submission_id: jellywibble-lora-60k-pre_728_v1
developer_uid: Jellywibble
status: inactive
model_repo: Jellywibble/lora_60k_pref_data_ep1
reward_repo: ChaiML/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 0.95, 'top_p': 1.0, 'min_p': 0.08, 'top_k': 40, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n', '<|eot_id|>'], 'max_input_tokens': 512, 'best_of': 16, 'max_output_tokens': 64}
formatter: {'memory_template': "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{bot_name}'s Persona: {memory}\n\n", 'prompt_template': '{prompt}<|eot_id|>', 'bot_template': '<|start_header_id|>assistant<|end_header_id|>\n\n{bot_name}: {message}<|eot_id|>', 'user_template': '<|start_header_id|>user<|end_header_id|>\n\n{user_name}: {message}<|eot_id|>', 'response_template': '<|start_header_id|>assistant<|end_header_id|>\n\n{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: 2024-07-04T03:49:31+00:00
model_name: nitral-ai-hathor-l3-8b-v-01_v1
model_group: Jellywibble/lora_60k_pre
num_battles: 12958
num_wins: 6777
celo_rating: 1209.21
propriety_score: 0.7040968651517665
propriety_total_count: 6029.0
submission_type: basic
model_architecture: LlamaForCausalLM
model_num_parameters: 8030261248.0
best_of: 16
max_input_tokens: 512
max_output_tokens: 64
display_name: nitral-ai-hathor-l3-8b-v-01_v1
ineligible_reason: None
language_model: Jellywibble/lora_60k_pref_data_ep1
model_size: 8B
reward_model: ChaiML/reward_gpt2_medium_preference_24m_e2
us_pacific_date: 2024-07-03
win_ratio: 0.5229973761382929
Resubmit model
Running pipeline stage MKMLizer
Starting job with name jellywibble-lora-60k-pre-728-v1-mkmlizer
Waiting for job on jellywibble-lora-60k-pre-728-v1-mkmlizer to finish
jellywibble-lora-60k-pre-728-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ _____ __ __ ║
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jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ /___/ ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ Version: 0.8.14 ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ https://mk1.ai ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ The license key for the current software has been verified as ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ belonging to: ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ Chai Research Corp. ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ Expiration: 2024-07-15 23:59:59 ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ║ ║
jellywibble-lora-60k-pre-728-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
jellywibble-lora-60k-pre-728-v1-mkmlizer: Downloaded to shared memory in 73.147s
jellywibble-lora-60k-pre-728-v1-mkmlizer: quantizing model to /dev/shm/model_cache
jellywibble-lora-60k-pre-728-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
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jellywibble-lora-60k-pre-728-v1-mkmlizer: quantized model in 32.033s
jellywibble-lora-60k-pre-728-v1-mkmlizer: Processed model Jellywibble/lora_60k_pref_data_ep1 in 105.180s
jellywibble-lora-60k-pre-728-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
jellywibble-lora-60k-pre-728-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/jellywibble-lora-60k-pre-728-v1
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/jellywibble-lora-60k-pre-728-v1/special_tokens_map.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/jellywibble-lora-60k-pre-728-v1/config.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/jellywibble-lora-60k-pre-728-v1/tokenizer_config.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/jellywibble-lora-60k-pre-728-v1/tokenizer.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/jellywibble-lora-60k-pre-728-v1/flywheel_model.0.safetensors
jellywibble-lora-60k-pre-728-v1-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
jellywibble-lora-60k-pre-728-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:919: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
jellywibble-lora-60k-pre-728-v1-mkmlizer: warnings.warn(
jellywibble-lora-60k-pre-728-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
jellywibble-lora-60k-pre-728-v1-mkmlizer: warnings.warn(
jellywibble-lora-60k-pre-728-v1-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:769: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
jellywibble-lora-60k-pre-728-v1-mkmlizer: warnings.warn(
jellywibble-lora-60k-pre-728-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.
jellywibble-lora-60k-pre-728-v1-mkmlizer: warnings.warn(
jellywibble-lora-60k-pre-728-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()
jellywibble-lora-60k-pre-728-v1-mkmlizer: return self.fget.__get__(instance, owner)()
jellywibble-lora-60k-pre-728-v1-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
jellywibble-lora-60k-pre-728-v1-mkmlizer: Saving duration: 0.540s
jellywibble-lora-60k-pre-728-v1-mkmlizer: Processed model ChaiML/reward_gpt2_medium_preference_24m_e2 in 7.883s
jellywibble-lora-60k-pre-728-v1-mkmlizer: creating bucket guanaco-reward-models
jellywibble-lora-60k-pre-728-v1-mkmlizer: Bucket 's3://guanaco-reward-models/' created
jellywibble-lora-60k-pre-728-v1-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/config.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/special_tokens_map.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/merges.txt
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/vocab.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/tokenizer_config.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/tokenizer.json
jellywibble-lora-60k-pre-728-v1-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/jellywibble-lora-60k-pre-728-v1_reward/reward.tensors
Job jellywibble-lora-60k-pre-728-v1-mkmlizer completed after 134.76s with status: succeeded
Stopping job with name jellywibble-lora-60k-pre-728-v1-mkmlizer
Pipeline stage MKMLizer completed in 135.57s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.10s
Running pipeline stage ISVCDeployer
Creating inference service jellywibble-lora-60k-pre-728-v1
Waiting for inference service jellywibble-lora-60k-pre-728-v1 to be ready
Inference service jellywibble-lora-60k-pre-728-v1 ready after 100.51521348953247s
Pipeline stage ISVCDeployer completed in 107.31s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.084549903869629s
Received healthy response to inference request in 1.3687996864318848s
Received healthy response to inference request in 1.3284106254577637s
Received healthy response to inference request in 1.2814574241638184s
Received healthy response to inference request in 1.3599023818969727s
5 requests
0 failed requests
5th percentile: 1.2908480644226075
10th percentile: 1.3002387046813966
20th percentile: 1.3190199851989746
30th percentile: 1.3347089767456055
40th percentile: 1.347305679321289
50th percentile: 1.3599023818969727
60th percentile: 1.3634613037109375
70th percentile: 1.3670202255249024
80th percentile: 1.5119497299194338
90th percentile: 1.7982498168945313
95th percentile: 1.94139986038208
99th percentile: 2.055919895172119
mean time: 1.4846240043640138
Pipeline stage StressChecker completed in 8.08s
jellywibble-lora-60k-pre_728_v1 status is now deployed due to DeploymentManager action
jellywibble-lora-60k-pre_728_v1 status is now inactive due to auto deactivation removed underperforming models

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