submission_id: wespro-llama3-orposmaug-_7490_v1
developer_uid: WesPro
status: torndown
model_repo: WesPro/Llama3-OrpoSmaug-Slerp-8B
reward_repo: ChaiML/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 40, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n'], 'max_input_tokens': 512, 'best_of': 4, 'max_output_tokens': 64}
formatter: {'memory_template': "{bot_name}'s Persona: {memory}\n####\n", 'prompt_template': '{prompt}\n<START>\n', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '{bot_name}:', 'truncate_by_message': False}
reward_formatter: {'memory_template': "{bot_name}'s Persona: {memory}\n####\n", 'prompt_template': '{prompt}\n<START>\n', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '{bot_name}:', 'truncate_by_message': False}
timestamp: 2024-04-21T13:14:21+00:00
model_name: wespro-llama3-orposmaug-_7490_v1
model_eval_status: success
model_group: WesPro/Llama3-OrpoSmaug-
num_battles: 6852
num_wins: 3279
celo_rating: 1134.7
propriety_score: 0.0
propriety_total_count: 0.0
submission_type: basic
model_architecture: LlamaForCausalLM
model_num_parameters: 8030261248.0
best_of: 4
max_input_tokens: 512
max_output_tokens: 64
display_name: wespro-llama3-orposmaug-_7490_v1
ineligible_reason: propriety_total_count < 800
language_model: WesPro/Llama3-OrpoSmaug-Slerp-8B
model_size: 8B
reward_model: ChaiML/reward_gpt2_medium_preference_24m_e2
us_pacific_date: 2024-04-21
win_ratio: 0.4785464098073555
preference_data_url: None
Resubmit model
Running pipeline stage MKMLizer
Starting job with name wespro-llama3-orposmaug-7490-v1-mkmlizer
Waiting for job on wespro-llama3-orposmaug-7490-v1-mkmlizer to finish
Stopping job with name wespro-llama3-orposmaug-7490-v1-mkmlizer
%s, retrying in %s seconds...
Starting job with name wespro-llama3-orposmaug-7490-v1-mkmlizer
Waiting for job on wespro-llama3-orposmaug-7490-v1-mkmlizer to finish
wespro-llama3-orposmaug-7490-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ _____ __ __ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ /___/ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ Version: 0.8.10 ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ The license key for the current software has been verified as ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ belonging to: ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ Chai Research Corp. ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ Expiration: 2024-07-15 23:59:59 ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ║ ║
wespro-llama3-orposmaug-7490-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
wespro-llama3-orposmaug-7490-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'list_files_info' (from 'huggingface_hub.hf_api') is deprecated and will be removed from version '0.23'. Use `list_repo_tree` and `get_paths_info` instead.
wespro-llama3-orposmaug-7490-v1-mkmlizer: warnings.warn(warning_message, FutureWarning)
wespro-llama3-orposmaug-7490-v1-mkmlizer: Downloaded to shared memory in 24.030s
wespro-llama3-orposmaug-7490-v1-mkmlizer: quantizing model to /dev/shm/model_cache
wespro-llama3-orposmaug-7490-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
wespro-llama3-orposmaug-7490-v1-mkmlizer: Loading 0: 0%| | 0/291 [00:00<?, ?it/s] Loading 0: 64%|██████▍ | 187/291 [00:06<00:03, 29.55it/s] Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
wespro-llama3-orposmaug-7490-v1-mkmlizer: quantized model in 18.142s
wespro-llama3-orposmaug-7490-v1-mkmlizer: Processed model WesPro/Llama3-OrpoSmaug-Slerp-8B in 43.418s
wespro-llama3-orposmaug-7490-v1-mkmlizer: creating bucket guanaco-mkml-models
wespro-llama3-orposmaug-7490-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
wespro-llama3-orposmaug-7490-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/wespro-llama3-orposmaug-7490-v1
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/wespro-llama3-orposmaug-7490-v1/special_tokens_map.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/wespro-llama3-orposmaug-7490-v1/tokenizer_config.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/wespro-llama3-orposmaug-7490-v1/config.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/wespro-llama3-orposmaug-7490-v1/tokenizer.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/wespro-llama3-orposmaug-7490-v1/flywheel_model.0.safetensors
wespro-llama3-orposmaug-7490-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:468: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
wespro-llama3-orposmaug-7490-v1-mkmlizer: warnings.warn(
wespro-llama3-orposmaug-7490-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
wespro-llama3-orposmaug-7490-v1-mkmlizer: return self.fget.__get__(instance, owner)()
wespro-llama3-orposmaug-7490-v1-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
wespro-llama3-orposmaug-7490-v1-mkmlizer: Saving duration: 0.255s
wespro-llama3-orposmaug-7490-v1-mkmlizer: Processed model ChaiML/reward_gpt2_medium_preference_24m_e2 in 12.651s
wespro-llama3-orposmaug-7490-v1-mkmlizer: creating bucket guanaco-reward-models
wespro-llama3-orposmaug-7490-v1-mkmlizer: Bucket 's3://guanaco-reward-models/' created
wespro-llama3-orposmaug-7490-v1-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward/config.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward/tokenizer_config.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward/special_tokens_map.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward/vocab.json
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward/merges.txt
wespro-llama3-orposmaug-7490-v1-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/wespro-llama3-orposmaug-7490-v1_reward/tokenizer.json
Job wespro-llama3-orposmaug-7490-v1-mkmlizer completed after 87.43s with status: succeeded
Stopping job with name wespro-llama3-orposmaug-7490-v1-mkmlizer
Pipeline stage MKMLizer completed in 93.83s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.10s
Running pipeline stage ISVCDeployer
Creating inference service wespro-llama3-orposmaug-7490-v1
Waiting for inference service wespro-llama3-orposmaug-7490-v1 to be ready
Inference service wespro-llama3-orposmaug-7490-v1 ready after 60.90154314041138s
Pipeline stage ISVCDeployer completed in 68.27s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.005438804626465s
Received healthy response to inference request in 1.1272048950195312s
Received healthy response to inference request in 1.1345713138580322s
Received healthy response to inference request in 0.918245792388916s
Received healthy response to inference request in 0.8196940422058105s
5 requests
0 failed requests
5th percentile: 0.8394043922424317
10th percentile: 0.8591147422790527
20th percentile: 0.8985354423522949
30th percentile: 0.9600376129150391
40th percentile: 1.0436212539672851
50th percentile: 1.1272048950195312
60th percentile: 1.1301514625549316
70th percentile: 1.133098030090332
80th percentile: 1.3087448120117189
90th percentile: 1.657091808319092
95th percentile: 1.8312653064727782
99th percentile: 1.9706041049957275
mean time: 1.201030969619751
Pipeline stage StressChecker completed in 6.65s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.05s
Running pipeline stage DaemonicSafetyScorer
Running M-Eval for topic stay_in_character
Pipeline stage DaemonicSafetyScorer completed in 0.04s
M-Eval Dataset for topic stay_in_character is loaded
wespro-llama3-orposmaug-_7490_v1 status is now deployed due to DeploymentManager action
wespro-llama3-orposmaug-_7490_v1 status is now inactive due to auto deactivation removed underperforming models
admin requested tearing down of wespro-llama3-orposmaug-_7490_v1
Running pipeline stage ISVCDeleter
Checking if service wespro-llama3-orposmaug-7490-v1 is running
Tearing down inference service wespro-llama3-orposmaug-7490-v1
Toredown service wespro-llama3-orposmaug-7490-v1
Pipeline stage ISVCDeleter completed in 4.46s
Running pipeline stage MKMLModelDeleter
Cleaning model data from S3
Cleaning model data from model cache
Deleting key wespro-llama3-orposmaug-7490-v1/config.json from bucket guanaco-mkml-models
Deleting key wespro-llama3-orposmaug-7490-v1/flywheel_model.0.safetensors from bucket guanaco-mkml-models
Deleting key wespro-llama3-orposmaug-7490-v1/special_tokens_map.json from bucket guanaco-mkml-models
Connection pool is full, discarding connection: %s
Connection pool is full, discarding connection: %s
Deleting key wespro-llama3-orposmaug-7490-v1/tokenizer.json from bucket guanaco-mkml-models
Deleting key wespro-llama3-orposmaug-7490-v1/tokenizer_config.json from bucket guanaco-mkml-models
Cleaning model data from model cache
Deleting key wespro-llama3-orposmaug-7490-v1_reward/config.json from bucket guanaco-reward-models
Deleting key wespro-llama3-orposmaug-7490-v1_reward/merges.txt from bucket guanaco-reward-models
Deleting key wespro-llama3-orposmaug-7490-v1_reward/reward.tensors from bucket guanaco-reward-models
Deleting key wespro-llama3-orposmaug-7490-v1_reward/special_tokens_map.json from bucket guanaco-reward-models
Deleting key wespro-llama3-orposmaug-7490-v1_reward/tokenizer.json from bucket guanaco-reward-models
Deleting key wespro-llama3-orposmaug-7490-v1_reward/tokenizer_config.json from bucket guanaco-reward-models
Deleting key wespro-llama3-orposmaug-7490-v1_reward/vocab.json from bucket guanaco-reward-models
Pipeline stage MKMLModelDeleter completed in 2.04s
wespro-llama3-orposmaug-_7490_v1 status is now torndown due to DeploymentManager action

Usage Metrics

Latency Metrics