[pull] master from ggerganov:master #26
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Flake lock file updates:
• Updated input 'nixpkgs':
'github:NixOS/nixpkgs/9b19f5e77dd906cb52dade0b7bd280339d2a1f3d' (2024-01-13)
→ 'github:NixOS/nixpkgs/bbe7d8f876fbbe7c959c90ba2ae2852220573261' (2024-01-19)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* imatrix: speedup by avoiding unnecessary allocations and copies * imatrix: add --no-ppl option to skip PPL calculations altogether --------- Co-authored-by: Iwan Kawrakow <[email protected]>
* TruthfulQA: 1st attempt, does not look like it is working The same implementation can be used for HellaSwag as well, so I converted a HellaSwag validation dataset to the binary format used here and tested with that. The score is only around 50, so something is not quite right. * TruthfulQA: works but the result is bad I know it works because if I convert the HellaSwag validation data to the binary format used in the truthful_qa_score() function I get the exact same result as from the hellaswag_score() function. But I guess, the questions are tricky and the way I have done the combination of question + answer is very likely not the best. The TruthfulQA validation dataset contains 817 questions, with random chance result around 19%. With this version I get 29.1% for Mistral-7B and 55.2% for Mistral-7B-Instruct-v0.2. The HF leader board results for these two models are 42.2% and 68.3%, respectively. * TruthfulQA: fix random sample * TruthfulQA: prepare tasks in parallel for large test datasets * Rename truthful_qa to multiple_choice * Make MSVC happy I had forgotten that MSVC does not make constexpr's available inside a lambda. --------- Co-authored-by: Iwan Kawrakow <[email protected]>
Co-authored-by: Jared Van Bortel <[email protected]>
* add safetensors support to convert-lora-to-ggml.py * Update convert-lora-to-ggml.py Remove white space in line 69.
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