albertwang8192-2025-07-11-9

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-9_v1

model_name: 2025-07-11_9

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-12T02:14:16+00:00

num_battles: 8427

num_wins: 3905

celo_rating: 1269.89

family_friendly_score: 0.5246

family_friendly_standard_error: 0.007062504371680062

submission_type: basic

model_repo: AlbertWang8192/2025-07-11_9

model_architecture: MistralForCausalLM

model_num_parameters: 12772070400.0

best_of: 8

max_input_tokens: 1024

max_output_tokens: 64

reward_model: default

latencies: [{'batch_size': 1, 'throughput': 0.589619401376762, 'latency_mean': 1.6958218836784362, 'latency_p50': 1.6946861743927002, 'latency_p90': 1.865909957885742}, {'batch_size': 3, 'throughput': 1.0458090767803354, 'latency_mean': 2.861226918697357, 'latency_p50': 2.8536189794540405, 'latency_p90': 3.1669585943222045}, {'batch_size': 5, 'throughput': 1.2449895437214589, 'latency_mean': 3.995278843641281, 'latency_p50': 3.9632521867752075, 'latency_p90': 4.484055924415588}, {'batch_size': 6, 'throughput': 1.3146741989969626, 'latency_mean': 4.541727790832519, 'latency_p50': 4.573469519615173, 'latency_p90': 5.024684476852417}, {'batch_size': 8, 'throughput': 1.3701701029296065, 'latency_mean': 5.7973538541793825, 'latency_p50': 5.758425712585449, 'latency_p90': 6.594542026519775}, {'batch_size': 10, 'throughput': 1.3968538939161799, 'latency_mean': 7.103650979995727, 'latency_p50': 7.1286197900772095, 'latency_p90': 7.962387156486511}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_9

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_9

model_size: 13B

ranking_group: single

throughput_3p7s: 1.21

us_pacific_date: 2025-07-11

win_ratio: 0.46339147976741424

generation_params: {'temperature': 0.6, 'top_p': 0.95, 'min_p': 0.025, 'top_k': 60, 'presence_penalty': 0.4, 'frequency_penalty': 0.4, 'stopping_words': ['\n', '<|im_start|>', '<|im_end|>'], 'max_input_tokens': 1024, 'best_of': 8, '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': True}

Resubmit model

Shutdown handler not registered because Python interpreter is not running in the main thread
run pipeline %s
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Running pipeline stage MKMLizer
Starting job with name albertwang8192-2025-07-11-9-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-11-9-v1-mkmlizer to finish
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albertwang8192-2025-07-11-9-v1-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-9-v1-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-9-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-9-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-11-9-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-11-9-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-11-9-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-11-9-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-11-9-v1-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
albertwang8192-2025-07-11-9-v1-mkmlizer: Downloaded to shared memory in 44.676s
albertwang8192-2025-07-11-9-v1-mkmlizer: Checking if AlbertWang8192/2025-07-11_9 already exists in ChaiML
albertwang8192-2025-07-11-9-v1-mkmlizer: Creating repo ChaiML/2025-07-11_9 and uploading /tmp/tmpvmztuube to it
albertwang8192-2025-07-11-9-v1-mkmlizer: 
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albertwang8192-2025-07-11-9-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpvmztuube, device:0
albertwang8192-2025-07-11-9-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-11-9-v1-mkmlizer: quantized model in 30.415s
albertwang8192-2025-07-11-9-v1-mkmlizer: Processed model AlbertWang8192/2025-07-11_9 in 137.216s
albertwang8192-2025-07-11-9-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-9-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-9-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-9-v1/nvidia
albertwang8192-2025-07-11-9-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-9-v1/nvidia/config.json
albertwang8192-2025-07-11-9-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-9-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-9-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-9-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-9-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-9-v1/nvidia/tokenizer.json
albertwang8192-2025-07-11-9-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-9-v1/nvidia/flywheel_model.0.safetensors
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Job albertwang8192-2025-07-11-9-v1-mkmlizer completed after 166.72s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-9-v1-mkmlizer
Pipeline stage MKMLizer completed in 167.22s
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Inference service albertwang8192-2025-07-11-9-v1 ready after 201.3189013004303s
Pipeline stage MKMLDeployer completed in 201.92s
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5 requests
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