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Testing grounds for LLMs - Validation Framework #35
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https://github.com/EleutherAI/lm-evaluation-harness has out-of-the-box HF connectors, seems easiest to use. |
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h2ogpt-oasst1-512-12b.eval.log hf-causal-experimental (pretrained=h2oai/h2ogpt-oasst1-512-12b), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
h2ogpt-oasst1-512-20b.eval.log hf-causal-experimental (pretrained=h2oai/h2ogpt-oasst1-512-20b), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
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https://huggingface.co/databricks/dolly-v2-12b |
What the tasks look like: created with |
let's see if Dolly v2 12B is doing same for their numbers:
yes, consistent with their reported numbers, so command itself seems reasonable. |
undertrained older models perform worse indeed:
h2ogpt-oig-oasst1-256-20b.eval.log
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CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=h2oai/h2ogpt-oig-oasst1-256-12b --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> h2ogpt-oig-oasst1-256-12b.eval.log h2ogpt-oig-oasst1-256-12b.eval.log
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https://github.com/EleutherAI/lm-evaluation-harness
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https://twitter.com/omarsar0/status/1641792530667675648/photo/1
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