albertwang8192-2025-07-11-2

developer_uid: RandomForest1024

submission_id: albertwang8192-2025-07-11-2_v1

model_name: 2025-07-11_2

model_group: AlbertWang8192/2025-07-1

status: torndown

timestamp: 2025-07-11T16:48:06+00:00

num_battles: 9442

num_wins: 4668

celo_rating: 1276.79

family_friendly_score: 0.5296000000000001

family_friendly_standard_error: 0.007058666162951752

submission_type: basic

model_repo: AlbertWang8192/2025-07-11_2

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': 0.5918697265931223, 'latency_mean': 1.6893833267688751, 'latency_p50': 1.695825219154358, 'latency_p90': 1.846745753288269}, {'batch_size': 3, 'throughput': 1.0571782435097654, 'latency_mean': 2.8330232536792757, 'latency_p50': 2.835049271583557, 'latency_p90': 3.097144842147827}, {'batch_size': 5, 'throughput': 1.2694123771229633, 'latency_mean': 3.9207137179374696, 'latency_p50': 3.917651414871216, 'latency_p90': 4.3542879819869995}, {'batch_size': 6, 'throughput': 1.3342584921409744, 'latency_mean': 4.461950969696045, 'latency_p50': 4.455165386199951, 'latency_p90': 5.071273589134217}, {'batch_size': 8, 'throughput': 1.3946196004186402, 'latency_mean': 5.697848969697953, 'latency_p50': 5.693702816963196, 'latency_p90': 6.380490255355835}, {'batch_size': 10, 'throughput': 1.4186876881144916, 'latency_mean': 6.986410459280014, 'latency_p50': 6.919869661331177, 'latency_p90': 7.8739742040634155}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: 2025-07-11_2

is_internal_developer: False

language_model: AlbertWang8192/2025-07-11_2

model_size: 13B

ranking_group: single

throughput_3p7s: 1.24

us_pacific_date: 2025-07-11

win_ratio: 0.49438678246134293

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_end|>', '<|im_start|>'], '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 albertwang8192-2025-07-11-2-v1-mkmlizer
Waiting for job on albertwang8192-2025-07-11-2-v1-mkmlizer to finish
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albertwang8192-2025-07-11-2-v1-mkmlizer: ║                                                                     ║
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albertwang8192-2025-07-11-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  Version: 0.29.15                                                   ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  https://mk1.ai                                                     ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  The license key for the current software has been verified as      ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  belonging to:                                                      ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  Chai Research Corp.                                                ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ║                                                                     ║
albertwang8192-2025-07-11-2-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
albertwang8192-2025-07-11-2-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`
albertwang8192-2025-07-11-2-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`
albertwang8192-2025-07-11-2-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`
albertwang8192-2025-07-11-2-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`
albertwang8192-2025-07-11-2-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`
albertwang8192-2025-07-11-2-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`
albertwang8192-2025-07-11-2-v1-mkmlizer: Downloaded to shared memory in 54.267s
albertwang8192-2025-07-11-2-v1-mkmlizer: Checking if AlbertWang8192/2025-07-11_2 already exists in ChaiML
albertwang8192-2025-07-11-2-v1-mkmlizer: Creating repo ChaiML/2025-07-11_2 and uploading /tmp/tmpeb43ntph to it
albertwang8192-2025-07-11-2-v1-mkmlizer: 
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albertwang8192-2025-07-11-2-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpeb43ntph, device:0
albertwang8192-2025-07-11-2-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
albertwang8192-2025-07-11-2-v1-mkmlizer: quantized model in 30.854s
albertwang8192-2025-07-11-2-v1-mkmlizer: Processed model AlbertWang8192/2025-07-11_2 in 141.421s
albertwang8192-2025-07-11-2-v1-mkmlizer: creating bucket guanaco-mkml-models
albertwang8192-2025-07-11-2-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
albertwang8192-2025-07-11-2-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v1/nvidia
albertwang8192-2025-07-11-2-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v1/nvidia/config.json
albertwang8192-2025-07-11-2-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v1/nvidia/special_tokens_map.json
albertwang8192-2025-07-11-2-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v1/nvidia/tokenizer_config.json
albertwang8192-2025-07-11-2-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v1/nvidia/tokenizer.json
albertwang8192-2025-07-11-2-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/albertwang8192-2025-07-11-2-v1/nvidia/flywheel_model.0.safetensors
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Job albertwang8192-2025-07-11-2-v1-mkmlizer completed after 168.33s with status: succeeded
Stopping job with name albertwang8192-2025-07-11-2-v1-mkmlizer
Pipeline stage MKMLizer completed in 168.82s
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Running pipeline stage MKMLTemplater
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Creating inference service albertwang8192-2025-07-11-2-v1
Waiting for inference service albertwang8192-2025-07-11-2-v1 to be ready
Inference service albertwang8192-2025-07-11-2-v1 ready after 200.69043803215027s
Pipeline stage MKMLDeployer completed in 201.24s
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Running pipeline stage StressChecker
Received healthy response to inference request in 2.3079733848571777s
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