junhua024-chai-16-full-_35624

developer_uid: junhua024
submission_id: junhua024-chai-16-full-_35624_v3
model_name: junhua024-chai-16-full-_35624_v3
model_group: junhua024/chai_16_full_1
status: torndown
timestamp: 2025-07-19T11:21:26+00:00
num_battles: 9679
num_wins: 4700
celo_rating: 1278.12
family_friendly_score: 0.0
family_friendly_standard_error: 0.0
submission_type: basic
model_repo: junhua024/chai_16_full_102_o_1925
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': 0.5963613369700919, 'latency_mean': 1.676717895269394, 'latency_p50': 1.6692402362823486, 'latency_p90': 1.850492000579834}, {'batch_size': 3, 'throughput': 1.0671146642617129, 'latency_mean': 2.8096799051761625, 'latency_p50': 2.805772066116333, 'latency_p90': 3.044974207878113}, {'batch_size': 5, 'throughput': 1.2837318034163065, 'latency_mean': 3.8716150176525117, 'latency_p50': 3.8732041120529175, 'latency_p90': 4.320278310775757}, {'batch_size': 6, 'throughput': 1.3467723833333367, 'latency_mean': 4.434241561889649, 'latency_p50': 4.44689416885376, 'latency_p90': 4.976966977119446}, {'batch_size': 8, 'throughput': 1.403134744816582, 'latency_mean': 5.664415674209595, 'latency_p50': 5.672565817832947, 'latency_p90': 6.363316774368286}, {'batch_size': 10, 'throughput': 1.439855765882635, 'latency_mean': 6.883429969549179, 'latency_p50': 6.9057170152664185, 'latency_p90': 7.752112340927124}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: junhua024-chai-16-full-_35624_v3
is_internal_developer: False
language_model: junhua024/chai_16_full_102_o_1925
model_size: 13B
ranking_group: single
throughput_3p7s: 1.26
us_pacific_date: 2025-07-19
win_ratio: 0.48558735406550263
generation_params: {'temperature': 1.0, 'top_p': 0.88, 'min_p': 0.0, 'top_k': 10, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n'], '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': False}
Resubmit model
Shutdown handler not registered because Python interpreter is not running in the main thread
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLizer
Starting job with name junhua024-chai-16-full-35624-v3-mkmlizer
Waiting for job on junhua024-chai-16-full-35624-v3-mkmlizer to finish
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junhua024-chai-16-full-35624-v3-mkmlizer: ║                                                                     ║
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junhua024-chai-16-full-35624-v3-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  Version: 0.29.15                                                   ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  https://mk1.ai                                                     ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  The license key for the current software has been verified as      ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  belonging to:                                                      ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  Chai Research Corp.                                                ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
junhua024-chai-16-full-35624-v3-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-35624-v3-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-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`
junhua024-chai-16-full-35624-v3-mkmlizer: Downloaded to shared memory in 116.147s
junhua024-chai-16-full-35624-v3-mkmlizer: Checking if junhua024/chai_16_full_102_o_1925 already exists in ChaiML
junhua024-chai-16-full-35624-v3-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpt7pr43gx, device:0
junhua024-chai-16-full-35624-v3-mkmlizer: Saving flywheel model at /dev/shm/model_cache
junhua024-chai-16-full-35624-v3-mkmlizer: quantized model in 30.891s
junhua024-chai-16-full-35624-v3-mkmlizer: Processed model junhua024/chai_16_full_102_o_1925 in 147.128s
junhua024-chai-16-full-35624-v3-mkmlizer: creating bucket guanaco-mkml-models
junhua024-chai-16-full-35624-v3-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
junhua024-chai-16-full-35624-v3-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/junhua024-chai-16-full-35624-v3/nvidia
junhua024-chai-16-full-35624-v3-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/junhua024-chai-16-full-35624-v3/nvidia/special_tokens_map.json
junhua024-chai-16-full-35624-v3-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/junhua024-chai-16-full-35624-v3/nvidia/config.json
junhua024-chai-16-full-35624-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/junhua024-chai-16-full-35624-v3/nvidia/tokenizer_config.json
junhua024-chai-16-full-35624-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/junhua024-chai-16-full-35624-v3/nvidia/tokenizer.json
Failed to get response for submission chaiml-nis-qwen32b-sim_98336_v34: HTTPConnectionPool(host='chaiml-nis-qwen32b-sim-98336-v34-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
junhua024-chai-16-full-35624-v3-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/junhua024-chai-16-full-35624-v3/nvidia/flywheel_model.0.safetensors
junhua024-chai-16-full-35624-v3-mkmlizer: 
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Job junhua024-chai-16-full-35624-v3-mkmlizer completed after 174.82s with status: succeeded
Stopping job with name junhua024-chai-16-full-35624-v3-mkmlizer
Pipeline stage MKMLizer completed in 175.71s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.19s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service junhua024-chai-16-full-35624-v3
Waiting for inference service junhua024-chai-16-full-35624-v3 to be ready
Inference service junhua024-chai-16-full-35624-v3 ready after 322.56573152542114s
Pipeline stage MKMLDeployer completed in 323.27s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.6104726791381836s
Received healthy response to inference request in 1.6668002605438232s
Received healthy response to inference request in 1.6038274765014648s
Received healthy response to inference request in 1.8713912963867188s
Received healthy response to inference request in 1.798064947128296s
5 requests
0 failed requests
5th percentile: 1.6164220333099366
10th percentile: 1.6290165901184082
20th percentile: 1.6542057037353515
30th percentile: 1.6930531978607177
40th percentile: 1.745559072494507
50th percentile: 1.798064947128296
60th percentile: 1.827395486831665
70th percentile: 1.8567260265350343
80th percentile: 2.019207572937012
90th percentile: 2.3148401260375975
95th percentile: 2.4626564025878905
99th percentile: 2.580909423828125
mean time: 1.9101113319396972
Pipeline stage StressChecker completed in 11.12s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyTriggerPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage OfflineFamilyFriendlyTriggerPipeline completed in 0.76s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage TriggerMKMLProfilingPipeline completed in 0.80s
Shutdown handler de-registered
junhua024-chai-16-full-_35624_v3 status is now deployed due to DeploymentManager action
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Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.18s
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Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.13s
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Running pipeline stage MKMLProfilerDeployer
Creating inference service junhua024-chai-16-full-35624-v3-profiler
Waiting for inference service junhua024-chai-16-full-35624-v3-profiler to be ready
Inference service junhua024-chai-16-full-35624-v3-profiler ready after 323.81338715553284s
Pipeline stage MKMLProfilerDeployer completed in 324.73s
run pipeline stage %s
Running pipeline stage MKMLProfilerRunner
kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/junhua024-chai-16-fuc0daad2ad02177f5bce9a15d3a0aedde-deplovzgz8:/code/chaiverse_profiler_1752924995 --namespace tenant-chaiml-guanaco
kubectl exec -it junhua024-chai-16-fuc0daad2ad02177f5bce9a15d3a0aedde-deplovzgz8 --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1752924995 && python profiles.py profile --best_of_n 8 --auto_batch 5 --batches 1,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,105,110,115,120,125,130,135,140,145,150,155,160,165,170,175,180,185,190,195 --samples 200 --input_tokens 1024 --output_tokens 64 --summary /code/chaiverse_profiler_1752924995/summary.json'
kubectl exec -it junhua024-chai-16-fuc0daad2ad02177f5bce9a15d3a0aedde-deplovzgz8 --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1752924995/summary.json'
Shutdown handler registered
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Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
clean up pipeline due to error=DeploymentChecksError('None: None')
Shutdown handler de-registered
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
clean up pipeline due to error=DeploymentChecksError('None: None')
Shutdown handler de-registered
junhua024-chai-16-full-_35624_v3 status is now inactive due to auto deactivation removed underperforming models
junhua024-chai-16-full-_35624_v3 status is now torndown due to DeploymentManager action