submission_id: jellywibble-qlora-120k-p_9463_v1
developer_uid: Jellywibble
status: inactive
model_repo: Jellywibble/qlora_120k_pref_data_ep2
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: 2024-07-03T22:42:18+00:00
model_name: nitral-ai-hathor-l3-8b-v-01_v1
model_group: Jellywibble/qlora_120k_p
num_battles: 13353
num_wins: 6957
celo_rating: 1203.82
propriety_score: 0.7080397181294042
propriety_total_count: 6244.0
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/qlora_120k_pref_data_ep2
model_size: 8B
reward_model: ChaiML/reward_gpt2_medium_preference_24m_e2
us_pacific_date: 2024-07-03
win_ratio: 0.5210065153898
Resubmit model
Running pipeline stage MKMLizer
Starting job with name jellywibble-qlora-120k-p-9463-v1-mkmlizer
Waiting for job on jellywibble-qlora-120k-p-9463-v1-mkmlizer to finish
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ _____ __ __ ║
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jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ ║
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ Version: 0.8.14 ║
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ https://mk1.ai ║
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jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ The license key for the current software has been verified as ║
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ belonging to: ║
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jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ Chai Research Corp. ║
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
jellywibble-qlora-120k-p-9463-v1-mkmlizer: ║ Expiration: 2024-07-15 23:59:59 ║
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jellywibble-qlora-120k-p-9463-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Downloaded to shared memory in 53.613s
jellywibble-qlora-120k-p-9463-v1-mkmlizer: quantizing model to /dev/shm/model_cache
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Loading 0: 0%| | 0/291 [00:00<?, ?it/s] Loading 0: 4%|▍ | 12/291 [00:00<00:02, 110.31it/s] Loading 0: 8%|▊ | 24/291 [00:00<00:02, 105.06it/s] Loading 0: 13%|█▎ | 39/291 [00:00<00:02, 122.25it/s] Loading 0: 18%|█▊ | 52/291 [00:00<00:02, 114.65it/s] Loading 0: 23%|██▎ | 66/291 [00:00<00:01, 122.68it/s] Loading 0: 27%|██▋ | 79/291 [00:00<00:01, 119.44it/s] Loading 0: 32%|███▏ | 92/291 [00:01<00:03, 62.46it/s] Loading 0: 35%|███▌ | 103/291 [00:01<00:02, 69.66it/s] Loading 0: 40%|████ | 117/291 [00:01<00:02, 83.01it/s] Loading 0: 45%|████▍ | 130/291 [00:01<00:01, 91.20it/s] Loading 0: 49%|████▉ | 143/291 [00:01<00:01, 100.21it/s] Loading 0: 54%|█████▎ | 156/291 [00:01<00:01, 107.70it/s] Loading 0: 58%|█████▊ | 168/291 [00:01<00:01, 107.60it/s] Loading 0: 62%|██████▏ | 181/291 [00:01<00:00, 112.29it/s] Loading 0: 66%|██████▋ | 193/291 [00:02<00:01, 63.27it/s] Loading 0: 70%|██████▉ | 203/291 [00:02<00:01, 68.41it/s] Loading 0: 75%|███████▌ | 219/291 [00:02<00:00, 85.83it/s] Loading 0: 79%|███████▉ | 230/291 [00:02<00:00, 89.38it/s] Loading 0: 85%|████████▍ | 246/291 [00:02<00:00, 105.02it/s] Loading 0: 89%|████████▉ | 259/291 [00:02<00:00, 107.79it/s] Loading 0: 94%|█████████▍| 273/291 [00:02<00:00, 115.07it/s] Loading 0: 98%|█████████▊| 286/291 [00:02<00:00, 116.99it/s] Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
jellywibble-qlora-120k-p-9463-v1-mkmlizer: quantized model in 25.261s
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Processed model Jellywibble/qlora_120k_pref_data_ep2 in 78.874s
jellywibble-qlora-120k-p-9463-v1-mkmlizer: creating bucket guanaco-mkml-models
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
jellywibble-qlora-120k-p-9463-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/jellywibble-qlora-120k-p-9463-v1
Failed to get response for submission mistralai-mixtral-8x7b-_3473_v33: ('http://mistralai-mixtral-8x7b-3473-v33-predictor-default.tenant-chaiml-guanaco.knative.ord1.coreweave.cloud/v1/models/GPT-J-6B-lit-v2:predict', 'read tcp 127.0.0.1:55404->127.0.0.1:8080: read: connection reset by peer\n')
jellywibble-qlora-120k-p-9463-v1-mkmlizer: DEBUG retryable error: RequestError: send request failed
jellywibble-qlora-120k-p-9463-v1-mkmlizer: caused by: dial tcp 216.153.53.63:443: i/o timeout
jellywibble-qlora-120k-p-9463-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/jellywibble-qlora-120k-p-9463-v1/tokenizer_config.json
jellywibble-qlora-120k-p-9463-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/jellywibble-qlora-120k-p-9463-v1/config.json
jellywibble-qlora-120k-p-9463-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/jellywibble-qlora-120k-p-9463-v1/special_tokens_map.json
jellywibble-qlora-120k-p-9463-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/jellywibble-qlora-120k-p-9463-v1/tokenizer.json
jellywibble-qlora-120k-p-9463-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/jellywibble-qlora-120k-p-9463-v1/flywheel_model.0.safetensors
jellywibble-qlora-120k-p-9463-v1-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
jellywibble-qlora-120k-p-9463-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-qlora-120k-p-9463-v1-mkmlizer: warnings.warn(
jellywibble-qlora-120k-p-9463-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-qlora-120k-p-9463-v1-mkmlizer: warnings.warn(
jellywibble-qlora-120k-p-9463-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-qlora-120k-p-9463-v1-mkmlizer: warnings.warn(
jellywibble-qlora-120k-p-9463-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-qlora-120k-p-9463-v1-mkmlizer: warnings.warn(
jellywibble-qlora-120k-p-9463-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-qlora-120k-p-9463-v1-mkmlizer: return self.fget.__get__(instance, owner)()
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Saving duration: 0.444s
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Processed model ChaiML/reward_gpt2_medium_preference_24m_e2 in 17.529s
jellywibble-qlora-120k-p-9463-v1-mkmlizer: creating bucket guanaco-reward-models
jellywibble-qlora-120k-p-9463-v1-mkmlizer: Bucket 's3://guanaco-reward-models/' created
jellywibble-qlora-120k-p-9463-v1-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/jellywibble-qlora-120k-p-9463-v1_reward
jellywibble-qlora-120k-p-9463-v1-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/jellywibble-qlora-120k-p-9463-v1_reward/reward.tensors
Job jellywibble-qlora-120k-p-9463-v1-mkmlizer completed after 157.63s with status: succeeded
Stopping job with name jellywibble-qlora-120k-p-9463-v1-mkmlizer
Pipeline stage MKMLizer completed in 158.49s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.11s
Running pipeline stage ISVCDeployer
Creating inference service jellywibble-qlora-120k-p-9463-v1
Waiting for inference service jellywibble-qlora-120k-p-9463-v1 to be ready
Inference service jellywibble-qlora-120k-p-9463-v1 ready after 50.22876214981079s
Pipeline stage ISVCDeployer completed in 57.17s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.0936808586120605s
Received healthy response to inference request in 1.3927161693572998s
Received healthy response to inference request in 1.3597402572631836s
Received healthy response to inference request in 1.3357532024383545s
Received healthy response to inference request in 1.3796956539154053s
5 requests
0 failed requests
5th percentile: 1.3405506134033203
10th percentile: 1.345348024368286
20th percentile: 1.3549428462982178
30th percentile: 1.3637313365936279
40th percentile: 1.3717134952545167
50th percentile: 1.3796956539154053
60th percentile: 1.384903860092163
70th percentile: 1.3901120662689208
80th percentile: 1.5329091072082521
90th percentile: 1.8132949829101563
95th percentile: 1.9534879207611082
99th percentile: 2.0656422710418703
mean time: 1.5123172283172608
Pipeline stage StressChecker completed in 8.35s
jellywibble-qlora-120k-p_9463_v1 status is now deployed due to DeploymentManager action
jellywibble-qlora-120k-p_9463_v1 status is now inactive due to auto deactivation removed underperforming models

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