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* 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 <iwan.kawrakow@gmail.com> |
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| .. | ||
| CMakeLists.txt | ||
| perplexity.cpp | ||
| README.md | ||
perplexity
TODO
Llama 2 70B Scorechart
| Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
|---|---|---|---|
| Q4_0 | 36.20 | 3.5550 | 3.61% |
| Q4_1 | 40.20 | 3.5125 | 2.37% |
| Q5_0 | 44.20 | 3.4744 | 1.26% |
| Q2_K | 27.27 | 3.7339 | 8.82% |
| Q3_K_S | 27.86 | 3.7019 | 7.89% |
| Q3_K_M | 30.83 | 3.5932 | 4.72% |
| Q3_K_L | 33.67 | 3.5617 | 3.80% |
| Q4_K_S | 36.39 | 3.4852 | 1.57% |
| Q4_K_M | 38.54 | 3.4725 | 1.20% |
| Q5_K_S | 44.20 | 3.4483 | 0.50% |
| Q5_K_M | 45.41 | 3.4451 | 0.40% |
| Q6_K | 52.70 | 3.4367 | 0.16% |
| fp16 | 128.5 | 3.4313 | - |