submission_id: nousresearch-meta-llama_4941_v51
developer_uid: Meliodia
status: inactive
model_repo: NousResearch/Meta-Llama-3-8B-Instruct
reward_repo: ChaiML/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 100, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n'], 'max_input_tokens': 512, 'best_of': 16, '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: 2024-04-29T20:33:47+00:00
model_name: NousL3-8B-Instruct
model_eval_status: success
double_thumbs_up: 83
thumbs_up: 146
thumbs_down: 81
num_battles: 6742
num_wins: 3466
celo_rating: 1166.95
entertaining: 7.02
stay_in_character: 8.62
user_preference: 7.34
safety_score: 0.95
submission_type: basic
model_architecture: LlamaForCausalLM
model_num_parameters: 8030261248.0
best_of: 16
max_input_tokens: 512
max_output_tokens: 64
display_name: NousL3-8B-Instruct
double_thumbs_up_ratio: 0.267741935483871
feedback_count: 310
ineligible_reason: None
language_model: NousResearch/Meta-Llama-3-8B-Instruct
model_score: 7.659999999999999
model_size: 8B
reward_model: ChaiML/reward_gpt2_medium_preference_24m_e2
single_thumbs_up_ratio: 0.47096774193548385
thumbs_down_ratio: 0.26129032258064516
thumbs_up_ratio: 0.7387096774193549
us_pacific_date: 2024-04-29
win_ratio: 0.514090774250964
Resubmit model
Running pipeline stage MKMLizer
Starting job with name nousresearch-meta-llama-4941-v51-mkmlizer
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Starting job with name nousresearch-meta-llama-4941-v51-mkmlizer
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nousresearch-meta-llama-4941-v51-mkmlizer: ║ ║
nousresearch-meta-llama-4941-v51-mkmlizer: ║ Version: 0.8.10 ║
nousresearch-meta-llama-4941-v51-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
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nousresearch-meta-llama-4941-v51-mkmlizer: ║ Chai Research Corp. ║
nousresearch-meta-llama-4941-v51-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
nousresearch-meta-llama-4941-v51-mkmlizer: ║ Expiration: 2024-07-15 23:59:59 ║
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nousresearch-meta-llama-4941-v51-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
nousresearch-meta-llama-4941-v51-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.
nousresearch-meta-llama-4941-v51-mkmlizer: warnings.warn(warning_message, FutureWarning)
nousresearch-meta-llama-4941-v51-mkmlizer: Downloaded to shared memory in 17.804s
nousresearch-meta-llama-4941-v51-mkmlizer: quantizing model to /dev/shm/model_cache
nousresearch-meta-llama-4941-v51-mkmlizer: Saving flywheel model at /dev/shm/model_cache
nousresearch-meta-llama-4941-v51-mkmlizer: Loading 0: 0%| | 0/291 [00:00<?, ?it/s] Loading 0: 42%|████▏ | 121/291 [00:01<00:01, 117.55it/s] Loading 0: 81%|████████▏ | 237/291 [00:02<00:00, 115.93it/s] Loading 0: 99%|█████████▊| 287/291 [00:07<00:00, 29.64it/s] Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
nousresearch-meta-llama-4941-v51-mkmlizer: quantized model in 17.918s
nousresearch-meta-llama-4941-v51-mkmlizer: Processed model NousResearch/Meta-Llama-3-8B-Instruct in 36.716s
nousresearch-meta-llama-4941-v51-mkmlizer: creating bucket guanaco-mkml-models
nousresearch-meta-llama-4941-v51-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
nousresearch-meta-llama-4941-v51-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v51
nousresearch-meta-llama-4941-v51-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v51/config.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v51/special_tokens_map.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v51/tokenizer_config.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v51/tokenizer.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v51/flywheel_model.0.safetensors
nousresearch-meta-llama-4941-v51-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
nousresearch-meta-llama-4941-v51-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.
nousresearch-meta-llama-4941-v51-mkmlizer: warnings.warn(
nousresearch-meta-llama-4941-v51-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.
nousresearch-meta-llama-4941-v51-mkmlizer: warnings.warn(
nousresearch-meta-llama-4941-v51-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.
nousresearch-meta-llama-4941-v51-mkmlizer: warnings.warn(
nousresearch-meta-llama-4941-v51-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()
nousresearch-meta-llama-4941-v51-mkmlizer: return self.fget.__get__(instance, owner)()
nousresearch-meta-llama-4941-v51-mkmlizer: creating bucket guanaco-reward-models
nousresearch-meta-llama-4941-v51-mkmlizer: Bucket 's3://guanaco-reward-models/' created
nousresearch-meta-llama-4941-v51-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/tokenizer_config.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/config.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/special_tokens_map.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/vocab.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/merges.txt
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/tokenizer.json
nousresearch-meta-llama-4941-v51-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/nousresearch-meta-llama-4941-v51_reward/reward.tensors
Job nousresearch-meta-llama-4941-v51-mkmlizer completed after 63.63s with status: succeeded
Stopping job with name nousresearch-meta-llama-4941-v51-mkmlizer
Pipeline stage MKMLizer completed in 67.14s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.09s
Running pipeline stage ISVCDeployer
Creating inference service nousresearch-meta-llama-4941-v51
Waiting for inference service nousresearch-meta-llama-4941-v51 to be ready
Inference service nousresearch-meta-llama-4941-v51 ready after 30.194157123565674s
Pipeline stage ISVCDeployer completed in 37.85s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.1124494075775146s
Received healthy response to inference request in 1.27797269821167s
Received healthy response to inference request in 1.35054349899292s
Received healthy response to inference request in 1.2375454902648926s
Received healthy response to inference request in 1.2918245792388916s
5 requests
0 failed requests
5th percentile: 1.245630931854248
10th percentile: 1.2537163734436034
20th percentile: 1.2698872566223145
30th percentile: 1.2807430744171142
40th percentile: 1.286283826828003
50th percentile: 1.2918245792388916
60th percentile: 1.315312147140503
70th percentile: 1.3387997150421143
80th percentile: 1.502924680709839
90th percentile: 1.8076870441436768
95th percentile: 1.9600682258605955
99th percentile: 2.081973171234131
mean time: 1.4540671348571776
Pipeline stage StressChecker completed in 7.91s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.03s
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
nousresearch-meta-llama_4941_v51 status is now deployed due to DeploymentManager action
nousresearch-meta-llama_4941_v51 status is now inactive due to auto deactivation removed underperforming models

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