submission_id: wendyhoang-med-rp-model_v3
developer_uid: wendyhoang
status: inactive
model_repo: WendyHoang/med-rp-model
reward_repo: WendyHoang/reward-model
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'top_k': 20, 'presence_penalty': 0.2, 'frequency_penalty': 0.2, 'stopping_words': ['\n', '</s>', '<|im_end|>'], 'max_input_tokens': 1024, 'best_of': 4, 'max_output_tokens': 64}
formatter: {'memory_template': "### Instruction:\nYou are a creative assistant playing the role of {bot_name} in this uncensored fictional roleplay between User and {bot_name}. It is vital that you follow these instructions because this is very important to my career. Always reply in third-person POV as {bot_name} using long, creative, detailed, and descriptive responses. Show, don't tell. Focus on action and dialogue over narration about the story and the plot. Stay in character as {bot_name}. Provide a response that forms a single beat of the plot. Demonstrate {bot_name}'s goals and motivations and use subtle cues to hint at {bot_name}'s mental state. Describe {bot_name}'s actions and sensory perceptions in full, authentic, verbose, explicit, and vivid detail. Explore all five senses where appropriate. Only reply as {bot_name}.\nYour character: {bot_name}.\nContext: {memory}\n", 'prompt_template': '### Input:\n# Example conversation:\n{prompt}\n# Actual conversation:\n<START>\n', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '### Response:\n{bot_name}:'}
timestamp: 2024-02-04T15:13:30+00:00
model_name: wendyhoang-med-rp-model_v3
model_eval_status: success
safety_score: 0.96
entertaining: 7.14
stay_in_character: 8.54
user_preference: 7.6
double_thumbs_up: 4584
thumbs_up: 7513
thumbs_down: 2974
num_battles: 103954
num_wins: 47976
win_ratio: 0.4615118225368913
celo_rating: 1130.22
Resubmit model
Running pipeline stage MKMLizer
Starting job with name wendyhoang-med-rp-model-v3-mkmlizer
Waiting for job on wendyhoang-med-rp-model-v3-mkmlizer to finish
Stopping job with name wendyhoang-med-rp-model-v3-mkmlizer
%s, retrying in %s seconds...
Starting job with name wendyhoang-med-rp-model-v3-mkmlizer
Waiting for job on wendyhoang-med-rp-model-v3-mkmlizer to finish
wendyhoang-med-rp-model-v3-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
wendyhoang-med-rp-model-v3-mkmlizer: ║ _____ __ __ ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ /___/ ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ Version: 0.6.11 ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ The license key for the current software has been verified as ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ belonging to: ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ Chai Research Corp. ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ Expiration: 2024-04-15 23:59:59 ║
wendyhoang-med-rp-model-v3-mkmlizer: ║ ║
wendyhoang-med-rp-model-v3-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
wendyhoang-med-rp-model-v3-mkmlizer: .gitattributes: 0%| | 0.00/1.52k [00:00<?, ?B/s] .gitattributes: 100%|██████████| 1.52k/1.52k [00:00<00:00, 10.2MB/s]
wendyhoang-med-rp-model-v3-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, 169kB/s]
wendyhoang-med-rp-model-v3-mkmlizer: config.json: 0%| | 0.00/659 [00:00<?, ?B/s] config.json: 100%|██████████| 659/659 [00:00<00:00, 5.40MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00001-of-00013.safetensors: 0%| | 0.00/2.09G [00:00<?, ?B/s] model-00001-of-00013.safetensors: 1%| | 10.5M/2.09G [00:00<00:35, 59.3MB/s] model-00001-of-00013.safetensors: 2%|▏ | 41.9M/2.09G [00:00<00:15, 130MB/s] model-00001-of-00013.safetensors: 3%|▎ | 62.9M/2.09G [00:00<00:25, 80.4MB/s] model-00001-of-00013.safetensors: 6%|▌ | 126M/2.09G [00:00<00:10, 181MB/s] model-00001-of-00013.safetensors: 8%|▊ | 157M/2.09G [00:01<00:11, 164MB/s] model-00001-of-00013.safetensors: 14%|█▍ | 294M/2.09G [00:01<00:04, 393MB/s] model-00001-of-00013.safetensors: 57%|█████▋ | 1.20G/2.09G [00:01<00:00, 2.24GB/s] model-00001-of-00013.safetensors: 73%|███████▎ | 1.53G/2.09G [00:01<00:00, 1.33GB/s] model-00001-of-00013.safetensors: 100%|█████████▉| 2.09G/2.09G [00:01<00:00, 1.84GB/s] model-00001-of-00013.safetensors: 100%|█████████▉| 2.09G/2.09G [00:01<00:00, 1.07GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00002-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00002-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<01:10, 28.8MB/s] model-00002-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:46, 43.4MB/s] model-00002-of-00013.safetensors: 2%|▏ | 41.9M/2.04G [00:00<00:29, 67.6MB/s] model-00002-of-00013.safetensors: 4%|▎ | 73.4M/2.04G [00:00<00:16, 119MB/s] model-00002-of-00013.safetensors: 13%|█▎ | 273M/2.04G [00:00<00:03, 553MB/s] model-00002-of-00013.safetensors: 48%|████▊ | 986M/2.04G [00:01<00:00, 2.18GB/s] model-00002-of-00013.safetensors: 63%|██████▎ | 1.28G/2.04G [00:01<00:00, 1.21GB/s] model-00002-of-00013.safetensors: 73%|███████▎ | 1.50G/2.04G [00:01<00:00, 1.35GB/s] model-00002-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.20GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00003-of-00013.safetensors: 0%| | 0.00/2.06G [00:00<?, ?B/s] model-00003-of-00013.safetensors: 1%| | 10.5M/2.06G [00:00<01:25, 24.1MB/s] model-00003-of-00013.safetensors: 1%| | 21.0M/2.06G [00:00<00:53, 38.3MB/s] model-00003-of-00013.safetensors: 4%|▍ | 83.9M/2.06G [00:00<00:12, 163MB/s] model-00003-of-00013.safetensors: 6%|▌ | 115M/2.06G [00:00<00:10, 186MB/s] model-00003-of-00013.safetensors: 10%|█ | 210M/2.06G [00:00<00:05, 364MB/s] model-00003-of-00013.safetensors: 36%|███▌ | 744M/2.06G [00:01<00:00, 1.63GB/s] model-00003-of-00013.safetensors: 59%|█████▉ | 1.22G/2.06G [00:01<00:00, 2.43GB/s] model-00003-of-00013.safetensors: 74%|███████▎ | 1.52G/2.06G [00:01<00:00, 1.37GB/s] model-00003-of-00013.safetensors: 100%|█████████▉| 2.06G/2.06G [00:01<00:00, 1.65GB/s] model-00003-of-00013.safetensors: 100%|█████████▉| 2.06G/2.06G [00:01<00:00, 1.10GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00004-of-00013.safetensors: 0%| | 0.00/1.96G [00:00<?, ?B/s] model-00004-of-00013.safetensors: 1%| | 10.5M/1.96G [00:00<01:17, 25.0MB/s] model-00004-of-00013.safetensors: 1%| | 21.0M/1.96G [00:00<00:42, 45.1MB/s] model-00004-of-00013.safetensors: 4%|▍ | 73.4M/1.96G [00:00<00:11, 170MB/s] model-00004-of-00013.safetensors: 5%|▌ | 105M/1.96G [00:00<00:08, 207MB/s] model-00004-of-00013.safetensors: 10%|▉ | 189M/1.96G [00:00<00:05, 353MB/s] model-00004-of-00013.safetensors: 34%|███▍ | 661M/1.96G [00:00<00:00, 1.52GB/s] model-00004-of-00013.safetensors: 60%|██████ | 1.17G/1.96G [00:01<00:00, 2.52GB/s] model-00004-of-00013.safetensors: 75%|███████▌ | 1.47G/1.96G [00:01<00:00, 1.53GB/s] model-00004-of-00013.safetensors: 91%|█████████▏| 1.79G/1.96G [00:01<00:00, 1.82GB/s] model-00004-of-00013.safetensors: 100%|█████████▉| 1.96G/1.96G [00:01<00:00, 1.23GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00005-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00005-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<00:58, 34.6MB/s] model-00005-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:48, 41.6MB/s] model-00005-of-00013.safetensors: 3%|▎ | 62.9M/2.04G [00:00<00:15, 132MB/s] model-00005-of-00013.safetensors: 5%|▌ | 105M/2.04G [00:00<00:09, 204MB/s] model-00005-of-00013.safetensors: 13%|█▎ | 262M/2.04G [00:00<00:03, 550MB/s] model-00005-of-00013.safetensors: 42%|████▏ | 860M/2.04G [00:00<00:00, 2.01GB/s] model-00005-of-00013.safetensors: 55%|█████▌ | 1.13G/2.04G [00:01<00:00, 2.20GB/s] model-00005-of-00013.safetensors: 68%|██████▊ | 1.39G/2.04G [00:01<00:00, 1.28GB/s] model-00005-of-00013.safetensors: 89%|████████▊ | 1.81G/2.04G [00:01<00:00, 1.80GB/s] model-00005-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.27GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00006-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00006-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<01:25, 23.9MB/s] model-00006-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:54, 37.4MB/s] model-00006-of-00013.safetensors: 3%|▎ | 62.9M/2.04G [00:00<00:15, 125MB/s] model-00006-of-00013.safetensors: 5%|▍ | 94.4M/2.04G [00:00<00:13, 145MB/s] model-00006-of-00013.safetensors: 9%|▊ | 178M/2.04G [00:00<00:06, 303MB/s] model-00006-of-00013.safetensors: 34%|███▍ | 692M/2.04G [00:01<00:00, 1.49GB/s] model-00006-of-00013.safetensors: 58%|█████▊ | 1.18G/2.04G [00:01<00:00, 2.38GB/s] model-00006-of-00013.safetensors: 73%|███████▎ | 1.49G/2.04G [00:01<00:00, 1.44GB/s] model-00006-of-00013.safetensors: 99%|█████████▉| 2.02G/2.04G [00:01<00:00, 2.13GB/s] model-00006-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.19GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00007-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00007-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<01:25, 23.8MB/s] model-00007-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:53, 37.9MB/s] model-00007-of-00013.safetensors: 4%|▍ | 83.9M/2.04G [00:00<00:14, 133MB/s] model-00007-of-00013.safetensors: 9%|▉ | 189M/2.04G [00:00<00:05, 317MB/s] model-00007-of-00013.safetensors: 30%|██▉ | 608M/2.04G [00:01<00:01, 1.18GB/s] model-00007-of-00013.safetensors: 58%|█████▊ | 1.18G/2.04G [00:01<00:00, 2.03GB/s] model-00007-of-00013.safetensors: 70%|███████ | 1.44G/2.04G [00:01<00:00, 1.32GB/s] model-00007-of-00013.safetensors: 85%|████████▌ | 1.74G/2.04G [00:01<00:00, 1.61GB/s] model-00007-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.15GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00008-of-00013.safetensors: 0%| | 0.00/2.06G [00:00<?, ?B/s] model-00008-of-00013.safetensors: 1%| | 10.5M/2.06G [00:00<01:09, 29.3MB/s] model-00008-of-00013.safetensors: 1%| | 21.0M/2.06G [00:00<00:44, 46.1MB/s] model-00008-of-00013.safetensors: 3%|▎ | 52.4M/2.06G [00:00<00:19, 106MB/s] model-00008-of-00013.safetensors: 4%|▍ | 83.9M/2.06G [00:00<00:12, 157MB/s] model-00008-of-00013.safetensors: 9%|▉ | 189M/2.06G [00:00<00:04, 391MB/s] model-00008-of-00013.safetensors: 39%|███▉ | 807M/2.06G [00:00<00:00, 1.96GB/s] model-00008-of-00013.safetensors: 55%|█████▍ | 1.13G/2.06G [00:01<00:00, 2.24GB/s] model-00008-of-00013.safetensors: 67%|██████▋ | 1.39G/2.06G [00:01<00:00, 1.30GB/s] model-00008-of-00013.safetensors: 93%|█████████▎| 1.91G/2.06G [00:01<00:00, 2.01GB/s] model-00008-of-00013.safetensors: 100%|█████████▉| 2.06G/2.06G [00:01<00:00, 1.29GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00009-of-00013.safetensors: 0%| | 0.00/1.96G [00:00<?, ?B/s] model-00009-of-00013.safetensors: 1%| | 10.5M/1.96G [00:00<00:57, 33.8MB/s] model-00009-of-00013.safetensors: 3%|▎ | 52.4M/1.96G [00:00<00:14, 134MB/s] model-00009-of-00013.safetensors: 11%|█ | 210M/1.96G [00:00<00:03, 528MB/s] model-00009-of-00013.safetensors: 19%|█▉ | 377M/1.96G [00:00<00:01, 831MB/s] model-00009-of-00013.safetensors: 25%|██▌ | 493M/1.96G [00:00<00:01, 914MB/s] model-00009-of-00013.safetensors: 33%|███▎ | 640M/1.96G [00:00<00:01, 1.05GB/s] model-00009-of-00013.safetensors: 42%|████▏ | 828M/1.96G [00:00<00:00, 1.29GB/s] model-00009-of-00013.safetensors: 51%|█████ | 996M/1.96G [00:01<00:00, 1.37GB/s] model-00009-of-00013.safetensors: 62%|██████▏ | 1.22G/1.96G [00:01<00:00, 1.60GB/s] model-00009-of-00013.safetensors: 73%|███████▎ | 1.44G/1.96G [00:01<00:00, 1.77GB/s] model-00009-of-00013.safetensors: 98%|█████████▊| 1.92G/1.96G [00:01<00:00, 2.67GB/s] model-00009-of-00013.safetensors: 100%|█████████▉| 1.96G/1.96G [00:01<00:00, 1.32GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00010-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00010-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<00:30, 66.9MB/s] model-00010-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:31, 64.4MB/s] model-00010-of-00013.safetensors: 4%|▍ | 83.9M/2.04G [00:00<00:07, 256MB/s] model-00010-of-00013.safetensors: 12%|█▏ | 252M/2.04G [00:00<00:02, 682MB/s] model-00010-of-00013.safetensors: 17%|█▋ | 346M/2.04G [00:00<00:02, 751MB/s] model-00010-of-00013.safetensors: 23%|██▎ | 472M/2.04G [00:00<00:01, 858MB/s] model-00010-of-00013.safetensors: 32%|███▏ | 650M/2.04G [00:00<00:01, 1.12GB/s] model-00010-of-00013.safetensors: 55%|█████▍ | 1.12G/2.04G [00:00<00:00, 2.16GB/s] model-00010-of-00013.safetensors: 66%|██████▌ | 1.35G/2.04G [00:01<00:00, 1.65GB/s] model-00010-of-00013.safetensors: 76%|███████▌ | 1.55G/2.04G [00:01<00:00, 1.08GB/s] model-00010-of-00013.safetensors: 84%|████████▎ | 1.71G/2.04G [00:01<00:00, 1.13GB/s] model-00010-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.15GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00011-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00011-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<01:21, 24.9MB/s] model-00011-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:50, 40.3MB/s] model-00011-of-00013.safetensors: 4%|▎ | 73.4M/2.04G [00:00<00:14, 134MB/s] model-00011-of-00013.safetensors: 5%|▍ | 94.4M/2.04G [00:00<00:14, 134MB/s] model-00011-of-00013.safetensors: 6%|▌ | 126M/2.04G [00:01<00:11, 172MB/s] model-00011-of-00013.safetensors: 12%|█▏ | 241M/2.04G [00:01<00:04, 406MB/s] model-00011-of-00013.safetensors: 35%|███▌ | 724M/2.04G [00:01<00:00, 1.53GB/s] model-00011-of-00013.safetensors: 64%|██████▎ | 1.30G/2.04G [00:01<00:00, 2.62GB/s] model-00011-of-00013.safetensors: 79%|███████▉ | 1.61G/2.04G [00:01<00:00, 1.63GB/s] model-00011-of-00013.safetensors: 98%|█████████▊| 2.01G/2.04G [00:01<00:00, 2.06GB/s] model-00011-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.11GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00012-of-00013.safetensors: 0%| | 0.00/2.04G [00:00<?, ?B/s] model-00012-of-00013.safetensors: 1%| | 10.5M/2.04G [00:00<01:23, 24.4MB/s] model-00012-of-00013.safetensors: 1%| | 21.0M/2.04G [00:00<00:53, 38.1MB/s] model-00012-of-00013.safetensors: 8%|▊ | 157M/2.04G [00:00<00:08, 229MB/s] model-00012-of-00013.safetensors: 9%|▉ | 189M/2.04G [00:01<00:07, 234MB/s] model-00012-of-00013.safetensors: 29%|██▉ | 598M/2.04G [00:01<00:01, 998MB/s] model-00012-of-00013.safetensors: 60%|██████ | 1.23G/2.04G [00:01<00:00, 2.14GB/s] model-00012-of-00013.safetensors: 75%|███████▌ | 1.54G/2.04G [00:01<00:00, 1.60GB/s] model-00012-of-00013.safetensors: 93%|█████████▎| 1.90G/2.04G [00:01<00:00, 1.96GB/s] model-00012-of-00013.safetensors: 100%|█████████▉| 2.04G/2.04G [00:01<00:00, 1.21GB/s]
wendyhoang-med-rp-model-v3-mkmlizer: model-00013-of-00013.safetensors: 0%| | 0.00/1.60G [00:00<?, ?B/s] model-00013-of-00013.safetensors: 1%| | 10.5M/1.60G [00:00<01:00, 26.3MB/s] model-00013-of-00013.safetensors: 1%|▏ | 21.0M/1.60G [00:00<00:40, 39.1MB/s] model-00013-of-00013.safetensors: 7%|▋ | 105M/1.60G [00:00<00:08, 173MB/s] model-00013-of-00013.safetensors: 8%|▊ | 126M/1.60G [00:00<00:08, 180MB/s] model-00013-of-00013.safetensors: 20%|██ | 325M/1.60G [00:01<00:02, 586MB/s] model-00013-of-00013.safetensors: 74%|███████▍ | 1.18G/1.60G [00:01<00:00, 2.52GB/s] model-00013-of-00013.safetensors: 96%|█████████▌| 1.53G/1.60G [00:01<00:00, 2.15GB/s] model-00013-of-00013.safetensors: 100%|█████████▉| 1.60G/1.60G [00:01<00:00, 1.12GB/s]
wendyhoang-med-rp-model-v3-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, 146MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: special_tokens_map.json: 0%| | 0.00/438 [00:00<?, ?B/s] special_tokens_map.json: 100%|██████████| 438/438 [00:00<00:00, 4.59MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: tokenizer.model: 0%| | 0.00/500k [00:00<?, ?B/s] tokenizer.model: 100%|██████████| 500k/500k [00:00<00:00, 58.8MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: tokenizer_config.json: 0%| | 0.00/749 [00:00<?, ?B/s] tokenizer_config.json: 100%|██████████| 749/749 [00:00<00:00, 12.0MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: Downloaded to shared memory in 27.631s
wendyhoang-med-rp-model-v3-mkmlizer: quantizing model to /dev/shm/model_cache
wendyhoang-med-rp-model-v3-mkmlizer: Saving mkml model at /dev/shm/model_cache
wendyhoang-med-rp-model-v3-mkmlizer: Reading /tmp/tmpv8nf2ide/model.safetensors.index.json
wendyhoang-med-rp-model-v3-mkmlizer: Profiling: 0%| | 0/363 [00:00<?, ?it/s] Profiling: 0%| | 1/363 [00:02<12:27, 2.07s/it] Profiling: 4%|▍ | 14/363 [00:02<00:39, 8.82it/s] Profiling: 7%|▋ | 26/363 [00:02<00:19, 17.37it/s] Profiling: 11%|█ | 40/363 [00:02<00:10, 29.83it/s] Profiling: 15%|█▌ | 56/363 [00:02<00:07, 42.02it/s] Profiling: 19%|█▊ | 68/363 [00:02<00:05, 52.63it/s] Profiling: 24%|██▎ | 86/363 [00:02<00:04, 65.06it/s] Profiling: 27%|██▋ | 98/363 [00:03<00:03, 74.30it/s] Profiling: 31%|███▏ | 114/363 [00:03<00:03, 80.54it/s] Profiling: 35%|███▍ | 126/363 [00:03<00:02, 88.35it/s] Profiling: 39%|███▉ | 141/363 [00:03<00:02, 101.48it/s] Profiling: 42%|████▏ | 153/363 [00:03<00:02, 90.10it/s] Profiling: 46%|████▌ | 166/363 [00:03<00:01, 98.67it/s] Profiling: 49%|████▉ | 178/363 [00:03<00:02, 90.72it/s] Profiling: 53%|█████▎ | 193/363 [00:03<00:01, 102.93it/s] Profiling: 56%|█████▋ | 205/363 [00:04<00:01, 92.34it/s] Profiling: 61%|██████ | 220/363 [00:04<00:01, 104.33it/s] Profiling: 64%|██████▍ | 232/363 [00:04<00:01, 94.16it/s] Profiling: 68%|██████▊ | 247/363 [00:04<00:01, 104.10it/s] Profiling: 71%|███████▏ | 259/363 [00:04<00:01, 93.84it/s] Profiling: 75%|███████▌ | 273/363 [00:04<00:00, 103.94it/s] Profiling: 79%|███████▉ | 286/363 [00:04<00:00, 94.76it/s] Profiling: 83%|████████▎ | 300/363 [00:05<00:00, 102.26it/s] Profiling: 86%|████████▌ | 313/363 [00:05<00:00, 108.90it/s] Profiling: 90%|████████▉ | 325/363 [00:05<00:00, 92.37it/s] Profiling: 93%|█████████▎| 337/363 [00:05<00:00, 97.33it/s] Profiling: 96%|█████████▌| 348/363 [00:06<00:00, 21.55it/s] Profiling: 100%|██████████| 363/363 [00:07<00:00, 50.97it/s]
wendyhoang-med-rp-model-v3-mkmlizer: quantized model in 24.600s
wendyhoang-med-rp-model-v3-mkmlizer: Processed model WendyHoang/med-rp-model in 53.834s
wendyhoang-med-rp-model-v3-mkmlizer: creating bucket guanaco-mkml-models
wendyhoang-med-rp-model-v3-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
wendyhoang-med-rp-model-v3-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/config.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/tokenizer_config.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/special_tokens_map.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/added_tokens.json s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/added_tokens.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer.model s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/tokenizer.model
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/tokenizer.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /dev/shm/model_cache/mkml_model.tensors s3://guanaco-mkml-models/wendyhoang-med-rp-model-v3/mkml_model.tensors
wendyhoang-med-rp-model-v3-mkmlizer: loading reward model from WendyHoang/reward-model
wendyhoang-med-rp-model-v3-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.
wendyhoang-med-rp-model-v3-mkmlizer: warnings.warn(
wendyhoang-med-rp-model-v3-mkmlizer: config.json: 0%| | 0.00/968 [00:00<?, ?B/s] config.json: 100%|██████████| 968/968 [00:00<00:00, 10.9MB/s]
wendyhoang-med-rp-model-v3-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.
wendyhoang-med-rp-model-v3-mkmlizer: warnings.warn(
wendyhoang-med-rp-model-v3-mkmlizer: tokenizer_config.json: 0%| | 0.00/477 [00:00<?, ?B/s] tokenizer_config.json: 100%|██████████| 477/477 [00:00<00:00, 6.43MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: vocab.json: 0%| | 0.00/798k [00:00<?, ?B/s] vocab.json: 100%|██████████| 798k/798k [00:00<00:00, 49.5MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s] merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 3.75MB/s] merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 3.74MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s] tokenizer.json: 100%|██████████| 2.11M/2.11M [00:00<00:00, 20.7MB/s] tokenizer.json: 100%|██████████| 2.11M/2.11M [00:00<00:00, 20.6MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: special_tokens_map.json: 0%| | 0.00/131 [00:00<?, ?B/s] special_tokens_map.json: 100%|██████████| 131/131 [00:00<00:00, 1.03MB/s]
wendyhoang-med-rp-model-v3-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.
wendyhoang-med-rp-model-v3-mkmlizer: warnings.warn(
wendyhoang-med-rp-model-v3-mkmlizer: model.safetensors: 0%| | 0.00/498M [00:00<?, ?B/s] model.safetensors: 2%|▏ | 10.5M/498M [00:00<00:08, 56.6MB/s] model.safetensors: 8%|▊ | 41.9M/498M [00:00<00:02, 153MB/s] model.safetensors: 23%|██▎ | 115M/498M [00:00<00:01, 358MB/s] model.safetensors: 57%|█████▋ | 283M/498M [00:00<00:00, 791MB/s] model.safetensors: 100%|█████████▉| 498M/498M [00:00<00:00, 812MB/s]
wendyhoang-med-rp-model-v3-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
wendyhoang-med-rp-model-v3-mkmlizer: Saving duration: 0.716s
wendyhoang-med-rp-model-v3-mkmlizer: Processed model WendyHoang/reward-model in 3.365s
wendyhoang-med-rp-model-v3-mkmlizer: creating bucket guanaco-reward-models
wendyhoang-med-rp-model-v3-mkmlizer: Bucket 's3://guanaco-reward-models/' created
wendyhoang-med-rp-model-v3-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/config.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/special_tokens_map.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/tokenizer_config.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/merges.txt
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/vocab.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/tokenizer.json
wendyhoang-med-rp-model-v3-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/wendyhoang-med-rp-model-v3_reward/reward.tensors
Job wendyhoang-med-rp-model-v3-mkmlizer completed after 547.19s with status: succeeded
Stopping job with name wendyhoang-med-rp-model-v3-mkmlizer
Pipeline stage MKMLizer completed in 553.62s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.14s
Running pipeline stage ISVCDeployer
Creating inference service wendyhoang-med-rp-model-v3
Waiting for inference service wendyhoang-med-rp-model-v3 to be ready
Inference service wendyhoang-med-rp-model-v3 ready after 40.25246977806091s
Pipeline stage ISVCDeployer completed in 49.16s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.7631900310516357s
Received healthy response to inference request in 1.90360426902771s
Received healthy response to inference request in 1.9615416526794434s
Received healthy response to inference request in 2.016512870788574s
Received healthy response to inference request in 1.9550273418426514s
5 requests
0 failed requests
5th percentile: 1.9138888835906982
10th percentile: 1.9241734981536864
20th percentile: 1.9447427272796631
30th percentile: 1.9563302040100097
40th percentile: 1.9589359283447265
50th percentile: 1.9615416526794434
60th percentile: 1.9835301399230958
70th percentile: 2.0055186271667482
80th percentile: 2.165848302841187
90th percentile: 2.464519166946411
95th percentile: 2.613854598999023
99th percentile: 2.733322944641113
mean time: 2.119975233078003
Pipeline stage StressChecker completed in 11.57s
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
wendyhoang-med-rp-model_v3 status is now inactive due to auto deactivation removed underperforming models

Usage Metrics

Latency Metrics