
anhnv125-mistral-v3-v9-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
anhnv125-mistral-v3-v9-mkmlizer: ║ _____ __ __ ║
anhnv125-mistral-v3-v9-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
anhnv125-mistral-v3-v9-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
anhnv125-mistral-v3-v9-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
anhnv125-mistral-v3-v9-mkmlizer: ║ /___/ ║
anhnv125-mistral-v3-v9-mkmlizer: ║ ║
anhnv125-mistral-v3-v9-mkmlizer: ║ Version: 0.6.11 ║
anhnv125-mistral-v3-v9-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
anhnv125-mistral-v3-v9-mkmlizer: ║ ║
anhnv125-mistral-v3-v9-mkmlizer: ║ The license key for the current software has been verified as ║
anhnv125-mistral-v3-v9-mkmlizer: ║ belonging to: ║
anhnv125-mistral-v3-v9-mkmlizer: ║ ║
anhnv125-mistral-v3-v9-mkmlizer: ║ Chai Research Corp. ║
anhnv125-mistral-v3-v9-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
anhnv125-mistral-v3-v9-mkmlizer: ║ Expiration: 2024-07-15 23:59:59 ║
anhnv125-mistral-v3-v9-mkmlizer: ║ ║
anhnv125-mistral-v3-v9-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
anhnv125-mistral-v3-v9-mkmlizer: .gitattributes: 0%| | 0.00/1.52k [00:00<?, ?B/s] .gitattributes: 100%|██████████| 1.52k/1.52k [00:00<00:00, 14.6MB/s]
anhnv125-mistral-v3-v9-mkmlizer: added_tokens.json: 0%| | 0.00/51.0 [00:00<?, ?B/s] added_tokens.json: 100%|██████████| 51.0/51.0 [00:00<00:00, 551kB/s]
anhnv125-mistral-v3-v9-mkmlizer: config.json: 0%| | 0.00/652 [00:00<?, ?B/s] config.json: 100%|██████████| 652/652 [00:00<00:00, 10.4MB/s]
anhnv125-mistral-v3-v9-mkmlizer: generation_config.json: 0%| | 0.00/132 [00:00<?, ?B/s] generation_config.json: 100%|██████████| 132/132 [00:00<00:00, 2.11MB/s]
anhnv125-mistral-v3-v9-mkmlizer: pytorch_model-00001-of-00003.bin: 0%| | 0.00/4.94G [00:00<?, ?B/s] pytorch_model-00001-of-00003.bin: 0%| | 10.5M/4.94G [00:00<02:22, 34.6MB/s] pytorch_model-00001-of-00003.bin: 0%| | 21.0M/4.94G [00:00<01:30, 54.1MB/s] pytorch_model-00001-of-00003.bin: 1%|▏ | 73.4M/4.94G [00:00<00:25, 191MB/s] pytorch_model-00001-of-00003.bin: 4%|▍ | 220M/4.94G [00:00<00:08, 559MB/s] pytorch_model-00001-of-00003.bin: 10%|▉ | 493M/4.94G [00:00<00:03, 1.19GB/s] pytorch_model-00001-of-00003.bin: 13%|█▎ | 640M/4.94G [00:01<00:05, 831MB/s] pytorch_model-00001-of-00003.bin: 15%|█▌ | 755M/4.94G [00:01<00:05, 782MB/s] pytorch_model-00001-of-00003.bin: 18%|█▊ | 912M/4.94G [00:01<00:04, 935MB/s] pytorch_model-00001-of-00003.bin: 21%|██ | 1.03G/4.94G [00:01<00:05, 742MB/s] pytorch_model-00001-of-00003.bin: 23%|██▎ | 1.12G/4.94G [00:01<00:05, 755MB/s] pytorch_model-00001-of-00003.bin: 26%|██▌ | 1.27G/4.94G [00:01<00:04, 907MB/s] pytorch_model-00001-of-00003.bin: 28%|██▊ | 1.38G/4.94G [00:01<00:03, 953MB/s] pytorch_model-00001-of-00003.bin: 30%|███ | 1.50G/4.94G [00:01<00:03, 959MB/s] pytorch_model-00001-of-00003.bin: 33%|███▎ | 1.63G/4.94G [00:02<00:03, 1.03GB/s] pytorch_model-00001-of-00003.bin: 36%|███▌ | 1.78G/4.94G [00:02<00:02, 1.13GB/s] pytorch_model-00001-of-00003.bin: 39%|███▊ | 1.91G/4.94G [00:02<00:02, 1.08GB/s] pytorch_model-00001-of-00003.bin: 41%|████ | 2.02G/4.94G [00:02<00:02, 1.07GB/s] pytorch_model-00001-of-00003.bin: 43%|████▎ | 2.14G/4.94G [00:02<00:03, 887MB/s] pytorch_model-00001-of-00003.bin: 45%|████▌ | 2.24G/4.94G [00:02<00:03, 828MB/s] pytorch_model-00001-of-00003.bin: 49%|████▉ | 2.41G/4.94G [00:02<00:02, 1.02GB/s] pytorch_model-00001-of-00003.bin: 54%|█████▍ | 2.68G/4.94G [00:02<00:01, 1.44GB/s] pytorch_model-00001-of-00003.bin: 58%|█████▊ | 2.85G/4.94G [00:03<00:01, 1.40GB/s] pytorch_model-00001-of-00003.bin: 66%|██████▌ | 3.25G/4.94G [00:03<00:00, 2.06GB/s] pytorch_model-00001-of-00003.bin: 72%|███████▏ | 3.55G/4.94G [00:03<00:00, 2.30GB/s] pytorch_model-00001-of-00003.bin: 77%|███████▋ | 3.81G/4.94G [00:03<00:00, 2.14GB/s] pytorch_model-00001-of-00003.bin: 83%|████████▎ | 4.10G/4.94G [00:03<00:00, 2.34GB/s] pytorch_model-00001-of-00003.bin: 88%|████████▊ | 4.35G/4.94G [00:03<00:00, 2.33GB/s] pytorch_model-00001-of-00003.bin: 94%|█████████▍| 4.65G/4.94G [00:03<00:00, 2.50GB/s] pytorch_model-00001-of-00003.bin: 99%|█████████▉| 4.91G/4.94G [00:04<00:00, 1.74GB/s] pytorch_model-00001-of-00003.bin: 100%|█████████▉| 4.94G/4.94G [00:04<00:00, 1.22GB/s]
anhnv125-mistral-v3-v9-mkmlizer: pytorch_model-00002-of-00003.bin: 0%| | 0.00/5.00G [00:00<?, ?B/s] pytorch_model-00002-of-00003.bin: 0%| | 10.5M/5.00G [00:00<02:17, 36.3MB/s] pytorch_model-00002-of-00003.bin: 2%|▏ | 94.4M/5.00G [00:00<00:16, 291MB/s] pytorch_model-00002-of-00003.bin: 4%|▎ | 178M/5.00G [00:00<00:10, 457MB/s] pytorch_model-00002-of-00003.bin: 6%|▌ | 283M/5.00G [00:00<00:10, 434MB/s] pytorch_model-00002-of-00003.bin: 7%|▋ | 357M/5.00G [00:00<00:09, 502MB/s] pytorch_model-00002-of-00003.bin: 9%|▉ | 461M/5.00G [00:00<00:07, 602MB/s] pytorch_model-00002-of-00003.bin: 25%|██▍ | 1.24G/5.00G [00:01<00:01, 2.37GB/s] pytorch_model-00002-of-00003.bin: 32%|███▏ | 1.60G/5.00G [00:01<00:01, 2.69GB/s] pytorch_model-00002-of-00003.bin: 38%|███▊ | 1.92G/5.00G [00:01<00:01, 1.83GB/s] pytorch_model-00002-of-00003.bin: 43%|████▎ | 2.17G/5.00G [00:01<00:02, 1.36GB/s] pytorch_model-00002-of-00003.bin: 49%|████▉ | 2.46G/5.00G [00:01<00:01, 1.62GB/s] pytorch_model-00002-of-00003.bin: 57%|█████▋ | 2.87G/5.00G [00:02<00:01, 2.08GB/s] pytorch_model-00002-of-00003.bin: 63%|██████▎ | 3.17G/5.00G [00:02<00:00, 2.22GB/s] pytorch_model-00002-of-00003.bin: 70%|███████ | 3.51G/5.00G [00:02<00:00, 2.49GB/s] pytorch_model-00002-of-00003.bin: 76%|███████▋ | 3.82G/5.00G [00:02<00:00, 2.41GB/s] pytorch_model-00002-of-00003.bin: 82%|████████▏ | 4.09G/5.00G [00:02<00:00, 2.22GB/s] pytorch_model-00002-of-00003.bin: 87%|████████▋ | 4.34G/5.00G [00:02<00:00, 1.92GB/s] pytorch_model-00002-of-00003.bin: 91%|█████████ | 4.56G/5.00G [00:02<00:00, 1.96GB/s] pytorch_model-00002-of-00003.bin: 100%|█████████▉| 5.00G/5.00G [00:02<00:00, 1.74GB/s]
anhnv125-mistral-v3-v9-mkmlizer: pytorch_model-00003-of-00003.bin: 0%| | 0.00/4.54G [00:00<?, ?B/s] pytorch_model-00003-of-00003.bin: 0%| | 10.5M/4.54G [00:00<02:06, 35.9MB/s] pytorch_model-00003-of-00003.bin: 2%|▏ | 94.4M/4.54G [00:00<00:14, 299MB/s] pytorch_model-00003-of-00003.bin: 5%|▍ | 220M/4.54G [00:00<00:07, 603MB/s] pytorch_model-00003-of-00003.bin: 10%|▉ | 451M/4.54G [00:00<00:03, 1.09GB/s] pytorch_model-00003-of-00003.bin: 13%|█▎ | 608M/4.54G [00:00<00:03, 1.24GB/s] pytorch_model-00003-of-00003.bin: 17%|█▋ | 755M/4.54G [00:00<00:03, 1.23GB/s] pytorch_model-00003-of-00003.bin: 20%|█▉ | 891M/4.54G [00:00<00:03, 1.15GB/s] pytorch_model-00003-of-00003.bin: 22%|██▏ | 1.02G/4.54G [00:01<00:03, 1.09GB/s] pytorch_model-00003-of-00003.bin: 25%|██▌ | 1.15G/4.54G [00:01<00:02, 1.14GB/s] pytorch_model-00003-of-00003.bin: 28%|██▊ | 1.28G/4.54G [00:01<00:03, 850MB/s] pytorch_model-00003-of-00003.bin: 30%|███ | 1.38G/4.54G [00:01<00:03, 823MB/s] pytorch_model-00003-of-00003.bin: 33%|███▎ | 1.48G/4.54G [00:01<00:04, 741MB/s] pytorch_model-00003-of-00003.bin: 35%|███▍ | 1.57G/4.54G [00:01<00:03, 777MB/s] pytorch_model-00003-of-00003.bin: 37%|███▋ | 1.67G/4.54G [00:01<00:03, 766MB/s] pytorch_model-00003-of-00003.bin: 39%|███▉ | 1.78G/4.54G [00:02<00:03, 854MB/s] pytorch_model-00003-of-00003.bin: 41%|████▏ | 1.88G/4.54G [00:02<00:03, 853MB/s] pytorch_model-00003-of-00003.bin: 43%|████▎ | 1.97G/4.54G [00:02<00:03, 797MB/s] pytorch_model-00003-of-00003.bin: 45%|████▌ | 2.07G/4.54G [00:02<00:02, 833MB/s] pytorch_model-00003-of-00003.bin: 48%|████▊ | 2.16G/4.54G [00:02<00:03, 749MB/s] pytorch_model-00003-of-00003.bin: 50%|█████ | 2.28G/4.54G [00:02<00:04, 539MB/s] pytorch_model-00003-of-00003.bin: 52%|█████▏ | 2.35G/4.54G [00:03<00:03, 567MB/s] pytorch_model-00003-of-00003.bin: 62%|██████▏ | 2.80G/4.54G [00:03<00:01, 1.37GB/s] pytorch_model-00003-of-00003.bin: 71%|███████▏ | 3.24G/4.54G [00:03<00:00, 2.04GB/s] pytorch_model-00003-of-00003.bin: 77%|███████▋ | 3.49G/4.54G [00:03<00:00, 2.08GB/s] pytorch_model-00003-of-00003.bin: 85%|████████▌ | 3.88G/4.54G [00:03<00:00, 2.50GB/s] pytorch_model-00003-of-00003.bin: 92%|█████████▏| 4.16G/4.54G [00:03<00:00, 1.96GB/s] pytorch_model-00003-of-00003.bin: 97%|█████████▋| 4.40G/4.54G [00:04<00:00, 1.23GB/s] pytorch_model-00003-of-00003.bin: 100%|█████████▉| 4.54G/4.54G [00:04<00:00, 1.10GB/s]
anhnv125-mistral-v3-v9-mkmlizer: pytorch_model.bin.index.json: 0%| | 0.00/23.9k [00:00<?, ?B/s] pytorch_model.bin.index.json: 100%|██████████| 23.9k/23.9k [00:00<00:00, 110MB/s]
anhnv125-mistral-v3-v9-mkmlizer: special_tokens_map.json: 0%| | 0.00/551 [00:00<?, ?B/s] special_tokens_map.json: 100%|██████████| 551/551 [00:00<00:00, 6.72MB/s]
anhnv125-mistral-v3-v9-mkmlizer: tokenizer.model: 0%| | 0.00/493k [00:00<?, ?B/s] tokenizer.model: 100%|██████████| 493k/493k [00:00<00:00, 64.2MB/s]
anhnv125-mistral-v3-v9-mkmlizer: tokenizer_config.json: 0%| | 0.00/1.02k [00:00<?, ?B/s] tokenizer_config.json: 100%|██████████| 1.02k/1.02k [00:00<00:00, 8.22MB/s]
anhnv125-mistral-v3-v9-mkmlizer: Downloaded to shared memory in 35.151s
anhnv125-mistral-v3-v9-mkmlizer: quantizing model to /dev/shm/model_cache
anhnv125-mistral-v3-v9-mkmlizer: Saving mkml model at /dev/shm/model_cache
anhnv125-mistral-v3-v9-mkmlizer: Reading /tmp/tmpqhtxjph_/pytorch_model.bin.index.json
anhnv125-mistral-v3-v9-mkmlizer: Profiling: 0%| | 0/291 [00:00<?, ?it/s] Profiling: 0%| | 1/291 [00:02<10:00, 2.07s/it] Profiling: 34%|███▎ | 98/291 [00:02<00:04, 43.91it/s] Profiling: 70%|███████ | 204/291 [00:03<00:01, 77.41it/s] Profiling: 100%|██████████| 291/291 [00:04<00:00, 74.82it/s] Profiling: 100%|██████████| 291/291 [00:04<00:00, 61.15it/s]
anhnv125-mistral-v3-v9-mkmlizer: quantized model in 14.783s
anhnv125-mistral-v3-v9-mkmlizer: Processed model anhnv125/mistral-v3 in 50.769s
anhnv125-mistral-v3-v9-mkmlizer: creating bucket guanaco-mkml-models
anhnv125-mistral-v3-v9-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
anhnv125-mistral-v3-v9-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/anhnv125-mistral-v3-v9
anhnv125-mistral-v3-v9-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/anhnv125-mistral-v3-v9/config.json
anhnv125-mistral-v3-v9-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/anhnv125-mistral-v3-v9/tokenizer_config.json
anhnv125-mistral-v3-v9-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/anhnv125-mistral-v3-v9/special_tokens_map.json
anhnv125-mistral-v3-v9-mkmlizer: cp /dev/shm/model_cache/tokenizer.model s3://guanaco-mkml-models/anhnv125-mistral-v3-v9/tokenizer.model
anhnv125-mistral-v3-v9-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/anhnv125-mistral-v3-v9/tokenizer.json
anhnv125-mistral-v3-v9-mkmlizer: cp /dev/shm/model_cache/mkml_model.tensors s3://guanaco-mkml-models/anhnv125-mistral-v3-v9/mkml_model.tensors
anhnv125-mistral-v3-v9-mkmlizer: config.json: 0%| | 0.00/1.05k [00:00<?, ?B/s] config.json: 100%|██████████| 1.05k/1.05k [00:00<00:00, 10.5MB/s]
anhnv125-mistral-v3-v9-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.
anhnv125-mistral-v3-v9-mkmlizer: warnings.warn(
anhnv125-mistral-v3-v9-mkmlizer: tokenizer_config.json: 0%| | 0.00/234 [00:00<?, ?B/s] tokenizer_config.json: 100%|██████████| 234/234 [00:00<00:00, 1.70MB/s]
anhnv125-mistral-v3-v9-mkmlizer: vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s] vocab.json: 100%|██████████| 1.04M/1.04M [00:00<00:00, 31.4MB/s]
anhnv125-mistral-v3-v9-mkmlizer: tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s] tokenizer.json: 100%|██████████| 2.11M/2.11M [00:00<00:00, 22.8MB/s]
anhnv125-mistral-v3-v9-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.
anhnv125-mistral-v3-v9-mkmlizer: warnings.warn(
anhnv125-mistral-v3-v9-mkmlizer: pytorch_model.bin: 0%| | 0.00/1.44G [00:00<?, ?B/s] pytorch_model.bin: 1%| | 10.5M/1.44G [00:00<00:20, 68.4MB/s] pytorch_model.bin: 3%|▎ | 41.9M/1.44G [00:00<00:09, 156MB/s] pytorch_model.bin: 4%|▍ | 62.9M/1.44G [00:00<00:13, 101MB/s] pytorch_model.bin: 7%|▋ | 105M/1.44G [00:00<00:07, 168MB/s] pytorch_model.bin: 9%|▉ | 136M/1.44G [00:00<00:08, 161MB/s] pytorch_model.bin: 11%|█ | 157M/1.44G [00:01<00:08, 158MB/s] pytorch_model.bin: 28%|██▊ | 398M/1.44G [00:01<00:01, 653MB/s] pytorch_model.bin: 81%|████████ | 1.16G/1.44G [00:01<00:00, 2.28GB/s] pytorch_model.bin: 100%|█████████▉| 1.44G/1.44G [00:01<00:00, 1.04GB/s]
anhnv125-mistral-v3-v9-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
anhnv125-mistral-v3-v9-mkmlizer: Saving duration: 0.213s
anhnv125-mistral-v3-v9-mkmlizer: Processed model rirv938/reward_gpt2_medium_preference_24m_e2 in 17.129s
anhnv125-mistral-v3-v9-mkmlizer: creating bucket guanaco-reward-models
anhnv125-mistral-v3-v9-mkmlizer: Bucket 's3://guanaco-reward-models/' created
anhnv125-mistral-v3-v9-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/config.json
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/special_tokens_map.json
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/tokenizer_config.json
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/merges.txt
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/vocab.json
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/tokenizer.json
anhnv125-mistral-v3-v9-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/anhnv125-mistral-v3-v9_reward/reward.tensors
Job anhnv125-mistral-v3-v9-mkmlizer completed after 85.6s with status: succeeded
Stopping job with name anhnv125-mistral-v3-v9-mkmlizer
Pipeline stage MKMLizer completed in 90.49s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.10s
Running pipeline stage ISVCDeployer
Creating inference service anhnv125-mistral-v3-v9
Waiting for inference service anhnv125-mistral-v3-v9 to be ready
Inference service anhnv125-mistral-v3-v9 ready after 30.16894006729126s
Pipeline stage ISVCDeployer completed in 38.57s
Running pipeline stage StressChecker
Received healthy response to inference request in 1.7914471626281738s
Received healthy response to inference request in 1.204376459121704s
Received healthy response to inference request in 1.2384941577911377s
Received healthy response to inference request in 1.2194628715515137s
Received healthy response to inference request in 1.3199293613433838s
5 requests
0 failed requests
5th percentile: 1.207393741607666
10th percentile: 1.2104110240936279
20th percentile: 1.2164455890655517
30th percentile: 1.2232691287994384
40th percentile: 1.230881643295288
50th percentile: 1.2384941577911377
60th percentile: 1.271068239212036
70th percentile: 1.3036423206329346
80th percentile: 1.414232921600342
90th percentile: 1.602840042114258
95th percentile: 1.6971436023712156
99th percentile: 1.772586450576782
mean time: 1.3547420024871826
Pipeline stage StressChecker completed in 7.62s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.04s
Running pipeline stage DaemonicSafetyScorer
Pipeline stage DaemonicSafetyScorer completed in 0.04s
Running M-Eval for topic stay_in_character
anhnv125-mistral-v3_v9 status is now deployed due to DeploymentManager action
M-Eval Dataset for topic stay_in_character is loaded
anhnv125-mistral-v3_v9 status is now inactive due to auto deactivation removed underperforming models
admin requested tearing down of anhnv125-mistral-v3_v9
Running pipeline stage ISVCDeleter
Checking if service anhnv125-mistral-v3-v9 is running
Tearing down inference service anhnv125-mistral-v3-v9
Toredown service anhnv125-mistral-v3-v9
Pipeline stage ISVCDeleter completed in 7.43s
Running pipeline stage MKMLModelDeleter
Cleaning model data from S3
Cleaning model data from model cache
Deleting key anhnv125-mistral-v3-v9/config.json from bucket guanaco-mkml-models
Deleting key anhnv125-mistral-v3-v9/mkml_model.tensors from bucket guanaco-mkml-models
Deleting key anhnv125-mistral-v3-v9/special_tokens_map.json from bucket guanaco-mkml-models
Deleting key anhnv125-mistral-v3-v9/tokenizer.json from bucket guanaco-mkml-models
Deleting key anhnv125-mistral-v3-v9/tokenizer.model from bucket guanaco-mkml-models
Deleting key anhnv125-mistral-v3-v9/tokenizer_config.json from bucket guanaco-mkml-models
Cleaning model data from model cache
Deleting key anhnv125-mistral-v3-v9_reward/config.json from bucket guanaco-reward-models
Deleting key anhnv125-mistral-v3-v9_reward/merges.txt from bucket guanaco-reward-models
Deleting key anhnv125-mistral-v3-v9_reward/reward.tensors from bucket guanaco-reward-models
Deleting key anhnv125-mistral-v3-v9_reward/special_tokens_map.json from bucket guanaco-reward-models
Deleting key anhnv125-mistral-v3-v9_reward/tokenizer.json from bucket guanaco-reward-models
Deleting key anhnv125-mistral-v3-v9_reward/tokenizer_config.json from bucket guanaco-reward-models
Deleting key anhnv125-mistral-v3-v9_reward/vocab.json from bucket guanaco-reward-models
Pipeline stage MKMLModelDeleter completed in 2.43s
anhnv125-mistral-v3_v9 status is now torndown due to DeploymentManager action

Usage Metrics

Latency Metrics