submission_id: rica40325-mistral-7356_v2
developer_uid: rica40325
best_of: 8
celo_rating: 1260.58
display_name: rica40325-mistral-7356_v1
family_friendly_score: 0.5542251111871366
family_friendly_standard_error: 0.009163658998844523
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}
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 40, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['</s>', 'Bot:', 'User:', 'You:', 'Me:', '####'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}
gpu_counts: {'NVIDIA RTX A5000': 1}
ineligible_reason: num_battles<5000
is_internal_developer: False
language_model: rica40325/mistral-7356
latencies: [{'batch_size': 1, 'throughput': 0.6206564320035739, 'latency_mean': 1.6111376106739044, 'latency_p50': 1.6188888549804688, 'latency_p90': 1.767330026626587}, {'batch_size': 3, 'throughput': 1.0943562672869553, 'latency_mean': 2.73918696641922, 'latency_p50': 2.7634856700897217, 'latency_p90': 3.0023796796798705}, {'batch_size': 5, 'throughput': 1.2433726504487328, 'latency_mean': 4.003507262468338, 'latency_p50': 4.026834726333618, 'latency_p90': 4.522751712799073}, {'batch_size': 6, 'throughput': 1.273986983995119, 'latency_mean': 4.685184941291809, 'latency_p50': 4.677178144454956, 'latency_p90': 5.256221747398376}, {'batch_size': 8, 'throughput': 1.2598554503856088, 'latency_mean': 6.309645557403565, 'latency_p50': 6.3839733600616455, 'latency_p90': 7.164608383178711}, {'batch_size': 10, 'throughput': 1.218242367230381, 'latency_mean': 8.164681758880615, 'latency_p50': 8.169075727462769, 'latency_p90': 9.343198704719544}]
max_input_tokens: 1024
max_output_tokens: 64
model_architecture: MistralForCausalLM
model_group: rica40325/mistral-7356
model_name: rica40325-mistral-7356_v1
model_num_parameters: 12772070400.0
model_repo: rica40325/mistral-7356
model_size: 13B
num_battles: 3003
num_wins: 1538
ranking_group: single
status: torndown
submission_type: basic
throughput_3p7s: 1.22
timestamp: 2024-09-25T06:00:45+00:00
us_pacific_date: 2024-09-24
win_ratio: 0.5121545121545121
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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 rica40325-mistral-7356-v2-mkmlizer
Waiting for job on rica40325-mistral-7356-v2-mkmlizer to finish
rica40325-mistral-7356-v2-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
rica40325-mistral-7356-v2-mkmlizer: ║ _____ __ __ ║
rica40325-mistral-7356-v2-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
rica40325-mistral-7356-v2-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
rica40325-mistral-7356-v2-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
rica40325-mistral-7356-v2-mkmlizer: ║ /___/ ║
rica40325-mistral-7356-v2-mkmlizer: ║ ║
rica40325-mistral-7356-v2-mkmlizer: ║ Version: 0.11.12 ║
rica40325-mistral-7356-v2-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
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rica40325-mistral-7356-v2-mkmlizer: ║ https://mk1.ai ║
rica40325-mistral-7356-v2-mkmlizer: ║ ║
rica40325-mistral-7356-v2-mkmlizer: ║ The license key for the current software has been verified as ║
rica40325-mistral-7356-v2-mkmlizer: ║ belonging to: ║
rica40325-mistral-7356-v2-mkmlizer: ║ ║
rica40325-mistral-7356-v2-mkmlizer: ║ Chai Research Corp. ║
rica40325-mistral-7356-v2-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
rica40325-mistral-7356-v2-mkmlizer: ║ Expiration: 2024-10-15 23:59:59 ║
rica40325-mistral-7356-v2-mkmlizer: ║ ║
rica40325-mistral-7356-v2-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
rica40325-mistral-7356-v2-mkmlizer: Downloaded to shared memory in 30.750s
rica40325-mistral-7356-v2-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpr4p9h23u, device:0
rica40325-mistral-7356-v2-mkmlizer: Saving flywheel model at /dev/shm/model_cache
rica40325-mistral-7356-v2-mkmlizer: /opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/loader.py:55: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
rica40325-mistral-7356-v2-mkmlizer: tensors = torch.load(model_shard_filename, map_location=torch.device(self.device), mmap=True)
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rica40325-mistral-7356-v2-mkmlizer: quantized model in 35.981s
rica40325-mistral-7356-v2-mkmlizer: Processed model rica40325/mistral-7356 in 66.731s
rica40325-mistral-7356-v2-mkmlizer: creating bucket guanaco-mkml-models
rica40325-mistral-7356-v2-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
rica40325-mistral-7356-v2-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/rica40325-mistral-7356-v2
rica40325-mistral-7356-v2-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/rica40325-mistral-7356-v2/config.json
rica40325-mistral-7356-v2-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/rica40325-mistral-7356-v2/special_tokens_map.json
rica40325-mistral-7356-v2-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/rica40325-mistral-7356-v2/tokenizer_config.json
rica40325-mistral-7356-v2-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/rica40325-mistral-7356-v2/tokenizer.json
rica40325-mistral-7356-v2-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/rica40325-mistral-7356-v2/flywheel_model.0.safetensors
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Job rica40325-mistral-7356-v2-mkmlizer completed after 88.27s with status: succeeded
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Inference service rica40325-mistral-7356-v2 ready after 212.89391207695007s
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mean time: 2.9448732852935793
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kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/rica40325-mistral-7356-v2-profiler-predictor-00001-deploymlvpn2:/code/chaiverse_profiler_1727244627 --namespace tenant-chaiml-guanaco
kubectl exec -it rica40325-mistral-7356-v2-profiler-predictor-00001-deploymlvpn2 --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1727244627 && 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_1727244627/summary.json'
kubectl exec -it rica40325-mistral-7356-v2-profiler-predictor-00001-deploymlvpn2 --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1727244627/summary.json'
Pipeline stage MKMLProfilerRunner completed in 1152.37s
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Checking if service rica40325-mistral-7356-v2-profiler is running
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rica40325-mistral-7356_v2 status is now inactive due to auto deactivation removed underperforming models
rica40325-mistral-7356_v2 status is now torndown due to DeploymentManager action