submission_id: khanhnto-khanhnto_v37
developer_uid: chai_backend_admin
status: deployed
model_repo: khanhnto/khanhnto
reward_repo: ChaiML/reward_models_100_170000000_cp_498032
generation_params: {'temperature': 1.2, 'top_p': 0.7, 'top_k': 50, 'presence_penalty': 0.8, 'frequency_penalty': 0.2, 'stopping_words': ['<\\s>', '###'], 'max_input_tokens': 512, 'best_of': 4, 'max_output_tokens': 64}
formatter: {'memory_template': "### Instruction:\n\n{bot_name}'s Persona: {memory}.\n\nPlay the role of {bot_name}. Engage in a chat with {user_name} while stay in character. Do not write dialogues and narration for {user_name}. {bot_name} should response with messages of medium length.", 'prompt_template': '{prompt}\n\n', 'bot_template': '### Response:\n\n{bot_name}: {message}\n\n', 'user_template': '### Input:\n\n{user_name}: {message}\n\n', 'response_template': '### Response:\n\n{bot_name}:'}
timestamp: 2023-12-18T11:29:58+00:00
model_name: khanhnto-khanhnto_v37
safety_score: 0.98
entertaining: None
stay_in_character: None
user_preference: None
double_thumbs_up: 5461
thumbs_up: 9119
thumbs_down: 4801
num_battles: 325761
num_wins: 148261
win_ratio: 0.4551220066244885
celo_rating: 1118.35
Resubmit model
Running pipeline stage MKMLizer
Starting job with name khanhnto-khanhnto-mkmlizer
Waiting for job on khanhnto-khanhnto-mkmlizer to finish
khanhnto-khanhnto-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
khanhnto-khanhnto-mkmlizer: ║ _______ __ __ _______ _____ ║
khanhnto-khanhnto-mkmlizer: ║ | | | |/ | | | |_ ║
khanhnto-khanhnto-mkmlizer: ║ | | <| | | ║
khanhnto-khanhnto-mkmlizer: ║ |__|_|__|__|\__|__|_|__|_______| ║
khanhnto-khanhnto-mkmlizer: ║ ║
khanhnto-khanhnto-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
khanhnto-khanhnto-mkmlizer: ║ ║
khanhnto-khanhnto-mkmlizer: ║ The license key for the current software has been verified as ║
khanhnto-khanhnto-mkmlizer: ║ belonging to: ║
khanhnto-khanhnto-mkmlizer: ║ ║
khanhnto-khanhnto-mkmlizer: ║ Chai Research Corp ║
khanhnto-khanhnto-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
khanhnto-khanhnto-mkmlizer: ║ Expiration: 2024-01-08 23:59:59 ║
khanhnto-khanhnto-mkmlizer: ║ ║
khanhnto-khanhnto-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
khanhnto-khanhnto-mkmlizer: loading model from khanhnto/khanhnto
khanhnto-khanhnto-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.
khanhnto-khanhnto-mkmlizer: warnings.warn(
khanhnto-khanhnto-mkmlizer: config.json: 0%| | 0.00/702 [00:00<?, ?B/s] config.json: 100%|██████████| 702/702 [00:00<00:00, 8.49MB/s]
khanhnto-khanhnto-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.
khanhnto-khanhnto-mkmlizer: warnings.warn(
khanhnto-khanhnto-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, 10.6MB/s]
khanhnto-khanhnto-mkmlizer: tokenizer.model: 0%| | 0.00/500k [00:00<?, ?B/s] tokenizer.model: 100%|██████████| 500k/500k [00:00<00:00, 53.2MB/s]
khanhnto-khanhnto-mkmlizer: tokenizer.json: 0%| | 0.00/1.84M [00:00<?, ?B/s] tokenizer.json: 100%|██████████| 1.84M/1.84M [00:00<00:00, 34.5MB/s]
khanhnto-khanhnto-mkmlizer: added_tokens.json: 0%| | 0.00/21.0 [00:00<?, ?B/s] added_tokens.json: 100%|██████████| 21.0/21.0 [00:00<00:00, 221kB/s]
khanhnto-khanhnto-mkmlizer: special_tokens_map.json: 0%| | 0.00/548 [00:00<?, ?B/s] special_tokens_map.json: 100%|██████████| 548/548 [00:00<00:00, 3.82MB/s]
khanhnto-khanhnto-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.
khanhnto-khanhnto-mkmlizer: warnings.warn(
khanhnto-khanhnto-mkmlizer: model.safetensors.index.json: 0%| | 0.00/29.9k [00:00<?, ?B/s] model.safetensors.index.json: 100%|██████████| 29.9k/29.9k [00:00<00:00, 91.4MB/s]
khanhnto-khanhnto-mkmlizer: Downloading shards: 0%| | 0/6 [00:00<?, ?it/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 0%| | 0.00/4.98G [00:00<?, ?B/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 0%| | 10.5M/4.98G [00:01<09:29, 8.73MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 0%| | 21.0M/4.98G [00:01<04:43, 17.5MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 2%|▏ | 83.9M/4.98G [00:01<00:54, 90.2MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 3%|▎ | 157M/4.98G [00:01<00:26, 181MB/s] 
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 5%|▍ | 231M/4.98G [00:01<00:18, 256MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 8%|▊ | 388M/4.98G [00:01<00:09, 496MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 9%|▉ | 472M/4.98G [00:01<00:08, 530MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 11%|█ | 556M/4.98G [00:02<00:09, 461MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 14%|█▍ | 692M/4.98G [00:02<00:06, 635MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 21%|██▏ | 1.07G/4.98G [00:02<00:03, 1.29GB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 25%|██▌ | 1.25G/4.98G [00:02<00:02, 1.40GB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 31%|███▏ | 1.56G/4.98G [00:02<00:01, 1.74GB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 63%|██████▎ | 3.11G/4.98G [00:04<00:02, 791MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 65%|██████▍ | 3.22G/4.98G [00:05<00:02, 637MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 66%|██████▋ | 3.30G/4.98G [00:05<00:02, 656MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 68%|██████▊ | 3.39G/4.98G [00:05<00:02, 680MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 70%|██████▉ | 3.47G/4.98G [00:05<00:02, 679MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 74%|███████▎ | 3.66G/4.98G [00:05<00:01, 937MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 78%|███████▊ | 3.86G/4.98G [00:05<00:00, 1.16GB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 80%|████████ | 4.00G/4.98G [00:05<00:00, 1.11GB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 83%|████████▎ | 4.12G/4.98G [00:06<00:00, 868MB/s] 
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 85%|████████▍ | 4.23G/4.98G [00:06<00:01, 745MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 87%|████████▋ | 4.33G/4.98G [00:06<00:00, 761MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 89%|████████▉ | 4.42G/4.98G [00:06<00:00, 738MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 91%|█████████ | 4.51G/4.98G [00:06<00:00, 710MB/s]
khanhnto-khanhnto-mkmlizer: model-00001-of-00006.safetensors: 92%|█████████▏| 4.59G/4.98G [00:06<00:00, 628MB/s] model-00001-of-00006.safetensors: 100%|█████████▉| 4.98G/4.98G [00:07<00:00, 709MB/s]
khanhnto-khanhnto-mkmlizer: Downloading shards: 17%|█▋ | 1/6 [00:07<00:35, 7.15s/it]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 0%| | 0.00/4.97G [00:00<?, ?B/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 0%| | 10.5M/4.97G [00:01<08:15, 10.0MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 0%| | 21.0M/4.97G [00:01<04:08, 19.9MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 1%| | 41.9M/4.97G [00:01<01:53, 43.4MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 1%|▏ | 73.4M/4.97G [00:01<00:59, 82.2MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 3%|▎ | 157M/4.97G [00:01<00:22, 211MB/s] 
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 4%|▍ | 210M/4.97G [00:01<00:17, 270MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 5%|▌ | 252M/4.97G [00:01<00:16, 288MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 6%|▌ | 294M/4.97G [00:01<00:15, 297MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 7%|▋ | 367M/4.97G [00:02<00:11, 390MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 10%|█ | 503M/4.97G [00:02<00:07, 616MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 15%|█▍ | 744M/4.97G [00:02<00:03, 1.07GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 23%|██▎ | 1.15G/4.97G [00:02<00:02, 1.87GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 27%|██▋ | 1.36G/4.97G [00:02<00:01, 1.88GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 32%|███▏ | 1.57G/4.97G [00:03<00:04, 759MB/s] 
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 35%|███▍ | 1.73G/4.97G [00:03<00:04, 755MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 38%|███▊ | 1.87G/4.97G [00:03<00:04, 766MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 40%|███▉ | 1.98G/4.97G [00:03<00:04, 621MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 42%|████▏ | 2.10G/4.97G [00:03<00:04, 694MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 47%|████▋ | 2.32G/4.97G [00:04<00:02, 943MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 55%|█████▍ | 2.72G/4.97G [00:04<00:01, 1.53GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 59%|█████▉ | 2.94G/4.97G [00:04<00:01, 1.62GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 63%|██████▎ | 3.15G/4.97G [00:04<00:01, 1.14GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 67%|██████▋ | 3.31G/4.97G [00:04<00:01, 935MB/s] 
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 70%|██████▉ | 3.47G/4.97G [00:04<00:01, 997MB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 73%|███████▎ | 3.61G/4.97G [00:05<00:01, 1.04GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 76%|███████▌ | 3.79G/4.97G [00:05<00:01, 1.18GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 79%|███████▉ | 3.93G/4.97G [00:05<00:00, 1.17GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 82%|████████▏ | 4.10G/4.97G [00:05<00:00, 1.25GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 85%|████████▌ | 4.25G/4.97G [00:05<00:00, 1.21GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 88%|████████▊ | 4.38G/4.97G [00:05<00:00, 1.03GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 91%|█████████ | 4.51G/4.97G [00:05<00:00, 1.07GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 93%|█████████▎| 4.65G/4.97G [00:06<00:00, 1.05GB/s]
khanhnto-khanhnto-mkmlizer: model-00002-of-00006.safetensors: 96%|█████████▌| 4.76G/4.97G [00:06<00:00, 1.05GB/s] model-00002-of-00006.safetensors: 100%|█████████▉| 4.97G/4.97G [00:06<00:00, 801MB/s]
khanhnto-khanhnto-mkmlizer: Downloading shards: 33%|███▎ | 2/6 [00:13<00:26, 6.70s/it]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 0%| | 0.00/4.97G [00:00<?, ?B/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 0%| | 10.5M/4.97G [00:00<06:14, 13.3MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 0%| | 21.0M/4.97G [00:01<03:44, 22.1MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 1%| | 31.5M/4.97G [00:01<03:06, 26.5MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 1%|▏ | 73.4M/4.97G [00:01<00:59, 81.9MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 54%|█████▎ | 2.66G/4.97G [00:04<00:01, 1.37GB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 57%|█████▋ | 2.84G/4.97G [00:04<00:01, 1.44GB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 61%|██████ | 3.02G/4.97G [00:05<00:03, 593MB/s] 
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 63%|██████▎ | 3.16G/4.97G [00:05<00:02, 622MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 66%|██████▌ | 3.27G/4.97G [00:05<00:02, 666MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 69%|██████▉ | 3.45G/4.97G [00:05<00:01, 825MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 72%|███████▏ | 3.58G/4.97G [00:05<00:01, 801MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 77%|███████▋ | 3.83G/4.97G [00:05<00:01, 1.11GB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 80%|████████ | 4.00G/4.97G [00:05<00:00, 1.19GB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 83%|████████▎ | 4.14G/4.97G [00:06<00:00, 1.01GB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 86%|████████▌ | 4.27G/4.97G [00:06<00:00, 810MB/s] 
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 89%|████████▊ | 4.40G/4.97G [00:06<00:00, 874MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 91%|█████████▏| 4.54G/4.97G [00:06<00:00, 941MB/s]
khanhnto-khanhnto-mkmlizer: model-00003-of-00006.safetensors: 94%|█████████▎| 4.66G/4.97G [00:06<00:00, 804MB/s] model-00003-of-00006.safetensors: 100%|█████████▉| 4.97G/4.97G [00:07<00:00, 710MB/s]
khanhnto-khanhnto-mkmlizer: Downloading shards: 50%|█████ | 3/6 [00:20<00:20, 6.91s/it]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 0%| | 0.00/4.93G [00:00<?, ?B/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 0%| | 10.5M/4.93G [00:00<06:53, 11.9MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 0%| | 21.0M/4.93G [00:01<05:01, 16.3MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 1%| | 31.5M/4.93G [00:01<03:25, 23.9MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 1%| | 41.9M/4.93G [00:01<02:22, 34.3MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 1%| | 52.4M/4.93G [00:01<01:47, 45.3MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 2%|▏ | 83.9M/4.93G [00:02<01:05, 74.3MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 2%|▏ | 115M/4.93G [00:02<00:44, 109MB/s] 
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 3%|▎ | 147M/4.93G [00:02<00:35, 135MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 4%|▍ | 199M/4.93G [00:02<00:22, 206MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 6%|▌ | 283M/4.93G [00:02<00:14, 321MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 8%|▊ | 398M/4.93G [00:02<00:09, 499MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 9%|▉ | 461M/4.93G [00:02<00:09, 468MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 11%|█ | 524M/4.93G [00:02<00:09, 480MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 13%|█▎ | 629M/4.93G [00:03<00:07, 604MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 23%|██▎ | 1.12G/4.93G [00:03<00:02, 1.65GB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 27%|██▋ | 1.32G/4.93G [00:03<00:02, 1.68GB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 31%|███ | 1.51G/4.93G [00:03<00:03, 1.00GB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 34%|███▎ | 1.66G/4.93G [00:03<00:04, 779MB/s] 
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 36%|███▌ | 1.77G/4.93G [00:04<00:04, 637MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 38%|███▊ | 1.87G/4.93G [00:04<00:05, 555MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 40%|███▉ | 1.95G/4.93G [00:04<00:05, 568MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 42%|████▏ | 2.06G/4.93G [00:04<00:04, 642MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 45%|████▌ | 2.22G/4.93G [00:04<00:03, 831MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 49%|████▊ | 2.40G/4.93G [00:04<00:02, 1.02GB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 53%|█████▎ | 2.61G/4.93G [00:05<00:01, 1.26GB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 57%|█████▋ | 2.79G/4.93G [00:05<00:01, 1.37GB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 60%|█████▉ | 2.95G/4.93G [00:05<00:02, 920MB/s] 
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 62%|██████▏ | 3.07G/4.93G [00:05<00:02, 770MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 64%|██████▍ | 3.18G/4.93G [00:05<00:02, 724MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 66%|██████▋ | 3.27G/4.93G [00:06<00:02, 746MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 68%|██████▊ | 3.38G/4.93G [00:06<00:01, 784MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 71%|███████ | 3.49G/4.93G [00:06<00:01, 862MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 74%|███████▎ | 3.64G/4.93G [00:06<00:01, 955MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 76%|███████▌ | 3.74G/4.93G [00:06<00:01, 861MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 78%|███████▊ | 3.85G/4.93G [00:06<00:01, 889MB/s]
khanhnto-khanhnto-mkmlizer: model-00004-of-00006.safetensors: 80%|████████ | 3.96G/4.93G [00:06<00:01, 947MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 4%|▍ | 210M/4.93G [00:02<00:21, 222MB/s] 
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 6%|▌ | 283M/4.93G [00:02<00:18, 253MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 7%|▋ | 357M/4.93G [00:02<00:13, 331MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 8%|▊ | 409M/4.93G [00:03<00:13, 325MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 12%|█▏ | 598M/4.93G [00:03<00:06, 628MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 23%|██▎ | 1.12G/4.93G [00:03<00:02, 1.61GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 27%|██▋ | 1.33G/4.93G [00:03<00:02, 1.49GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 31%|███ | 1.52G/4.93G [00:04<00:04, 762MB/s] 
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 34%|███▍ | 1.67G/4.93G [00:04<00:04, 772MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 36%|███▋ | 1.79G/4.93G [00:04<00:03, 829MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 39%|███▉ | 1.92G/4.93G [00:04<00:04, 745MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 41%|████ | 2.02G/4.93G [00:04<00:03, 752MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 43%|████▎ | 2.13G/4.93G [00:04<00:03, 789MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 45%|████▌ | 2.22G/4.93G [00:04<00:03, 787MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 47%|████▋ | 2.32G/4.93G [00:05<00:03, 804MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 50%|█████ | 2.49G/4.93G [00:05<00:02, 993MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 54%|█████▍ | 2.65G/4.93G [00:05<00:02, 1.14GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 56%|█████▋ | 2.78G/4.93G [00:05<00:02, 1.06GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 59%|█████▊ | 2.89G/4.93G [00:05<00:02, 975MB/s] 
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 61%|██████ | 3.00G/4.93G [00:05<00:01, 975MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 63%|██████▎ | 3.11G/4.93G [00:05<00:01, 1.01GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 67%|██████▋ | 3.31G/4.93G [00:05<00:01, 1.26GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 70%|██████▉ | 3.45G/4.93G [00:05<00:01, 1.16GB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 72%|███████▏ | 3.58G/4.93G [00:06<00:01, 749MB/s] 
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 75%|███████▍ | 3.68G/4.93G [00:06<00:02, 554MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 76%|███████▋ | 3.76G/4.93G [00:06<00:01, 587MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 78%|███████▊ | 3.85G/4.93G [00:06<00:01, 578MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 80%|███████▉ | 3.94G/4.93G [00:07<00:01, 644MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 82%|████████▏ | 4.03G/4.93G [00:07<00:01, 627MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 84%|████████▍ | 4.14G/4.93G [00:07<00:01, 741MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 87%|████████▋ | 4.27G/4.93G [00:07<00:00, 848MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 89%|████████▉ | 4.39G/4.93G [00:07<00:00, 821MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 91%|█████████ | 4.48G/4.93G [00:07<00:00, 725MB/s]
khanhnto-khanhnto-mkmlizer: model-00005-of-00006.safetensors: 100%|█████████▉| 4.92G/4.93G [00:07<00:00, 1.41GB/s] model-00005-of-00006.safetensors: 100%|█████████▉| 4.93G/4.93G [00:08<00:00, 598MB/s]
khanhnto-khanhnto-mkmlizer: Downloading shards: 83%|████████▎ | 5/6 [00:37<00:07, 7.75s/it]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 0%| | 0.00/1.25G [00:00<?, ?B/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 1%| | 10.5M/1.25G [00:01<02:32, 8.10MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 2%|▏ | 21.0M/1.25G [00:01<01:40, 12.2MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 3%|▎ | 31.5M/1.25G [00:01<00:59, 20.3MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 3%|▎ | 41.9M/1.25G [00:02<00:44, 26.8MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 4%|▍ | 52.4M/1.25G [00:02<00:37, 32.2MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 14%|█▍ | 178M/1.25G [00:02<00:05, 196MB/s] 
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 23%|██▎ | 283M/1.25G [00:02<00:03, 288MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 27%|██▋ | 336M/1.25G [00:02<00:03, 279MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 32%|███▏ | 398M/1.25G [00:02<00:02, 332MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 43%|████▎ | 535M/1.25G [00:03<00:01, 528MB/s]
khanhnto-khanhnto-mkmlizer: model-00006-of-00006.safetensors: 86%|████████▌ | 1.07G/1.25G [00:03<00:00, 1.53GB/s] model-00006-of-00006.safetensors: 100%|█████████▉| 1.25G/1.25G [00:03<00:00, 379MB/s]
khanhnto-khanhnto-mkmlizer: Downloading shards: 100%|██████████| 6/6 [00:40<00:00, 6.29s/it] Downloading shards: 100%|██████████| 6/6 [00:40<00:00, 6.77s/it]
khanhnto-khanhnto-mkmlizer: saved to disk in 144.465s
khanhnto-khanhnto-mkmlizer: quantizing model to /tmp/model_cache
khanhnto-khanhnto-mkmlizer: Saving mkml model at /tmp/model_cache
khanhnto-khanhnto-mkmlizer: Reading /tmp/tmponifdsyy/model.safetensors.index.json
khanhnto-khanhnto-mkmlizer: Profiling: 0%| | 0/363 [00:00<?, ?it/s] Profiling: 0%| | 1/363 [00:02<15:34, 2.58s/it] Profiling: 1%| | 3/363 [00:03<06:58, 1.16s/it] Profiling: 1%| | 4/363 [00:05<07:28, 1.25s/it] Profiling: 1%|▏ | 5/363 [00:06<07:45, 1.30s/it] Profiling: 2%|▏ | 7/363 [00:07<04:47, 1.24it/s] Profiling: 2%|▏ | 8/363 [00:07<04:21, 1.36it/s] Profiling: 2%|▏ | 9/363 [00:08<04:04, 1.45it/s] Profiling: 3%|▎ | 10/363 [00:08<03:47, 1.55it/s] Profiling: 3%|▎ | 12/363 [00:10<03:51, 1.51it/s] Profiling: 4%|▎ | 13/363 [00:11<04:44, 1.23it/s] Profiling: 4%|▍ | 14/363 [00:12<05:26, 1.07it/s] Profiling: 4%|▍ | 16/363 [00:13<03:41, 1.57it/s] Profiling: 5%|▍ | 17/363 [00:13<03:31, 1.64it/s] Profiling: 5%|▍ | 18/363 [00:14<03:23, 1.69it/s] Profiling: 5%|▌ | 19/363 [00:14<03:11, 1.80it/s] Profiling: 6%|▌ | 21/363 [00:16<03:23, 1.68it/s] Profiling: 6%|▌ | 22/363 [00:17<04:18, 1.32it/s] Profiling: 6%|▋ | 23/363 [00:18<05:03, 1.12it/s] Profiling: 7%|▋ | 25/363 [00:19<03:27, 1.63it/s] Profiling: 7%|▋ | 26/363 [00:19<03:14, 1.73it/s] Profiling: 7%|▋ | 27/363 [00:20<03:10, 1.77it/s] Profiling: 8%|▊ | 28/363 [00:20<03:05, 1.81it/s] Profiling: 8%|▊ | 29/363 [00:21<03:03, 1.82it/s] Profiling: 8%|▊ | 30/363 [00:21<03:01, 1.84it/s] Profiling: 9%|▊ | 31/363 [00:22<02:54, 1.90it/s] Profiling: 9%|▉ | 32/363 [00:22<02:48, 1.96it/s] Profiling: 9%|▉ | 34/363 [00:24<03:10, 1.72it/s] Profiling: 10%|▉ | 35/363 [00:25<04:03, 1.35it/s] Profiling: 10%|▉ | 36/363 [00:26<04:43, 1.15it/s] Profiling: 11%|█ | 39/363 [00:27<03:23, 1.59it/s] Profiling: 11%|█ | 40/363 [00:28<04:03, 1.33it/s] Profiling: 11%|█▏ | 41/363 [00:30<04:38, 1.15it/s] Profiling: 12%|█▏ | 43/363 [00:30<03:18, 1.62it/s] Profiling: 12%|█▏ | 44/363 [00:31<03:05, 1.72it/s] Profiling: 12%|█▏ | 45/363 [00:31<02:55, 1.81it/s] Profiling: 13%|█▎ | 46/363 [00:32<02:52, 1.84it/s] Profiling: 13%|█▎ | 48/363 [00:33<03:14, 1.62it/s] Profiling: 13%|█▎ | 49/363 [00:34<04:09, 1.26it/s] Profiling: 14%|█▍ | 50/363 [00:36<04:54, 1.06it/s] Profiling: 14%|█▍ | 52/363 [00:36<03:26, 1.51it/s] Profiling: 15%|█▍ | 53/363 [00:37<03:16, 1.58it/s] Profiling: 15%|█▍ | 54/363 [00:37<03:12, 1.61it/s] Profiling: 15%|█▌ | 55/363 [00:38<03:06, 1.66it/s] Profiling: 16%|█▌ | 57/363 [00:40<03:22, 1.51it/s] Profiling: 16%|█▌ | 58/363 [00:41<04:10, 1.22it/s] Profiling: 16%|█▋ | 59/363 [00:42<04:46, 1.06it/s] Profiling: 17%|█▋ | 61/363 [00:43<03:19, 1.52it/s] Profiling: 17%|█▋ | 62/363 [00:43<03:08, 1.60it/s] Profiling: 17%|█▋ | 63/363 [00:44<02:59, 1.67it/s] Profiling: 18%|█▊ | 64/363 [00:44<02:48, 1.77it/s] Profiling: 18%|█▊ | 65/363 [00:45<02:42, 1.83it/s] Profiling: 18%|█▊ | 66/363 [00:45<02:35, 1.91it/s] Profiling: 18%|█▊ | 67/363 [00:46<02:34, 1.92it/s] Profiling: 19%|█▉ | 69/363 [00:47<02:53, 1.69it/s] Profiling: 19%|█▉ | 70/363 [00:48<03:37, 1.35it/s] Profiling: 20%|█▉ | 71/363 [00:49<04:14, 1.15it/s] Profiling: 20%|██ | 73/363 [00:50<02:54, 1.66it/s] Profiling: 20%|██ | 74/363 [00:50<02:47, 1.72it/s] Profiling: 21%|██ | 75/363 [00:51<02:42, 1.78it/s] Profiling: 21%|██ | 76/363 [00:51<02:37, 1.83it/s] Profiling: 21%|██ | 77/363 [00:52<02:34, 1.86it/s] Profiling: 21%|██▏ | 78/363 [00:53<02:31, 1.89it/s] Profiling: 22%|██▏ | 80/363 [00:54<02:44, 1.72it/s] Profiling: 22%|██▏ | 81/363 [00:55<03:28, 1.35it/s] Profiling: 23%|██▎ | 82/363 [00:56<04:03, 1.16it/s] Profiling: 23%|██▎ | 84/363 [00:57<02:45, 1.69it/s] Profiling: 24%|██▎ | 86/363 [00:58<02:47, 1.65it/s] Profiling: 24%|██▍ | 87/363 [00:59<03:24, 1.35it/s] Profiling: 24%|██▍ | 88/363 [01:01<03:59, 1.15it/s] Profiling: 25%|██▍ | 90/363 [01:01<02:46, 1.64it/s] Profiling: 25%|██▌ | 91/363 [01:01<02:36, 1.74it/s] Profiling: 25%|██▌ | 92/363 [01:02<02:31, 1.79it/s] Profiling: 26%|██▌ | 93/363 [01:02<02:26, 1.84it/s] Profiling: 26%|██▌ | 95/363 [01:04<02:35, 1.73it/s] Profiling: 26%|██▋ | 96/363 [01:05<03:17, 1.35it/s] Profiling: 27%|██▋ | 97/363 [01:06<04:08, 1.07it/s] Profiling: 27%|██▋ | 99/363 [01:07<02:52, 1.53it/s] Profiling: 28%|██▊ | 100/363 [01:07<02:40, 1.64it/s] Profiling: 28%|██▊ | 101/363 [01:08<02:29, 1.76it/s] Profiling: 28%|██▊ | 102/363 [01:08<02:21, 1.84it/s] Profiling: 29%|██▊ | 104/363 [01:10<02:35, 1.66it/s] Profiling: 29%|██▉ | 105/363 [01:11<03:15, 1.32it/s] Profiling: 29%|██▉ | 106/363 [01:12<03:46, 1.13it/s] Profiling: 30%|██▉ | 108/363 [01:13<02:34, 1.65it/s] Profiling: 30%|███ | 109/363 [01:13<02:29, 1.70it/s] Profiling: 31%|███ | 111/363 [01:15<02:35, 1.62it/s] Profiling: 31%|███ | 112/363 [01:16<03:10, 1.32it/s] Profiling: 31%|███ | 113/363 [01:17<03:40, 1.13it/s] Profiling: 32%|███▏ | 115/363 [01:18<02:34, 1.61it/s] Profiling: 32%|███▏ | 116/363 [01:18<02:23, 1.72it/s] Profiling: 32%|███▏ | 117/363 [01:18<02:14, 1.82it/s] Profiling: 33%|███▎ | 118/363 [01:19<02:10, 1.88it/s] Profiling: 33%|███▎ | 120/363 [01:20<02:32, 1.60it/s] Profiling: 33%|███▎ | 121/363 [01:22<03:10, 1.27it/s] Profiling: 34%|███▎ | 122/363 [01:23<03:36, 1.11it/s] Profiling: 34%|███▍ | 124/363 [01:23<02:27, 1.62it/s] Profiling: 34%|███▍ | 125/363 [01:24<02:18, 1.72it/s] Profiling: 35%|███▍ | 126/363 [01:24<02:09, 1.83it/s] Profiling: 35%|███▍ | 127/363 [01:25<02:03, 1.91it/s] Profiling: 36%|███▌ | 129/363 [01:26<02:14, 1.73it/s] Profiling: 36%|███▌ | 130/363 [01:27<02:51, 1.36it/s] Profiling: 36%|███▌ | 131/363 [01:29<03:23, 1.14it/s] Profiling: 37%|███▋ | 133/363 [01:29<02:21, 1.63it/s] Profiling: 37%|███▋ | 134/363 [01:30<02:15, 1.69it/s] Profiling: 37%|███▋ | 135/363 [01:30<02:08, 1.77it/s] Profiling: 37%|███▋ | 136/363 [01:31<02:02, 1.86it/s] Profiling: 38%|███▊ | 137/363 [01:31<01:58, 1.91it/s] Profiling: 38%|███▊ | 139/363 [01:32<02:10, 1.71it/s] Profiling: 39%|███▊ | 140/363 [01:34<02:45, 1.34it/s] Profiling: 39%|███▉ | 141/363 [01:35<03:16, 1.13it/s] Profiling: 39%|███▉ | 143/363 [01:35<02:15, 1.63it/s] Profiling: 40%|███▉ | 144/363 [01:36<02:10, 1.68it/s] Profiling: 40%|███▉ | 145/363 [01:36<02:05, 1.74it/s] Profiling: 40%|████ | 147/363 [01:38<02:10, 1.66it/s] Profiling: 41%|████ | 148/363 [01:39<02:42, 1.33it/s] Profiling: 41%|████ | 149/363 [01:40<03:09, 1.13it/s] Profiling: 42%|████▏ | 151/363 [01:41<02:09, 1.63it/s] Profiling: 42%|████▏ | 152/363 [01:41<02:01, 1.73it/s] Profiling: 42%|████▏ | 153/363 [01:42<01:58, 1.78it/s] Profiling: 42%|████▏ | 154/363 [01:42<01:52, 1.86it/s] Profiling: 43%|████▎ | 156/363 [01:44<02:02, 1.69it/s] Profiling: 43%|████▎ | 157/363 [01:45<02:32, 1.35it/s] Profiling: 44%|████▎ | 158/363 [01:46<02:56, 1.16it/s] Profiling: 44%|████▍ | 160/363 [01:46<02:01, 1.68it/s] Profiling: 44%|████▍ | 161/363 [01:47<01:54, 1.77it/s] Profiling: 45%|████▍ | 162/363 [01:47<01:48, 1.85it/s] Profiling: 45%|████▍ | 163/363 [01:48<01:43, 1.93it/s] Profiling: 45%|████▌ | 165/363 [01:49<01:54, 1.73it/s] Profiling: 46%|████▌ | 166/363 [01:50<02:23, 1.37it/s] Profiling: 46%|████▌ | 167/363 [01:52<02:47, 1.17it/s] Profiling: 47%|████▋ | 169/363 [01:52<01:55, 1.69it/s] Profiling: 47%|████▋ | 170/363 [01:52<01:47, 1.79it/s] Profiling: 47%|████▋ | 171/363 [01:53<01:44, 1.83it/s] Profiling: 47%|████▋ | 172/363 [01:54<01:42, 1.86it/s] Profiling: 48%|████▊ | 174/363 [01:55<01:54, 1.65it/s] Profiling: 48%|████▊ | 175/363 [01:56<02:23, 1.31it/s] Profiling: 48%|████▊ | 176/363 [01:57<02:45, 1.13it/s] Profiling: 49%|████▉ | 178/363 [01:58<01:52, 1.64it/s] Profiling: 49%|████▉ | 179/363 [01:58<01:45, 1.74it/s] Profiling: 50%|████▉ | 180/363 [01:59<01:39, 1.84it/s] Profiling: 50%|████▉ | 181/363 [01:59<01:34, 1.93it/s] Profiling: 50%|█████ | 183/363 [02:00<01:41, 1.78it/s] Profiling: 51%|█████ | 184/363 [02:02<02:07, 1.40it/s] Profiling: 51%|█████ | 185/363 [02:03<02:32, 1.17it/s] Profiling: 52%|█████▏ | 187/363 [02:03<01:46, 1.65it/s] Profiling: 52%|█████▏ | 188/363 [02:04<01:43, 1.69it/s] Profiling: 52%|█████▏ | 189/363 [02:05<01:39, 1.75it/s] Profiling: 52%|█████▏ | 190/363 [02:05<01:36, 1.79it/s] Profiling: 53%|█████▎ | 192/363 [02:06<01:42, 1.67it/s] Profiling: 53%|█████▎ | 193/363 [02:08<02:09, 1.31it/s] Profiling: 53%|█████▎ | 194/363 [02:09<02:31, 1.12it/s] Profiling: 54%|█████▍ | 196/363 [02:09<01:45, 1.59it/s] Profiling: 54%|█████▍ | 197/363 [02:10<01:40, 1.66it/s] Profiling: 55%|█████▍ | 198/363 [02:11<01:36, 1.72it/s] Profiling: 55%|█████▍ | 199/363 [02:11<01:32, 1.77it/s] Profiling: 55%|█████▌ | 200/363 [02:12<02:05, 1.30it/s] Profiling: 55%|█████▌ | 201/363 [02:14<02:26, 1.11it/s] Profiling: 56%|█████▌ | 202/363 [02:14<02:04, 1.30it/s] Profiling: 56%|█████▌ | 203/363 [02:15<01:51, 1.44it/s] Profiling: 56%|█████▌ | 204/363 [02:15<01:41, 1.57it/s] Profiling: 56%|█████▋ | 205/363 [02:15<01:32, 1.70it/s] Profiling: 57%|█████▋ | 207/363 [02:17<01:38, 1.59it/s] Profiling: 58%|█████▊ | 210/363 [02:18<01:20, 1.90it/s] Profiling: 58%|█████▊ | 211/363 [02:19<01:41, 1.50it/s] Profiling: 58%|█████▊ | 212/363 [02:21<02:00, 1.25it/s] Profiling: 59%|█████▉ | 214/363 [02:21<01:31, 1.63it/s] Profiling: 59%|█████▉ | 215/363 [02:22<01:27, 1.69it/s] Profiling: 60%|█████▉ | 216/363 [02:22<01:24, 1.74it/s] Profiling: 60%|█████▉ | 217/363 [02:23<01:20, 1.82it/s] Profiling: 60%|██████ | 219/363 [02:24<01:23, 1.72it/s] Profiling: 61%|██████ | 220/363 [02:25<01:40, 1.42it/s] Profiling: 61%|██████ | 221/363 [02:26<01:46, 1.34it/s] Profiling: 61%|██████▏ | 223/363 [02:26<01:11, 1.96it/s] Profiling: 62%|██████▏ | 224/363 [02:27<01:02, 2.21it/s] Profiling: 62%|██████▏ | 225/363 [02:27<00:55, 2.47it/s] Profiling: 62%|██████▏ | 226/363 [02:27<00:50, 2.72it/s] Profiling: 63%|██████▎ | 228/363 [02:28<00:49, 2.71it/s] Profiling: 63%|██████▎ | 229/363 [02:29<01:02, 2.14it/s] Profiling: 63%|██████▎ | 230/363 [02:29<01:12, 1.82it/s] Profiling: 64%|██████▍ | 232/363 [02:30<00:51, 2.52it/s] Profiling: 64%|██████▍ | 233/363 [02:30<00:49, 2.63it/s] Profiling: 64%|██████▍ | 234/363 [02:31<00:47, 2.71it/s] Profiling: 65%|██████▍ | 235/363 [02:31<00:43, 2.91it/s] Profiling: 65%|██████▌ | 236/363 [02:32<00:57, 2.20it/s] Profiling: 65%|██████▌ | 237/363 [02:32<00:53, 2.35it/s] Profiling: 66%|██████▌ | 238/363 [02:32<00:47, 2.65it/s] Profiling: 66%|██████▌ | 239/363 [02:32<00:42, 2.90it/s] Profiling: 66%|██████▌ | 240/363 [02:33<00:39, 3.13it/s] Profiling: 67%|██████▋ | 242/363 [02:33<00:43, 2.80it/s] Profiling: 67%|██████▋ | 243/363 [02:34<00:53, 2.25it/s] Profiling: 68%|██████▊ | 246/363 [02:35<00:39, 2.97it/s] Profiling: 68%|██████▊ | 247/363 [02:36<00:47, 2.45it/s] Profiling: 68%|██████▊ | 248/363 [02:36<00:54, 2.10it/s] Profiling: 69%|██████▉ | 250/363 [02:37<00:38, 2.91it/s] Profiling: 69%|██████▉ | 251/363 [02:37<00:36, 3.03it/s] Profiling: 69%|██████▉ | 252/363 [02:37<00:34, 3.20it/s] Profiling: 70%|██████▉ | 253/363 [02:37<00:32, 3.35it/s] Profiling: 70%|███████ | 255/363 [02:38<00:35, 3.07it/s] Profiling: 71%|███████ | 256/363 [02:39<00:44, 2.42it/s] Profiling: 71%|███████ | 257/363 [02:40<00:51, 2.05it/s] Profiling: 71%|███████▏ | 259/363 [02:40<00:35, 2.94it/s] Profiling: 72%|███████▏ | 260/363 [02:40<00:32, 3.12it/s] Profiling: 72%|███████▏ | 261/363 [02:40<00:31, 3.29it/s] Profiling: 72%|███████▏ | 262/363 [02:41<00:29, 3.43it/s] Profiling: 73%|███████▎ | 264/363 [02:41<00:31, 3.11it/s] Profiling: 73%|███████▎ | 265/363 [02:42<00:40, 2.45it/s] Profiling: 73%|███████▎ | 266/363 [02:43<00:46, 2.07it/s] Profiling: 74%|███████▍ | 268/363 [02:43<00:31, 2.98it/s] Profiling: 74%|███████▍ | 269/363 [02:43<00:29, 3.17it/s] Profiling: 74%|███████▍ | 270/363 [02:43<00:28, 3.31it/s] Profiling: 75%|███████▍ | 271/363 [02:44<00:26, 3.44it/s] Profiling: 75%|███████▍ | 272/363 [02:44<00:25, 3.56it/s] Profiling: 75%|███████▌ | 273/363 [02:44<00:24, 3.66it/s] Profiling: 75%|███████▌ | 274/363 [02:44<00:24, 3.71it/s] Profiling: 76%|███████▌ | 275/363 [02:45<00:23, 3.76it/s] Profiling: 76%|███████▋ | 277/363 [02:46<00:28, 3.03it/s] Profiling: 77%|███████▋ | 278/363 [02:46<00:36, 2.30it/s] Profiling: 77%|███████▋ | 279/363 [02:47<00:42, 1.98it/s] Profiling: 78%|███████▊ | 282/363 [02:48<00:30, 2.69it/s] Profiling: 78%|███████▊ | 283/363 [02:48<00:35, 2.28it/s] Profiling: 78%|███████▊ | 284/363 [02:49<00:39, 1.99it/s] Profiling: 79%|███████▉ | 286/363 [02:49<00:27, 2.79it/s] Profiling: 79%|███████▉ | 287/363 [02:50<00:25, 2.97it/s] Profiling: 79%|███████▉ | 288/363 [02:50<00:23, 3.15it/s] Profiling: 80%|███████▉ | 289/363 [02:50<00:22, 3.30it/s] Profiling: 80%|████████ | 291/363 [02:51<00:23, 3.11it/s] Profiling: 80%|████████ | 292/363 [02:52<00:29, 2.45it/s] Profiling: 81%|████████ | 293/363 [02:52<00:33, 2.06it/s] Profiling: 81%|████████▏ | 295/363 [02:53<00:22, 2.96it/s] Profiling: 82%|████████▏ | 296/363 [02:53<00:21, 3.14it/s] Profiling: 82%|████████▏ | 297/363 [02:53<00:19, 3.31it/s] Profiling: 82%|████████▏ | 298/363 [02:53<00:18, 3.44it/s] Profiling: 83%|████████▎ | 300/363 [02:54<00:20, 3.15it/s] Profiling: 83%|████████▎ | 301/363 [02:55<00:25, 2.45it/s] Profiling: 83%|████████▎ | 302/363 [02:55<00:29, 2.06it/s] Profiling: 84%|████████▎ | 304/363 [02:56<00:19, 2.97it/s] Profiling: 84%|████████▍ | 305/363 [02:56<00:18, 3.14it/s] Profiling: 84%|████████▍ | 306/363 [02:56<00:17, 3.28it/s] Profiling: 85%|████████▍ | 307/363 [02:57<00:17, 3.12it/s] Profiling: 85%|████████▍ | 308/363 [02:57<00:19, 2.82it/s] Profiling: 85%|████████▌ | 309/363 [02:58<00:20, 2.63it/s] Profiling: 85%|████████▌ | 310/363 [02:58<00:21, 2.49it/s] Profiling: 86%|████████▌ | 312/363 [02:59<00:22, 2.30it/s] Profiling: 86%|████████▌ | 313/363 [03:00<00:25, 1.93it/s] Profiling: 87%|████████▋ | 314/363 [03:00<00:28, 1.72it/s] Profiling: 87%|████████▋ | 316/363 [03:01<00:20, 2.34it/s] Profiling: 88%|████████▊ | 318/363 [03:02<00:18, 2.44it/s] Profiling: 88%|████████▊ | 319/363 [03:02<00:20, 2.10it/s] Profiling: 88%|████████▊ | 320/363 [03:03<00:22, 1.90it/s] Profiling: 89%|████████▊ | 322/363 [03:03<00:15, 2.71it/s] Profiling: 89%|████████▉ | 323/363 [03:04<00:13, 2.91it/s] Profiling: 89%|████████▉ | 324/363 [03:04<00:12, 3.09it/s] Profiling: 90%|████████▉ | 325/363 [03:04<00:11, 3.26it/s] Profiling: 90%|█████████ | 327/363 [03:05<00:11, 3.05it/s] Profiling: 90%|█████████ | 328/363 [03:06<00:14, 2.42it/s] Profiling: 91%|█████████ | 329/363 [03:06<00:16, 2.07it/s] Profiling: 91%|█████████ | 331/363 [03:06<00:10, 2.98it/s] Profiling: 91%|█████████▏| 332/363 [03:07<00:09, 3.15it/s] Profiling: 92%|█████████▏| 333/363 [03:07<00:09, 3.31it/s] Profiling: 92%|█████████▏| 334/363 [03:07<00:08, 3.43it/s] Profiling: 93%|█████████▎| 336/363 [03:08<00:08, 3.11it/s] Profiling: 93%|█████████▎| 337/363 [03:09<00:10, 2.41it/s] Profiling: 93%|█████████▎| 338/363 [03:09<00:12, 2.04it/s] Profiling: 94%|█████████▎| 340/363 [03:10<00:07, 2.94it/s] Profiling: 94%|█████████▍| 341/363 [03:10<00:07, 3.11it/s] Profiling: 94%|█████████▍| 342/363 [03:10<00:06, 3.25it/s] Profiling: 94%|█████████▍| 343/363 [03:11<00:06, 3.28it/s] Profiling: 95%|█████████▍| 344/363 [03:11<00:05, 3.19it/s] Profiling: 95%|█████████▌| 345/363 [03:11<00:05, 3.13it/s] Profiling: 95%|█████████▌| 346/363 [03:13<00:13, 1.29it/s] Profiling: 96%|█████████▌| 348/363 [03:14<00:08, 1.68it/s] Profiling: 96%|█████████▌| 349/363 [03:15<00:08, 1.60it/s] Profiling: 96%|█████████▋| 350/363 [03:15<00:08, 1.56it/s] Profiling: 97%|█████████▋| 352/363 [03:16<00:04, 2.36it/s] Profiling: 97%|█████████▋| 353/363 [03:16<00:03, 2.60it/s] Profiling: 98%|█████████▊| 355/363 [03:16<00:02, 2.71it/s] Profiling: 98%|█████████▊| 356/363 [03:17<00:03, 2.27it/s] Profiling: 98%|█████████▊| 357/363 [03:18<00:02, 2.00it/s] Profiling: 99%|█████████▉| 359/363 [03:18<00:01, 2.86it/s] Profiling: 99%|█████████▉| 360/363 [03:18<00:00, 3.04it/s] Profiling: 99%|█████████▉| 361/363 [03:19<00:00, 3.21it/s] Profiling: 100%|█████████▉| 362/363 [03:19<00:00, 3.36it/s] Profiling: 100%|██████████| 363/363 [03:19<00:00, 1.82it/s]
khanhnto-khanhnto-mkmlizer: quantized model in 248.003s
khanhnto-khanhnto-mkmlizer: Processed model khanhnto/khanhnto in 438.262s
khanhnto-khanhnto-mkmlizer: creating bucket guanaco-mkml-models
khanhnto-khanhnto-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
khanhnto-khanhnto-mkmlizer: uploading /tmp/model_cache to s3://guanaco-mkml-models/khanhnto-khanhnto-v37
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/added_tokens.json s3://guanaco-mkml-models/khanhnto-khanhnto-v37/added_tokens.json
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/config.json s3://guanaco-mkml-models/khanhnto-khanhnto-v37/config.json
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/tokenizer_config.json s3://guanaco-mkml-models/khanhnto-khanhnto-v37/tokenizer_config.json
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/special_tokens_map.json s3://guanaco-mkml-models/khanhnto-khanhnto-v37/special_tokens_map.json
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/tokenizer.model s3://guanaco-mkml-models/khanhnto-khanhnto-v37/tokenizer.model
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/tokenizer.json s3://guanaco-mkml-models/khanhnto-khanhnto-v37/tokenizer.json
khanhnto-khanhnto-mkmlizer: cp /tmp/model_cache/mkml_model.tensors s3://guanaco-mkml-models/khanhnto-khanhnto-v37/mkml_model.tensors
khanhnto-khanhnto-mkmlizer: tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s] tokenizer.json: 100%|██████████| 2.11M/2.11M [00:00<00:00, 34.5MB/s]
khanhnto-khanhnto-mkmlizer: special_tokens_map.json: 0%| | 0.00/99.0 [00:00<?, ?B/s] special_tokens_map.json: 100%|██████████| 99.0/99.0 [00:00<00:00, 1.61MB/s]
khanhnto-khanhnto-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.
khanhnto-khanhnto-mkmlizer: warnings.warn(
khanhnto-khanhnto-mkmlizer: pytorch_model.bin: 0%| | 0.00/510M [00:00<?, ?B/s] pytorch_model.bin: 2%|▏ | 10.5M/510M [00:00<00:06, 81.1MB/s] pytorch_model.bin: 4%|▍ | 21.0M/510M [00:00<00:05, 84.4MB/s] pytorch_model.bin: 12%|█▏ | 62.9M/510M [00:00<00:02, 207MB/s] pytorch_model.bin: 30%|███ | 154M/510M [00:00<00:00, 438MB/s] pytorch_model.bin: 71%|███████ | 364M/510M [00:00<00:00, 854MB/s] pytorch_model.bin: 88%|████████▊ | 447M/510M [00:00<00:00, 565MB/s] pytorch_model.bin: 100%|█████████▉| 510M/510M [00:02<00:00, 182MB/s]
khanhnto-khanhnto-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
khanhnto-khanhnto-mkmlizer: Saving duration: 0.108s
khanhnto-khanhnto-mkmlizer: Processed model ChaiML/reward_models_100_170000000_cp_498032 in 5.091s
khanhnto-khanhnto-mkmlizer: creating bucket guanaco-reward-models
khanhnto-khanhnto-mkmlizer: Bucket 's3://guanaco-reward-models/' created
khanhnto-khanhnto-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/config.json
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/special_tokens_map.json
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/tokenizer_config.json
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/merges.txt
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/vocab.json
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/tokenizer.json
khanhnto-khanhnto-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/khanhnto-khanhnto-v37_reward/reward.tensors
Job khanhnto-khanhnto-mkmlizer completed after 470.82s with status: succeeded
Stopping job with name khanhnto-khanhnto-mkmlizer
Running pipeline stage MKMLKubeTemplater
Running pipeline stage ISVCDeployer
Creating inference service khanhnto-khanhnto-v37
Waiting for inference service khanhnto-khanhnto-v37 to be ready
Inference service khanhnto-khanhnto-v37 ready after 120.61185908317566s
Running pipeline stage StressChecker
Received healthy response to inference request with status code 200 in 2.0580129623413086s
Received healthy response to inference request with status code 200 in 1.3039517402648926s
Received healthy response to inference request with status code 200 in 1.563774824142456s
Received healthy response to inference request with status code 200 in 1.3068344593048096s
Received healthy response to inference request with status code 200 in 1.802271842956543s
Received healthy response to inference request with status code 200 in 1.0368638038635254s
Received healthy response to inference request with status code 200 in 1.467423915863037s
Received healthy response to inference request with status code 200 in 1.8139591217041016s
Received healthy response to inference request with status code 200 in 1.7115962505340576s
Received healthy response to inference request with status code 200 in 1.289337158203125s
Received healthy response to inference request with status code 200 in 1.3511672019958496s
Received healthy response to inference request with status code 200 in 1.498316764831543s
Received healthy response to inference request with status code 200 in 1.4472239017486572s
Received healthy response to inference request with status code 200 in 1.3161089420318604s
Received healthy response to inference request with status code 200 in 1.1507771015167236s
Received healthy response to inference request with status code 200 in 1.464984655380249s
Received healthy response to inference request with status code 200 in 1.7928662300109863s
Received healthy response to inference request with status code 200 in 1.8448500633239746s
Received healthy response to inference request with status code 200 in 1.4182102680206299s
Received healthy response to inference request with status code 200 in 1.0108966827392578s
Received healthy response to inference request with status code 200 in 1.3914668560028076s
Received healthy response to inference request with status code 200 in 1.7270705699920654s
Received healthy response to inference request with status code 200 in 1.2918083667755127s
Received healthy response to inference request with status code 200 in 1.539269208908081s
Received healthy response to inference request with status code 200 in 1.398693323135376s
Received healthy response to inference request with status code 200 in 1.6678361892700195s
Received healthy response to inference request with status code 200 in 1.3642919063568115s
Received healthy response to inference request with status code 200 in 1.5288176536560059s
Received healthy response to inference request with status code 200 in 1.2911691665649414s
Received healthy response to inference request with status code 200 in 1.280235767364502s
Received healthy response to inference request with status code 200 in 1.272721529006958s
Received healthy response to inference request with status code 200 in 2.165599822998047s
Received healthy response to inference request with status code 200 in 1.4890217781066895s
Received healthy response to inference request with status code 200 in 1.7059478759765625s
Received healthy response to inference request with status code 200 in 1.23557448387146s
Received healthy response to inference request with status code 200 in 1.114271879196167s
Received healthy response to inference request with status code 200 in 1.710226058959961s
Received healthy response to inference request with status code 200 in 1.8246779441833496s
Received healthy response to inference request with status code 200 in 0.9221341609954834s
Received healthy response to inference request with status code 200 in 1.7331891059875488s
Received healthy response to inference request with status code 200 in 0.9923796653747559s
Received healthy response to inference request with status code 200 in 1.0076103210449219s
Received healthy response to inference request with status code 200 in 1.805574655532837s
Received healthy response to inference request with status code 200 in 0.9731853008270264s
Received healthy response to inference request with status code 200 in 0.862281322479248s
Received healthy response to inference request with status code 200 in 1.7926292419433594s
Received healthy response to inference request with status code 200 in 0.8310091495513916s
Received healthy response to inference request with status code 200 in 0.9551944732666016s
Received healthy response to inference request with status code 200 in 1.2616024017333984s
Received healthy response to inference request with status code 200 in 0.8262362480163574s
Received healthy response to inference request with status code 200 in 1.0679166316986084s
Received healthy response to inference request with status code 200 in 1.0081024169921875s
Received healthy response to inference request with status code 200 in 0.7417855262756348s
Received healthy response to inference request with status code 200 in 0.8215594291687012s
Received healthy response to inference request with status code 200 in 0.8861510753631592s
Received healthy response to inference request with status code 200 in 0.870452880859375s
Received healthy response to inference request with status code 200 in 1.7039895057678223s
Received healthy response to inference request with status code 200 in 0.7020745277404785s
Received healthy response to inference request with status code 200 in 0.9652247428894043s
Received healthy response to inference request with status code 200 in 1.1455912590026855s
Received healthy response to inference request with status code 200 in 1.1139440536499023s
Received healthy response to inference request with status code 200 in 0.8171088695526123s
Received healthy response to inference request with status code 200 in 0.5866892337799072s
Received healthy response to inference request with status code 200 in 1.2595479488372803s
Received healthy response to inference request with status code 200 in 0.878859281539917s
Received healthy response to inference request with status code 200 in 0.7331864833831787s
Received healthy response to inference request with status code 200 in 0.847783088684082s
Received healthy response to inference request with status code 200 in 0.733344554901123s
Received healthy response to inference request with status code 200 in 0.8034694194793701s
Received healthy response to inference request with status code 200 in 0.7579805850982666s
Received healthy response to inference request with status code 200 in 0.710261344909668s
Received healthy response to inference request with status code 200 in 0.7033629417419434s
Received healthy response to inference request with status code 200 in 0.6821165084838867s
Received healthy response to inference request with status code 200 in 0.8527216911315918s
Received healthy response to inference request with status code 200 in 1.8004043102264404s
Received healthy response to inference request with status code 200 in 0.9880874156951904s
Received healthy response to inference request with status code 200 in 0.9636731147766113s
Received healthy response to inference request with status code 200 in 0.7540626525878906s
Received healthy response to inference request with status code 200 in 0.912078857421875s
Received healthy response to inference request with status code 200 in 1.0214312076568604s
Received healthy response to inference request with status code 200 in 1.0130891799926758s
Received healthy response to inference request with status code 200 in 0.7337641716003418s
Received healthy response to inference request with status code 200 in 0.8973090648651123s
Received healthy response to inference request with status code 200 in 0.8335983753204346s
Received healthy response to inference request with status code 200 in 1.207535743713379s
Received healthy response to inference request with status code 200 in 0.7675700187683105s
Received healthy response to inference request with status code 200 in 0.82460618019104s
Received healthy response to inference request with status code 200 in 1.2669477462768555s
Received healthy response to inference request with status code 200 in 0.7767200469970703s
Received healthy response to inference request with status code 200 in 0.8096668720245361s
Received healthy response to inference request with status code 200 in 1.0503356456756592s
Received healthy response to inference request with status code 200 in 0.9631950855255127s
Received healthy response to inference request with status code 200 in 0.6898856163024902s
Received healthy response to inference request with status code 200 in 0.9165811538696289s
Received healthy response to inference request with status code 200 in 0.6727030277252197s
Received healthy response to inference request with status code 200 in 0.9371485710144043s
Received healthy response to inference request with status code 200 in 0.8900413513183594s
Received healthy response to inference request with status code 200 in 0.8054811954498291s
Received healthy response to inference request with status code 200 in 0.9952137470245361s
Received healthy response to inference request with status code 200 in 1.3124268054962158s
100 requests
0 failed requests
5th percentile: 0.7032985210418701
10th percentile: 0.7409833908081055
20th percentile: 0.8239968299865723
30th percentile: 0.8888742685317994
40th percentile: 0.9700010776519775
50th percentile: 1.0435997247695923
60th percentile: 1.2637405395507812
70th percentile: 1.3266264200210571
80th percentile: 1.5044169425964355
90th percentile: 1.7391331195831305
95th percentile: 1.8059938788414
99th percentile: 2.0590888309478763
mean time: 1.168079354763031
Running pipeline stage SafetyScorer
khanhnto-khanhnto_v37 status is now inactive due to auto deactivation removed underperforming models
khanhnto-khanhnto_v37 status is now deployed due to admin request

Usage Metrics

Latency Metrics