CausalLM
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miniG

@@ -38,6 +38,8 @@ Cautionary Notes: It is strongly recommended to utilize a standardized implement
 Inference Parameters: Our observations suggest that, if one desires to achieve results with fewer hallucinations, it is advisable to employ sampling with top_p=0.8 followed by a temperature setting of 0.3, or alternatively, to use pure temperature sampling with a setting of 0.2. In general, a lower temperature is required compared to similar models, which we tentatively attribute to overfitting on the vast dataset.
 Disclaimer: Please note that the model was trained on unfiltered internet data. Since we do not have the capacity to vet all of it, there may be a substantial amount of objectionable content, pornography, violence, and offensive language present that we are unable to remove. Therefore, you will still need to complete your own checks on the model's safety and filter keywords in the output. Due to computational resource constraints, we are presently unable to implement RLHF for the model's ethics and safety, nor training on SFT samples that refuse to answer certain questions for restrictive fine-tuning.
 # 迷你G
@@ -52,4 +54,6 @@ Disclaimer: Please note that the model was trained on unfiltered internet data.
 推理参数：我们的观察表明，如果想要减少幻觉结果，建议使用top_p=0.8的采样方式，然后设置temperature为0.3，或者使用纯粹的temperature采样，设置为0.2。总体来说，相比类似的模型，该模型需要较低的temperature，我们暂时将其归因于在庞大数据集上的过拟合。
 免责声明：请注意，该模型是在未经过滤的互联网数据上训练的。由于我们无法对所有数据进行筛选，仍有可能存在大量不适当的内容——包括从露骨的材料到暴力和攻击性语言的内容——我们无法移除。因此，您必须自行对模型进行安全检查，并在输出中实施关键词过滤。由于计算资源的限制，我们目前无法为伦理和安全考虑进行人类反馈的强化学习（RLHF），也不能对SFT样本进行限制性微调，以限制模型回答某些问题的能力。

 Inference Parameters: Our observations suggest that, if one desires to achieve results with fewer hallucinations, it is advisable to employ sampling with top_p=0.8 followed by a temperature setting of 0.3, or alternatively, to use pure temperature sampling with a setting of 0.2. In general, a lower temperature is required compared to similar models, which we tentatively attribute to overfitting on the vast dataset.
+Regarding benchmark scores: Generally, you shouldn't worry too much about them, as people can always train specifically to achieve good results. We mainly use them as a smoke test, a quick check to ensure no major regressions have occurred. In fact, if you actually read through the benchmark questions themselves, you'll often find yourself chuckling at how inane, low-quality, or even downright silly they are.
 Disclaimer: Please note that the model was trained on unfiltered internet data. Since we do not have the capacity to vet all of it, there may be a substantial amount of objectionable content, pornography, violence, and offensive language present that we are unable to remove. Therefore, you will still need to complete your own checks on the model's safety and filter keywords in the output. Due to computational resource constraints, we are presently unable to implement RLHF for the model's ethics and safety, nor training on SFT samples that refuse to answer certain questions for restrictive fine-tuning.
 # 迷你G
 推理参数：我们的观察表明，如果想要减少幻觉结果，建议使用top_p=0.8的采样方式，然后设置temperature为0.3，或者使用纯粹的temperature采样，设置为0.2。总体来说，相比类似的模型，该模型需要较低的temperature，我们暂时将其归因于在庞大数据集上的过拟合。
+关于基准测试分数：一般来说，你不应该太过在意这些分数，因为人们总是可以专门训练以取得好成绩。我们主要将它们作为一个冒烟测试，一种快速检查，确保没有发生重大回退。事实上，如果你真的去阅读这些基准测试问题本身，你常常会发现自己会忍不住笑出声来，因为它们是多么无聊、低质量，甚至荒谬可笑。
 免责声明：请注意，该模型是在未经过滤的互联网数据上训练的。由于我们无法对所有数据进行筛选，仍有可能存在大量不适当的内容——包括从露骨的材料到暴力和攻击性语言的内容——我们无法移除。因此，您必须自行对模型进行安全检查，并在输出中实施关键词过滤。由于计算资源的限制，我们目前无法为伦理和安全考虑进行人类反馈的强化学习（RLHF），也不能对SFT样本进行限制性微调，以限制模型回答某些问题的能力。