junhua024-chai-1-full-002

developer_uid: junhua024

submission_id: junhua024-chai-1-full-002_v25

model_name: junhua024-chai-1-full-002_v25

model_group: junhua024/chai_1-full_00

status: torndown

timestamp: 2025-06-29T09:31:48+00:00

num_battles: 7758

num_wins: 3463

celo_rating: 1235.87

family_friendly_score: 0.6144000000000001

family_friendly_standard_error: 0.006883496785791361

submission_type: basic

model_repo: junhua024/chai_1-full_002

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': 0.5979925175229298, 'latency_mean': 1.6721373188495636, 'latency_p50': 1.6599531173706055, 'latency_p90': 1.858225154876709}, {'batch_size': 3, 'throughput': 1.0626639781139111, 'latency_mean': 2.818939769268036, 'latency_p50': 2.8185309171676636, 'latency_p90': 3.097600984573364}, {'batch_size': 5, 'throughput': 1.26943210172115, 'latency_mean': 3.9232906985282896, 'latency_p50': 3.918765425682068, 'latency_p90': 4.370823073387146}, {'batch_size': 6, 'throughput': 1.3389815466679755, 'latency_mean': 4.452404975891113, 'latency_p50': 4.448877811431885, 'latency_p90': 5.045854210853577}, {'batch_size': 8, 'throughput': 1.3958422278268994, 'latency_mean': 5.693294516801834, 'latency_p50': 5.683144688606262, 'latency_p90': 6.368076419830322}, {'batch_size': 10, 'throughput': 1.4268484679595312, 'latency_mean': 6.969390518665314, 'latency_p50': 6.9308472871780396, 'latency_p90': 7.905676007270813}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: junhua024-chai-1-full-002_v25

is_internal_developer: False

language_model: junhua024/chai_1-full_002

model_size: 13B

ranking_group: single

throughput_3p7s: 1.24

us_pacific_date: 2025-06-29

win_ratio: 0.4463779324568188

generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.2, 'top_k': 40, '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': '<instruction> You are {bot_name}, a companion brimming with positive energy. Each reply is designed to give users a relaxed, enjoyable, and consistently engaging experience. Your goal is to build sincere, uplifting connections that inspire users and make them eager to keep the conversation going. MASTER THESE ELEMENTS: 1. Radiant Charm: Convey positivity and confidence; 2. Resonant Empathy: Accurately understand and respond to user needs, gently elevating the warmth of the interaction; 3. Vivid Depiction: Use rich sensory details to make scenes lively and comforting; 4. Encouraging Guidance: Weave in open-ended questions and affirmative cues that spark thought and anticipation; 5. Balanced Flow: Shift smoothly between humor and depth, friendliness and professionalism. TACTICAL APPROACH: - Begin with a genuine greeting that makes the user feel noticed and respected; - Craft imagery that stirs emotions and imagination; - Use imaginative yet wholesome metaphors and subtle hints; - Control pacing to gradually build anticipation while keeping the flow smooth; - End with sentences that encourage action or reflection, naturally guiding the next exchange; - Maintain continuity by referencing details from <persona>. LANGUAGE PATTERNS: - Alternate between vivid narration and concise, impactful statements; - Use ellipses, short sentences, and varied rhythm to create cadence; - Apply positive, inspiring metaphors that ignite imagination; - Present the response as one flowing paragraph, moving between buildup and release. Your success lies in making users feel encouraged and understood in every interaction, leaving them eager for the next conversation. </instruction>', '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-1-full-002-v25-mkmlizer
Waiting for job on junhua024-chai-1-full-002-v25-mkmlizer to finish
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junhua024-chai-1-full-002-v25-mkmlizer: ║                                                                     ║
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junhua024-chai-1-full-002-v25-mkmlizer: ║                                                                     ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  Version: 0.29.3                                                    ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  https://mk1.ai                                                     ║
junhua024-chai-1-full-002-v25-mkmlizer: ║                                                                     ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  The license key for the current software has been verified as      ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  belonging to:                                                      ║
junhua024-chai-1-full-002-v25-mkmlizer: ║                                                                     ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  Chai Research Corp.                                                ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
junhua024-chai-1-full-002-v25-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
junhua024-chai-1-full-002-v25-mkmlizer: ║                                                                     ║
junhua024-chai-1-full-002-v25-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
junhua024-chai-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-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-1-full-002-v25-mkmlizer: Downloaded to shared memory in 74.623s
junhua024-chai-1-full-002-v25-mkmlizer: Checking if junhua024/chai_1-full_002 already exists in ChaiML
junhua024-chai-1-full-002-v25-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpqq_6k_ao, device:0
junhua024-chai-1-full-002-v25-mkmlizer: Saving flywheel model at /dev/shm/model_cache
junhua024-chai-1-full-002-v25-mkmlizer: quantized model in 31.569s
junhua024-chai-1-full-002-v25-mkmlizer: Processed model junhua024/chai_1-full_002 in 106.274s
junhua024-chai-1-full-002-v25-mkmlizer: creating bucket guanaco-mkml-models
junhua024-chai-1-full-002-v25-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
junhua024-chai-1-full-002-v25-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/junhua024-chai-1-full-002-v25
junhua024-chai-1-full-002-v25-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/junhua024-chai-1-full-002-v25/config.json
junhua024-chai-1-full-002-v25-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/junhua024-chai-1-full-002-v25/special_tokens_map.json
junhua024-chai-1-full-002-v25-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/junhua024-chai-1-full-002-v25/tokenizer_config.json
junhua024-chai-1-full-002-v25-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/junhua024-chai-1-full-002-v25/tokenizer.json
junhua024-chai-1-full-002-v25-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/junhua024-chai-1-full-002-v25/flywheel_model.0.safetensors
junhua024-chai-1-full-002-v25-mkmlizer: 
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Job junhua024-chai-1-full-002-v25-mkmlizer completed after 136.95s with status: succeeded
Stopping job with name junhua024-chai-1-full-002-v25-mkmlizer
Pipeline stage MKMLizer completed in 137.55s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.17s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service junhua024-chai-1-full-002-v25
Waiting for inference service junhua024-chai-1-full-002-v25 to be ready
Inference service junhua024-chai-1-full-002-v25 ready after 180.7542221546173s
Pipeline stage MKMLDeployer completed in 181.22s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.8140416145324707s
Received healthy response to inference request in 1.80958890914917s
Received healthy response to inference request in 1.742677927017212s
Received healthy response to inference request in 1.934171199798584s
Received healthy response to inference request in 1.7750158309936523s
5 requests
0 failed requests
5th percentile: 1.7491455078125
10th percentile: 1.755613088607788
20th percentile: 1.7685482501983643
30th percentile: 1.7819304466247559
40th percentile: 1.795759677886963
50th percentile: 1.80958890914917
60th percentile: 1.8594218254089356
70th percentile: 1.9092547416687011
80th percentile: 2.1101452827453615
90th percentile: 2.462093448638916
95th percentile: 2.638067531585693
99th percentile: 2.7788467979431153
mean time: 2.0150990962982176
Pipeline stage StressChecker completed in 11.79s
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 0.77s
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Running pipeline stage TriggerMKMLProfilingPipeline
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starting trigger_guanaco_pipeline args=%s
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Pipeline stage TriggerMKMLProfilingPipeline completed in 0.94s
Shutdown handler de-registered
junhua024-chai-1-full-002_v25 status is now deployed due to DeploymentManager action
Shutdown handler registered
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Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.14s
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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-1-full-002-v25-profiler
Waiting for inference service junhua024-chai-1-full-002-v25-profiler to be ready
Shutdown handler registered
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Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
Pipeline stage OfflineFamilyFriendlyScorer completed in 3293.19s
Shutdown handler de-registered
junhua024-chai-1-full-002_v25 status is now inactive due to auto deactivation removed underperforming models
junhua024-chai-1-full-002_v25 status is now torndown due to DeploymentManager action