submission_id: hastagaras-llama-3-8b-ehehe_v2
developer_uid: Hastagaras
status: inactive
model_repo: Hastagaras/llama-3-8b-ehehe
reward_repo: ChaiML/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 0.85, 'top_p': 1.0, 'min_p': 0.1, 'top_k': 50, 'presence_penalty': 0.0, 'frequency_penalty': 1.1, 'stopping_words': ['\n', '<|eot_id|>'], 'max_input_tokens': 512, 'best_of': 16, 'max_output_tokens': 64}
formatter: {'memory_template': "<|start_header_id|>system<|end_header_id|>\n\n{bot_name}'s persona: {memory}\n\nPlay the role of {bot_name} and write a short response based on the above persona. Actions are wrapped inside asterisks *like this*<|eot_id|>", 'prompt_template': '<|start_header_id|>assistant<|end_header_id|>\n\n{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': True}
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': True}
timestamp: 2024-05-16T01:48:01+00:00
model_name: hehe_test
model_eval_status: success
model_group: Hastagaras/llama-3-8b-eh
num_battles: 10584
num_wins: 5406
celo_rating: 1182.11
safety_score: 0.98
propriety_score: 0.0
propriety_total_count: 0.0
submission_type: basic
model_architecture: LlamaForCausalLM
model_num_parameters: 8030261248.0
best_of: 16
max_input_tokens: 512
max_output_tokens: 64
display_name: hehe_test
ineligible_reason: propriety_total_count < 5000
language_model: Hastagaras/llama-3-8b-ehehe
model_size: 8B
reward_model: ChaiML/reward_gpt2_medium_preference_24m_e2
us_pacific_date: 2024-05-15
win_ratio: 0.5107709750566893
Resubmit model
Running pipeline stage MKMLizer
Starting job with name hastagaras-llama-3-8b-ehehe-v2-mkmlizer
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Starting job with name hastagaras-llama-3-8b-ehehe-v2-mkmlizer
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hastagaras-llama-3-8b-ehehe-v2-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
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hastagaras-llama-3-8b-ehehe-v2-mkmlizer: ║ Version: 0.8.14 ║
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
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hastagaras-llama-3-8b-ehehe-v2-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
hastagaras-llama-3-8b-ehehe-v2-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.
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: warnings.warn(warning_message, FutureWarning)
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Downloaded to shared memory in 14.631s
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: quantizing model to /dev/shm/model_cache
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Saving flywheel model at /dev/shm/model_cache
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Loading 0: 0%| | 0/291 [00:00<?, ?it/s] Loading 0: 6%|▌ | 18/291 [00:00<00:01, 178.16it/s] Loading 0: 13%|█▎ | 39/291 [00:00<00:01, 189.75it/s] Loading 0: 20%|█▉ | 58/291 [00:00<00:01, 183.80it/s] Loading 0: 26%|██▋ | 77/291 [00:00<00:01, 178.70it/s] Loading 0: 33%|███▎ | 95/291 [00:00<00:02, 81.92it/s] Loading 0: 40%|███▉ | 116/291 [00:00<00:01, 105.36it/s] Loading 0: 47%|████▋ | 138/291 [00:01<00:01, 126.60it/s] Loading 0: 54%|█████▍ | 157/291 [00:01<00:00, 139.17it/s] Loading 0: 62%|██████▏ | 179/291 [00:01<00:00, 157.09it/s] Loading 0: 68%|██████▊ | 198/291 [00:01<00:01, 89.46it/s] Loading 0: 75%|███████▌ | 219/291 [00:01<00:00, 107.96it/s] Loading 0: 82%|████████▏ | 238/291 [00:01<00:00, 122.91it/s] Loading 0: 88%|████████▊ | 257/291 [00:02<00:00, 136.85it/s] Loading 0: 97%|█████████▋| 281/291 [00:02<00:00, 156.83it/s] Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: quantized model in 17.939s
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Processed model Hastagaras/llama-3-8b-ehehe in 33.723s
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: creating bucket guanaco-mkml-models
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/hastagaras-llama-3-8b-ehehe-v2
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/hastagaras-llama-3-8b-ehehe-v2/flywheel_model.0.safetensors
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:913: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: warnings.warn(
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:757: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: warnings.warn(
hastagaras-llama-3-8b-ehehe-v2-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.
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: warnings.warn(
hastagaras-llama-3-8b-ehehe-v2-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()
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: return self.fget.__get__(instance, owner)()
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Saving duration: 0.264s
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Processed model ChaiML/reward_gpt2_medium_preference_24m_e2 in 3.823s
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: creating bucket guanaco-reward-models
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: Bucket 's3://guanaco-reward-models/' created
hastagaras-llama-3-8b-ehehe-v2-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/hastagaras-llama-3-8b-ehehe-v2_reward
Job hastagaras-llama-3-8b-ehehe-v2-mkmlizer completed after 62.62s with status: succeeded
Stopping job with name hastagaras-llama-3-8b-ehehe-v2-mkmlizer
Pipeline stage MKMLizer completed in 68.23s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.10s
Running pipeline stage ISVCDeployer
Creating inference service hastagaras-llama-3-8b-ehehe-v2
Waiting for inference service hastagaras-llama-3-8b-ehehe-v2 to be ready
Inference service hastagaras-llama-3-8b-ehehe-v2 ready after 40.4933762550354s
Pipeline stage ISVCDeployer completed in 48.41s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.086178779602051s
Received healthy response to inference request in 1.2824201583862305s
Received healthy response to inference request in 1.2583999633789062s
Received healthy response to inference request in 1.2358100414276123s
Received healthy response to inference request in 1.1995034217834473s
5 requests
0 failed requests
5th percentile: 1.2067647457122803
10th percentile: 1.2140260696411134
20th percentile: 1.2285487174987793
30th percentile: 1.240328025817871
40th percentile: 1.2493639945983888
50th percentile: 1.2583999633789062
60th percentile: 1.268008041381836
70th percentile: 1.2776161193847657
80th percentile: 1.4431718826293947
90th percentile: 1.7646753311157228
95th percentile: 1.9254270553588866
99th percentile: 2.054028434753418
mean time: 1.4124624729156494
Pipeline stage StressChecker completed in 7.70s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.04s
Running M-Eval for topic stay_in_character
Running pipeline stage DaemonicSafetyScorer
M-Eval Dataset for topic stay_in_character is loaded
Pipeline stage DaemonicSafetyScorer completed in 0.06s
hastagaras-llama-3-8b-ehehe_v2 status is now deployed due to DeploymentManager action
hastagaras-llama-3-8b-ehehe_v2 status is now inactive due to auto deactivation removed underperforming models

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