science-of-finetuning/ultrachat_200k_gemma-2-2b-it-generated
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这个数据集包含512个由gemma-2-2b-it模型在ultrachat 200k测试数据集的一个子集上使用贪婪解码生成的答案。这个子集是通过过滤掉长度大于或等于1024-128个token的对话生成的，并且每个批次的答案在生成1024减去batch_prompt_lengths的最小值之后就会被截断，确保每个答案最多128个token。生成的答案共有200k个token，平均每个答案大约390个token（约300个单词或2/3页）。
This dataset contains 512 answers generated by the gemma-2-2b-it model on a subset of the ultrachat 200k test_sft dataset using greedy decoding. The subset was generated by filtering out conversations that were >= 1024 - 128 tokens long, and answers were cut off at each batch after 1024 - min(batch_prompt_lengths) generated tokens, such that each answer is at most 128 tokens long. The generated answers are 200k tokens so 390 tokens (~300 words or 2/3 pages) on average.
