Implementation plan for building a standardized assessment platform
For model comparison requirements needed for academic research, automated test environments can be built with DeepInfra:
- Test Data Set Preparation::
1. Use of platform-supportedapplication/jsonlinesFormat Batch Import Problem Set
2. Design of test cases incorporating complexity hierarchies (common sense/reasoning/specialized areas) - Parallel Test Architecture::
1. Create separate test threads for each model
2. Adoptionmodel=meta-llama/Meta-Llama-3-70B-InstructSpecify the model with parameters such as
3. Recording of metadata such as response latency, length of results, etc. - Quantitative assessment system::
1. Automatic scoring using algorithms such as BLEU, ROUGE, etc.
2. Establishment of a manual assessment scale (1-5 scale)
3. Generation of key indicators for visualization of comparative radar charts
Example of a complete process:
1. Launching parallel requests with Python multithreading
2. Storing results in Pandas DataFrame
3. Use of Matplotlib to plot elapsed time/quality curves
4. Output of evaluation reports in Markdown format
This answer comes from the articleDeepInfra Chat: experiencing and invoking a variety of open source big model chat servicesThe
May not be reproduced without permission:AI productivity tools " How to implement multi-model comparison testing in educational research scenarios?
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