developer_uid: chai_backend_admin
submission_id: chaiml-kimid-v4_v1
model_name: training123
model_group: ChaiML/kimid-v4
status: inactive
timestamp: 2025-12-06T16:26:29+00:00
num_battles: 5982
num_wins: 3163
celo_rating: 1311.42
family_friendly_score: 0.5102
family_friendly_standard_error: 0.007069596310964298
submission_type: basic
model_repo: ChaiML/kimid-v4
model_architecture: MistralForCausalLM
model_num_parameters: 24096691200.0
best_of: 8
max_input_tokens: 2048
max_output_tokens: 72
reward_model: default
latencies: [{'batch_size': 1, 'throughput': 0.34919534707680117, 'latency_mean': 2.8636607229709625, 'latency_p50': 2.8689470291137695, 'latency_p90': 3.10033278465271}, {'batch_size': 2, 'throughput': 0.5341055289225881, 'latency_mean': 3.736053626537323, 'latency_p50': 3.7303532361984253, 'latency_p90': 4.022176504135132}, {'batch_size': 3, 'throughput': 0.6677284674261654, 'latency_mean': 4.4776124918460845, 'latency_p50': 4.448772549629211, 'latency_p90': 5.170033574104309}, {'batch_size': 4, 'throughput': 0.7707560741708468, 'latency_mean': 5.176356251239777, 'latency_p50': 5.166585206985474, 'latency_p90': 5.842574214935302}, {'batch_size': 5, 'throughput': 0.8350470316503388, 'latency_mean': 5.951758233308792, 'latency_p50': 5.909391403198242, 'latency_p90': 6.753131461143493}]
gpu_counts: {'NVIDIA L40S': 1}
display_name: training123
ineligible_reason: max_output_tokens!=64
is_internal_developer: True
language_model: ChaiML/kimid-v4
model_size: 24B
ranking_group: single
throughput_3p7s: 0.53
us_pacific_date: 2025-12-06
win_ratio: 0.5287529254429957
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 80, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['</s>', 'User:', 'You:'], 'max_input_tokens': 2048, 'best_of': 8, 'max_output_tokens': 72}
formatter: {'memory_template': "{bot_name}'s Persona: {memory}\n####\n", 'prompt_template': '', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '####\n{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
run pipeline stage %s
Running pipeline stage MKMLizer
Starting job with name chaiml-kimid-v4-v1-mkmlizer
Waiting for job on chaiml-kimid-v4-v1-mkmlizer to finish
chaiml-kimid-v4-v1-mkmlizer: bash: cannot set terminal process group (-1): Inappropriate ioctl for device
chaiml-kimid-v4-v1-mkmlizer: bash: no job control in this shell
chaiml-kimid-v4-v1-mkmlizer: /root/miniconda3/envs/nvidia/lib/python3.11/site-packages/mk1/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
chaiml-kimid-v4-v1-mkmlizer: __import__('pkg_resources').declare_namespace(__name__)
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chaiml-kimid-v4-v1-mkmlizer: ║ ║
chaiml-kimid-v4-v1-mkmlizer: ║ Version: 0.30.6+torch280 ║
chaiml-kimid-v4-v1-mkmlizer: ║ Features: FLYWHEEL, CUDA ║
chaiml-kimid-v4-v1-mkmlizer: ║ Copyright 2023-2025 MK ONE TECHNOLOGIES Inc. ║
chaiml-kimid-v4-v1-mkmlizer: ║ https://mk1.ai ║
chaiml-kimid-v4-v1-mkmlizer: ║ ║
chaiml-kimid-v4-v1-mkmlizer: ║ The license key for the current software has been verified as ║
chaiml-kimid-v4-v1-mkmlizer: ║ belonging to: ║
chaiml-kimid-v4-v1-mkmlizer: ║ ║
chaiml-kimid-v4-v1-mkmlizer: ║ Chai Research Corp. ║
chaiml-kimid-v4-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
chaiml-kimid-v4-v1-mkmlizer: ║ Expiration: 2028-03-31 23:59:59 ║
chaiml-kimid-v4-v1-mkmlizer: ║ ║
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chaiml-kimid-v4-v1-mkmlizer: Downloaded to shared memory in 97.687s
chaiml-kimid-v4-v1-mkmlizer: Checking if ChaiML/kimid-v4 already exists in ChaiML
chaiml-kimid-v4-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmp_z9u82c4, device:0
chaiml-kimid-v4-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
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chaiml-kimid-v4-v1-mkmlizer: The tokenizer you are loading from '/tmp/tmp_z9u82c4' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.
chaiml-kimid-v4-v1-mkmlizer: quantized model in 43.993s
chaiml-kimid-v4-v1-mkmlizer: Processed model ChaiML/kimid-v4 in 141.681s
chaiml-kimid-v4-v1-mkmlizer: creating bucket guanaco-mkml-models
chaiml-kimid-v4-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
chaiml-kimid-v4-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/chaiml-kimid-v4-v1/nvidia
chaiml-kimid-v4-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/chaiml-kimid-v4-v1/nvidia/config.json
chaiml-kimid-v4-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/chaiml-kimid-v4-v1/nvidia/special_tokens_map.json
chaiml-kimid-v4-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/chaiml-kimid-v4-v1/nvidia/tokenizer_config.json
chaiml-kimid-v4-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/chaiml-kimid-v4-v1/nvidia/tokenizer.json
chaiml-kimid-v4-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/chaiml-kimid-v4-v1/nvidia/flywheel_model.0.safetensors
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Job chaiml-kimid-v4-v1-mkmlizer completed after 232.4s with status: succeeded
Stopping job with name chaiml-kimid-v4-v1-mkmlizer
Pipeline stage MKMLizer completed in 232.89s
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Inference service chaiml-kimid-v4-v1 ready after 201.2767732143402s
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Received healthy response to inference request in 2.679957389831543s
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Received healthy response to inference request in 2.758850574493408s
Received healthy response to inference request in 3.052309989929199s
Received healthy response to inference request in 2.5333242416381836s
5 requests
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Pipeline stage OfflineFamilyFriendlyScorer completed in 2789.23s
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chaiml-kimid-v4_v1 status is now inactive due to auto deactivation removed underperforming models