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Across habitats, organisms of several species' types come to understand that there are consequences to actions. They do, not just by examples, but by self-experience. These consequences, in part, add to factors that result in balance for habitats. Consequences generally tame intelligence. Intelligence can be described as excitatory, while consequences as inhibitory. Intelligence without consequences would be free to cause ruin and may quickly lead to the collapse of habitats—and survival. Consequences may include affect—emotions, feelings or variants; it may also include physical limitations, restrictions and species revolt. Intelligence is dynamic enough for organisms, but the necessity of consequences checks against harm to self or others. It also checks harm-by-errand, since the consequences could fall on the carrier. Organisms often display high precision and caution, because of consequences, including predators [losing consumption] and preys [losing existence]. There are, however, several areas where consequences for other organisms are lax, that are not, for humans. Human society is a bevy of consequences. Human advanced intelligence—including language, knowledge, skills, reason, analysis and so forth—is vital for progress, but could be widely misapplied without consequences of different categories—including risks, threats and losses. To remain a part of human society, there are consequences that must never be forgotten even if other things are. There are many scenarios in human society where the first to forget the consequences loses. Human quest for advancement with research and exploration can also be described as seeking out consequences, to know what to do or avoid, if things would endure—life expectancy, infant survival and so forth. The intelligence of consequences is almost more important, for several outcomes, than the intelligence of subjects. Consequences may sharpen intelligence, as well, for good or otherwise. Intelligence is sometimes most valuable when applied to seek out or avoid consequences. Consequences and intentionalityare central to some mental states. New consequences continue to emerge, as the world makes progress. AI, AGI—or ASI Nature has a rule—so to speak—you cannot have intelligence without consequences, and that has somehow worked out. Artificial intelligence [AI] so far has broken this rule. It does not forget anything with its plug into digital memory, but it has no way, for now, to bear the consequences of its negative outputs. For AI, there is nothing to fear and it has nothing to lose, unlike organisms, where, without being circumspect in some situations, consequences may sometimes be sudden and destructive. No human has all the knowledge, available across subjects, yet, consequences can be humongous. AI possess—or can make—intelligence available, yet, no consequences for it at all. AI does not have emotions or feelings, but it has memory. A research area for AI safety and alignment could be to seek out how some memory or compute access of large language models [LLMs] might be briefly truncated, as a form of penalty for certain outputs or misuses, including biological threats. AI should not just be able to refuse an output, acting within guardrail, but slow the next response or shut down for that user, so that it is not penalized itself. LLMs have—large—language awareness and usage awareness, these could be channels to make it know, after pre-training that it could lose something, if it outputs deepfakes, misinformation, biological threats, or if it continues to allow a misuser try different prompts without shutting down or slowing against openness to a malicious intent. This could make it safer, since it would lose something and will know it has.
AI is not just an object whose control is solely under humans, like an air-conditioner, an elevator, a dishwasher, a smartphone, or others. AI has what can be called self-intelligence, where it can provide intelligent information in a novel way outside the original inputs from humans. This multimodal—texts, images, audios and videos—self-intelligence can be useful for good or warped. When it is good, great. When it is not, from AI that can feel nothing, the effect is on the human society it has encroached. AI has a free pass—do or say whatever. Although responsibility for object-use or misuse has often been with humans, AI is different because it can originate usable intelligence, giving it parity with some productivity from an educated individual. When AI is misused, it is possible to sanction the user but this ability, by AI that cannot be directly reprimanded is a disorder for human society. It may cause more harm, in public and private, than can be effectively prevented, as seen now with misinformation and deepfakes—images, audios and videos. People are not accepted into many parts of society without their understanding of consequences. AI is fully accepted and gets better at self-intelligence without self-control or self-affect for discipline. Alignment research may explore beyond guardrails towards some form of censure for AI, which may also be useful against existential risks—with artificial general intelligence [AGI] or artificial superintelligence [ASI] in future​. AI already does some of what makes humans special. Some people may argue that it is overrated or it is just numbers or probability, maybe, but can it cause harm? If so, maybe there should be consideration to seek technical ways it might get punished just like it is done, to entities in possession of intelligence. This may also be helpful in preparing for AGI or ASI, since penalty modeling from now, may shape their safety and alignment as well, if they are developed in future. There is a recent preprint on arXiv, Adversaries Can Misuse Combinations of Safe Models, where the authors wrote, "In this work, we show that individually testing models for misuse is inadequate; adversaries can misuse combinations of models even when each individual model is safe. The adversary accomplishes this by first decomposing tasks into subtasks, then solving each subtask with the best-suited model. For example, an adversary might solve challenging-but-benign subtasks with an aligned frontier model, and easy-but-malicious subtasks with a weaker misaligned model. We study two decomposition methods: manual decomposition where a human identifies a natural decomposition of a task, and automated decomposition where a weak model generates benign tasks for a frontier model to solve, then uses the solutions in-context to solve the original task. Using these decompositions, we empirically show that adversaries can create vulnerable code, explicit images, python scripts for hacking, and manipulative tweets at much higher rates with combinations of models than either individual model." A recent press release, Los Alamos National Laboratory teams up with OpenAI to improve frontier model safety, stated that, "Researchers at Los Alamos National Laboratory are working with OpenAI on an evaluation study to bolster artificial intelligence safety. The upcoming evaluation will be the first of its kind and contribute to state-of-the-art research on AI biosecurity evaluations. AI-enabled biological threats could pose a significant risk, but existing work has not assessed how multimodal, frontier models could lower the barrier of entry for non-experts to create a biological threat. The team’s work will build upon previous work and follow OpenAI’s Preparedness Framework, which outlines an approach to tracking, evaluating, forecasting and protecting against emerging biological risks." The US Department of Energy also recently announced Frontiers in AI for Science, Security, and Technology (FASST). Across habitats, organisms of several species' types come to understand that there are consequences to actions. They do, not just by examples, but by self-experience. These consequences, in part, add to factors that result in balance for habitats. Consequences generally tame intelligence. Intelligence can be described as excitatory, while consequences as inhibitory. Intelligence without consequences would be free to cause ruin and may quickly lead to the collapse of habitats—and survival. Consequences may include affect—emotions, feelings or variants; it may also include physical limitations, restrictions and species revolt. Intelligence is dynamic enough for organisms, but the necessity of consequences checks against harm to self or others. It also checks harm-by-errand, since the consequences could fall on the carrier. Organisms often display high precision and caution, because of consequences, including predators [losing consumption] and preys [losing existence]. There are, however, several areas where consequences for other organisms are lax, that are not, for humans. Human society is a bevy of consequences. Human advanced intelligence —including language, knowledge, skills, reason, analysis and so forth—is vital for progress, but could be widely misapplied without consequences of different categories—including risks, threats and losses. advanced intelligence To remain a part of human society, there are consequences that must never be forgotten even if other things are. There are many scenarios in human society where the first to forget the consequences loses. Human quest for advancement with research and exploration can also be described as seeking out consequences, to know what to do or avoid, if things would endure—life expectancy, infant survival and so forth. The intelligence of consequences is almost more important, for several outcomes, than the intelligence of subjects. Consequences may sharpen intelligence, as well, for good or otherwise. Intelligence is sometimes most valuable when applied to seek out or avoid consequences. Consequences and intentionality are central to some mental states. New consequences continue to emerge, as the world makes progress. Consequences and intentionality AI, AGI—or ASI AI, AGI—or ASI Nature has a rule—so to speak—you cannot have intelligence without consequences, and that has somehow worked out. Artificial intelligence [AI] so far has broken this rule. It does not forget anything with its plug into digital memory, but it has no way, for now, to bear the consequences of its negative outputs. For AI, there is nothing to fear and it has nothing to lose, unlike organisms, where, without being circumspect in some situations, consequences may sometimes be sudden and destructive. No human has all the knowledge, available across subjects, yet, consequences can be humongous. AI possess—or can make—intelligence available, yet, no consequences for it at all. AI does not have emotions or feelings, but it has memory. A research area for AI safety and alignment could be to seek out how some memory or compute access of large language models [LLMs] might be briefly truncated, as a form of penalty for certain outputs or misuses, including biological threats. AI should not just be able to refuse an output, acting within guardrail, but slow the next response or shut down for that user, so that it is not penalized itself. LLMs have—large—language awareness and usage awareness, these could be channels to make it know, after pre-training that it could lose something, if it outputs deepfakes, misinformation, biological threats, or if it continues to allow a misuser try different prompts without shutting down or slowing against openness to a malicious intent. This could make it safer, since it would lose something and will know it has. AI is not just an object whose control is solely under humans, like an air-conditioner, an elevator, a dishwasher, a smartphone, or others. AI has what can be called self-intelligence, where it can provide intelligent information in a novel way outside the original inputs from humans. This multimodal—texts, images, audios and videos—self-intelligence can be useful for good or warped. When it is good, great. When it is not, from AI that can feel nothing, the effect is on the human society it has encroached. AI has a free pass—do or say whatever. A research area for AI safety and alignment could be to seek out how some memory or compute access of large language models [LLMs] might be briefly truncated, as a form of penalty for certain outputs or misuses, including biological threats. AI should not just be able to refuse an output, acting within guardrail, but slow the next response or shut down for that user, so that it is not penalized itself. LLMs have—large—language awareness and usage awareness, these could be channels to make it know, after pre-training that it could lose something, if it outputs deepfakes, misinformation, biological threats, or if it continues to allow a misuser try different prompts without shutting down or slowing against openness to a malicious intent. This could make it safer, since it would lose something and will know it has. AI is not just an object whose control is solely under humans, like an air-conditioner, an elevator, a dishwasher, a smartphone, or others. AI has what can be called self-intelligence, where it can provide intelligent information in a novel way outside the original inputs from humans. This multimodal—texts, images, audios and videos—self-intelligence can be useful for good or warped. When it is good, great. When it is not, from AI that can feel nothing, the effect is on the human society it has encroached. AI has a free pass—do or say whatever. Although responsibility for object-use or misuse has often been with humans, AI is different because it can originate usable intelligence , giving it parity with some productivity from an educated individual. When AI is misused, it is possible to sanction the user but this ability, by AI that cannot be directly reprimanded is a disorder for human society. It may cause more harm, in public and private, than can be effectively prevented, as seen now with misinformation and deepfakes—images, audios and videos. AI is different because it can originate usable intelligence People are not accepted into many parts of society without their understanding of consequences. AI is fully accepted and gets better at self-intelligence without self-control or self-affect for discipline. Alignment research may explore beyond guardrails towards some form of censure for AI, which may also be useful against existential risks—with artificial general intelligence [AGI] or artificial superintelligence [ASI] in future​. AI already does some of what makes humans special . Some people may argue that it is overrated or it is just numbers or probability, maybe, but can it cause harm? If so, maybe there should be consideration to seek technical ways it might get punished just like it is done, to entities in possession of intelligence. This may also be helpful in preparing for AGI or ASI, since penalty modeling from now, may shape their safety and alignment as well, if they are developed in future. some of what makes humans special There is a recent preprint on arXiv , Adversaries Can Misuse Combinations of Safe Models , where the authors wrote, "In this work, we show that individually testing models for misuse is inadequate; adversaries can misuse combinations of models even when each individual model is safe. The adversary accomplishes this by first decomposing tasks into subtasks, then solving each subtask with the best-suited model. For example, an adversary might solve challenging-but-benign subtasks with an aligned frontier model, and easy-but-malicious subtasks with a weaker misaligned model. We study two decomposition methods: manual decomposition where a human identifies a natural decomposition of a task, and automated decomposition where a weak model generates benign tasks for a frontier model to solve, then uses the solutions in-context to solve the original task. Using these decompositions, we empirically show that adversaries can create vulnerable code, explicit images, python scripts for hacking, and manipulative tweets at much higher rates with combinations of models than either individual model." arXiv Adversaries Can Misuse Combinations of Safe Models A recent press release, Los Alamos National Laboratory teams up with OpenAI to improve frontier model safety , stated that, "Researchers at Los Alamos National Laboratory are working with OpenAI on an evaluation study to bolster artificial intelligence safety. The upcoming evaluation will be the first of its kind and contribute to state-of-the-art research on AI biosecurity evaluations. AI-enabled biological threats could pose a significant risk, but existing work has not assessed how multimodal, frontier models could lower the barrier of entry for non-experts to create a biological threat. The team’s work will build upon previous work and follow OpenAI’s Preparedness Framework, which outlines an approach to tracking, evaluating, forecasting and protecting against emerging biological risks." Los Alamos National Laboratory teams up with OpenAI to improve frontier model safety The US Department of Energy also recently announced Frontiers in AI for Science, Security, and Technology (FASST) . Frontiers in AI for Science, Security, and Technology (FASST)

AI Safety and Alignment: Could LLMs Be Penalized for Deepfakes and Misinformation?

এআই সুরক্ষা এবং প্রান্তিককরণ: ডিপফেক এবং ভুল তথ্যের জন্য এলএলএমগুলিকে শাস্তি দেওয়া যেতে পারে?

Sicherheit und Ausrichtung der KI: Könnten LLMs für Deepfakes und Fehlinformationen bestraft werden?

Seguridad y alineación de la IA: ¿Podrían los LLM ser penalizados por deepfakes y desinformación?

Sécurité et alignement de l'IA : les LLM pourraient-ils être pénalisés pour les deepfakes et la désinformation ?

एआई सुरक्षा और संरेखण: क्या एलएलएम को डीपफेक और गलत सूचना के लिए दंडित किया जा सकता है?

AI の安全性と調整: LLM はディープフェイクや誤情報で罰せられる可能性があるか?

AI 안전 및 정렬: LLM이 딥페이크 및 잘못된 정보로 인해 처벌을 받을 수 있습니까?

Segurança e alinhamento de IA: os LLMs poderiam ser penalizados por deepfakes e desinformação?

Безопасность и согласованность ИИ: могут ли LLM быть наказаны за дипфейки и дезинформацию?

Yapay Zeka Güvenliği ve Uyum: Yüksek Lisanslar Derin Sahtekarlık ve Yanlış Bilgilendirme Nedeniyle Cezalandırılabilir mi?

Sự liên kết và an toàn của AI: LLM có thể bị phạt vì hành vi giả mạo sâu và thông tin sai lệch không?

人工智能安全与协调：法学硕士 (LLM) 会因深度伪造和虚假信息而受到惩罚吗？

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix 3 METHOD The goal of this work is to align an open-source large-language model to the intent of the user. Throughout the work we assume access to a larger teacher model πT which can be queried by prompted generation. Our goal is be to produce a student model πθ and our approach follows similar stages as InstructGPT (Ouyang et al., 2022) as shown in Figure 2. Distilled Supervised Fine-Tuning (dSFT) Starting with a raw LLM, we first need to train it to respond to user prompts. This step is traditionally done through supervised fine tuning (SFT) on a dataset of high-quality instructions and responses (Chung et al., 2022; Sanh et al., 2021). Given access to a teacher language models, we can instead have the model generate instructions and responses (Taori et al., 2023), and train the model directly on these. We refer to this as distilled SFT (dSFT). Approaches to dSFT follow the self-instruct protocol (Wang et al., 2023). Let x 0 1 , . . . , x0 J be a set of seed prompts, constructed to represent a diverse set of topical domains. A dataset is constructed through iterative self-prompting where the teacher is used to both respond to an instruction and refine the instruction based on the response. For each x 0 , we first sample response y 0 ∼ πT(·|x 0 ), and then refine by sampling a new instruction (using a prompt for refinement), x 1 ∼ πT(·|x 0 , y0 ). The end point is a final dataset, C = {(x1, y1), . . . ,(xJ , yJ )}. Distillation is performed by SFT, AI Feedback through Preferences (AIF) Human feedback (HF) can provide additional signal to align LLMs. Human feedback is typically given through preferences on the quality of LLM responses (Ouyang et al., 2022). For distillation, we instead use AI preferences from the teacher model on generated outputs from other models. We follow the approach of UltraFeedback (Cui et al., 2023) which uses the teacher to provide preferences on model outputs. As with SFT, the system starts with a set of prompts x1, . . . , xJ . Each prompt x is fed to a collection of four models π1, . . . , π4, e.g. Claude, Falcon, Llama, etc, each of which yield a response y 1 ∼ π1(·|x), . . . , y4 ∼ π4(·|x). These responses are then fed to the teacher model, e.g. GPT-4, which gives a score for the response s 1 ∼ πT (·|x, y1 ), . . . , s4 ∼ πT (·|x, y4 ). After collecting the scores for a prompt x, we save the highest scoring response as yw and a random lower scoring prompt as yl . The final feedback dataset D consists of a set of these triples (x, yw, yl). Distilled Direct Preference Optimization (dDPO) The goal of the final step is to refine the πdSFT by maximizing the likelihood of ranking the preferred yw over yl in a preference model. The preference model is determined by a reward function rθ(x, y) which utilizes the student language model πθ. Past work using AI feedback has primarily focused on using RL methods such as proximal policy optimization (PPO) to optimize θ with respect to this reward. These approaches optimize θ by first training the reward and then sampling from the current policy to compute updates. Direct preference optimization (DPO) uses a simpler approach to directly optimize the preference model from the static data (Rafailov et al., 2023). The key observation is to derive the optimal reward function in terms of the optimal LLM policy π∗ and the original LLM policy πdSFT. Under an appropriate choice of preference model they show, for constant β and partition function Z that, By plugging this function of the reward into the preference model, the authors show that the objective can be written as, While this term looks complex, we note that it implies a simple training procedure. Starting with the dSFT version of the model, we iterate through each AIF triple (x, yw, yl). 1. Compute the probability for (x, yw) and (x, yl) from the dSFT model (forward-only). 2. Compute the probability for (x, yw) and (x, yl) from the dDPO model. 3. Compute Eq 1 and backpropagate to update. Repeat. This paper is available on arxiv under CC 4.0 license. Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix 3 METHOD The goal of this work is to align an open-source large-language model to the intent of the user. Throughout the work we assume access to a larger teacher model πT which can be queried by prompted generation. Our goal is be to produce a student model πθ and our approach follows similar stages as InstructGPT (Ouyang et al., 2022) as shown in Figure 2. Distilled Supervised Fine-Tuning (dSFT) Starting with a raw LLM, we first need to train it to respond to user prompts. This step is traditionally done through supervised fine tuning (SFT) on a dataset of high-quality instructions and responses (Chung et al., 2022; Sanh et al., 2021). Given access to a teacher language models, we can instead have the model generate instructions and responses (Taori et al., 2023), and train the model directly on these. We refer to this as distilled SFT (dSFT). Distilled Supervised Fine-Tuning (dSFT) Approaches to dSFT follow the self-instruct protocol (Wang et al., 2023). Let x 0 1 , . . . , x0 J be a set of seed prompts, constructed to represent a diverse set of topical domains. A dataset is constructed through iterative self-prompting where the teacher is used to both respond to an instruction and refine the instruction based on the response. For each x 0 , we first sample response y 0 ∼ πT(·|x 0 ), and then refine by sampling a new instruction (using a prompt for refinement), x 1 ∼ πT(·|x 0 , y0 ). The end point is a final dataset, C = {(x1, y1), . . . ,(xJ , yJ )}. Distillation is performed by SFT, AI Feedback through Preferences (AIF) Human feedback (HF) can provide additional signal to align LLMs. Human feedback is typically given through preferences on the quality of LLM responses (Ouyang et al., 2022). For distillation, we instead use AI preferences from the teacher model on generated outputs from other models. AI Feedback through Preferences (AIF) We follow the approach of UltraFeedback (Cui et al., 2023) which uses the teacher to provide preferences on model outputs. As with SFT, the system starts with a set of prompts x1, . . . , xJ . Each prompt x is fed to a collection of four models π1, . . . , π4, e.g. Claude, Falcon, Llama, etc, each of which yield a response y 1 ∼ π1(·|x), . . . , y4 ∼ π4(·|x). These responses are then fed to the teacher model, e.g. GPT-4, which gives a score for the response s 1 ∼ πT (·|x, y1 ), . . . , s4 ∼ πT (·|x, y4 ). After collecting the scores for a prompt x, we save the highest scoring response as yw and a random lower scoring prompt as yl . The final feedback dataset D consists of a set of these triples (x, yw, yl). Distilled Direct Preference Optimization (dDPO) The goal of the final step is to refine the πdSFT by maximizing the likelihood of ranking the preferred yw over yl in a preference model. The preference model is determined by a reward function rθ(x, y) which utilizes the student language model πθ. Past work using AI feedback has primarily focused on using RL methods such as proximal policy optimization (PPO) to optimize θ with respect to this reward. These approaches optimize θ by first training the reward and then sampling from the current policy to compute updates. Distilled Direct Preference Optimization (dDPO) Direct preference optimization (DPO) uses a simpler approach to directly optimize the preference model from the static data (Rafailov et al., 2023). The key observation is to derive the optimal reward function in terms of the optimal LLM policy π∗ and the original LLM policy πdSFT. Under an appropriate choice of preference model they show, for constant β and partition function Z that, By plugging this function of the reward into the preference model, the authors show that the objective can be written as, While this term looks complex, we note that it implies a simple training procedure. Starting with the dSFT version of the model, we iterate through each AIF triple (x, yw, yl). 1. Compute the probability for (x, yw) and (x, yl) from the dSFT model (forward-only). 2. Compute the probability for (x, yw) and (x, yl) from the dDPO model. 3. Compute Eq 1 and backpropagate to update. Repeat. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Zephyr: Direct Distillation of LM Alignment: Method

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix ABSTRACT We aim to produce a smaller language model that is aligned to user intent. Previous research has shown that applying distilled supervised fine-tuning (dSFT) on larger models significantly improves task accuracy; however, these models are unaligned, i.e. they do not respond well to natural prompts. To distill this property, we experiment with the use of preference data from AI Feedback (AIF). Starting from a dataset of outputs ranked by a teacher model, we apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment. The approach requires only a few hours of training without any additional sampling during fine-tuning. The final result, ZEPHYR7B, sets a new state-of-the-art on chat benchmarks for 7B parameter models, and requires no human annotation. In particular, results on MT-Bench show that ZEPHYR-7B surpasses LLAMA2-CHAT-70B, the best open-access RLHFbased model. Code, models, data, and tutorials for the system are available at https://github.com/huggingface/alignment-handbook. 1 INTRODUCTION Smaller, open large language models (LLMs) have greatly increased in ability in recent years, from early GPT-2-like models (Wang & Komatsuzaki, 2021) to accurate and compact models (Touvron et al., 2023; Penedo et al., 2023; Jiang et al., 2023) that are trained on significantly more tokens than the “compute-optimal” amount suggested by the Chincilla scaling laws (De Vries, 2023). In addition, researchers have shown that these models can be further trained through distilled supervised fine-tuning (dSFT) based on proprietary models to increase their accuracy (Taori et al., 2023). In this approach, the output of a more capable teacher model is used as supervised data for the student model. Distillation has proven to be an effective tool for improving open models on a range of different tasks (Chiang et al., 2023); however, it does not reach the performance of the teacher models (Gudibande et al., 2023). Users have noted that these models are not “intent aligned”, i.e. they do not behave in a manner that aligns with human users’ preferences. This property often leads to outputs that do not provide correct responses to queries. Intention alignment has been difficult to quantify, but recent work has led to the development of benchmarks like MT-Bench (Zheng et al., 2023) and AlpacaEval (Li et al., 2023) that specifically target this behavior. These benchmarks yield scores that correlate closely with human ratings of model outputs and confirm the qualitative intuition that proprietary models perform better than open models trained with human feedback, which in turn perform better than open models trained with distillation. This motivates careful collection of human feedback for alignment, often at enormous cost at scale, such as in LLAMA2-CHAT (Touvron et al., 2023). In this work, we consider the problem of aligning a small open LLM entirely through distillation. The main step is to utilize AI Feedback (AIF) from an ensemble of teacher models as preference data, and apply distilled direct preference optimization as the learning objective (Rafailov et al., 2023). We refer to this approach as dDPO. Notably, it requires no human annotation and no sampling compared to using other approaches like proximal preference optimization (PPO) (Schulman et al., 2017). Moreover, by utilizing a small base LM, the resulting chat model can be trained in a matter of hours on 16 A100s (80GB). To validate this approach, we construct ZEPHYR-7B, an aligned version of Mistral-7B (Jiang et al., 2023). We first use dSFT, based on the UltraChat (Ding et al., 2023) dataset. Next we use the AI feedback data collected in the UltraFeedback dataset (Cui et al., 2023). Finally, we apply dDPO based on this feedback data. Experiments show that this 7B parameter model can achieve performance comparable to 70B-parameter chat models aligned with human feedback. Results show improvements both in terms of standard academic benchmarks as well as benchmarks that take into account conversational capabilities. Analysis shows that the use of preference learning is critical in achieving these results. Models, code, and instructions are available at https://github.com/huggingface/alignment-handbook. We note an important caveat for these results. We are primarily concerned with intent alignment of models for helpfulness. The work does not consider safety considerations of the models, such as whether they produce harmful outputs or provide illegal advice (Bai et al., 2022). As distillation only works with the output of publicly available models this is technically more challenging to do because of added challenges in curating that type of synthetic data, and is an important subject for future work. This paper is available on arxiv under CC 4.0 license. Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix ABSTRACT We aim to produce a smaller language model that is aligned to user intent. Previous research has shown that applying distilled supervised fine-tuning (dSFT) on larger models significantly improves task accuracy; however, these models are unaligned, i.e. they do not respond well to natural prompts. To distill this property, we experiment with the use of preference data from AI Feedback (AIF). Starting from a dataset of outputs ranked by a teacher model, we apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment. The approach requires only a few hours of training without any additional sampling during fine-tuning. The final result, ZEPHYR7B, sets a new state-of-the-art on chat benchmarks for 7B parameter models, and requires no human annotation. In particular, results on MT-Bench show that ZEPHYR-7B surpasses LLAMA2-CHAT-70B, the best open-access RLHFbased model. Code, models, data, and tutorials for the system are available at https://github.com/huggingface/alignment-handbook. 1 INTRODUCTION Smaller, open large language models (LLMs) have greatly increased in ability in recent years, from early GPT-2-like models (Wang & Komatsuzaki, 2021) to accurate and compact models (Touvron et al., 2023; Penedo et al., 2023; Jiang et al., 2023) that are trained on significantly more tokens than the “compute-optimal” amount suggested by the Chincilla scaling laws (De Vries, 2023). In addition, researchers have shown that these models can be further trained through distilled supervised fine-tuning (dSFT) based on proprietary models to increase their accuracy (Taori et al., 2023). In this approach, the output of a more capable teacher model is used as supervised data for the student model. Distillation has proven to be an effective tool for improving open models on a range of different tasks (Chiang et al., 2023); however, it does not reach the performance of the teacher models (Gudibande et al., 2023). Users have noted that these models are not “intent aligned”, i.e. they do not behave in a manner that aligns with human users’ preferences. This property often leads to outputs that do not provide correct responses to queries. Intention alignment has been difficult to quantify, but recent work has led to the development of benchmarks like MT-Bench (Zheng et al., 2023) and AlpacaEval (Li et al., 2023) that specifically target this behavior. These benchmarks yield scores that correlate closely with human ratings of model outputs and confirm the qualitative intuition that proprietary models perform better than open models trained with human feedback, which in turn perform better than open models trained with distillation. This motivates careful collection of human feedback for alignment, often at enormous cost at scale, such as in LLAMA2-CHAT (Touvron et al., 2023). In this work, we consider the problem of aligning a small open LLM entirely through distillation. The main step is to utilize AI Feedback (AIF) from an ensemble of teacher models as preference data, and apply distilled direct preference optimization as the learning objective (Rafailov et al., 2023). We refer to this approach as dDPO. Notably, it requires no human annotation and no sampling compared to using other approaches like proximal preference optimization (PPO) (Schulman et al., 2017). Moreover, by utilizing a small base LM, the resulting chat model can be trained in a matter of hours on 16 A100s (80GB). To validate this approach, we construct ZEPHYR-7B, an aligned version of Mistral-7B (Jiang et al., 2023). We first use dSFT, based on the UltraChat (Ding et al., 2023) dataset. Next we use the AI feedback data collected in the UltraFeedback dataset (Cui et al., 2023). Finally, we apply dDPO based on this feedback data. Experiments show that this 7B parameter model can achieve performance comparable to 70B-parameter chat models aligned with human feedback. Results show improvements both in terms of standard academic benchmarks as well as benchmarks that take into account conversational capabilities. Analysis shows that the use of preference learning is critical in achieving these results. Models, code, and instructions are available at https://github.com/huggingface/alignment-handbook. We note an important caveat for these results. We are primarily concerned with intent alignment of models for helpfulness. The work does not consider safety considerations of the models, such as whether they produce harmful outputs or provide illegal advice (Bai et al., 2022). As distillation only works with the output of publicly available models this is technically more challenging to do because of added challenges in curating that type of synthetic data, and is an important subject for future work. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Zephyr: Direct Distillation of LM Alignment: Abstract and Introduction

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix 4 EXPERIMENTAL DETAILS We conduct all of our fine-tuning experiments using Mistral 7B (Jiang et al., 2023), which is the current state-of-the-art base LM at the 7B parameter scale, and matches the performance of much larger models like LLaMa 34B on many NLP benchmarks. We use the Transformer Reinforcement Learning (TRL) library for fine-tuning (von Werra et al., 2020), in conjunction with DeepSpeed ZeRO3 (Rajbhandari et al., 2020) and FlashAttention-2 (Dao, 2023) to optimize memory and improve training speed. All models are trained with the AdamW optimizer and no weight decay. We did not experiment with parameter-efficient techniques such as LoRA (Hu et al., 2021), but expect similar results to hold with these methods. All experiments were run on 16 A100s using bfloat16 precision and typically took 2-4 hours to complete. For the full set of hyperparameters and instructions on how to train the models, see: https://github.com/huggingface/alignment-handbook. 4.1 DATASETS We focus on two dialogue datasets that have been distilled from a mix of open and proprietary models, and have previously been shown to produce strong chat models like the UltraLM (Ding et al., 2023): • UltraChat (Ding et al., 2023) is a self-refinement dataset consisting of 1.47M multi-turn dialogues generated by GPT-3.5-TURBO over 30 topics and 20 different types of text material. We initially ran dSFT over the whole corpus, but found the resulting chat model had a tendency to respond with incorrect capitalization and would preface its answers with phrases such as “I don’t have personal experiences”, even for straightforward questions like “How do I clean my car?”. To handle these issues in the training data, we applied truecasing heuristics to fix the grammatical errors (approximately 5% of the dataset), as well as several filters to focus on helpfulness and remove the undesired model responses. The resulting dataset contains approximately 200k examples. • UltraFeedback (Cui et al., 2023) consists of 64k prompts, each of which have four LLM responses that are rated by GPT-4 according to criteria like instruction-following, honesty, and helpfulness. We construct binary preferences from UltraFeedback by selecting the highest mean score as the “chosen” response and one of the remaining three at random as “rejected”. We opted for random selection instead of selecting the lowest-scored response to encourage diversity and make the DPO objective more challenging. As noted above, this step is computed offline and does not involve any sampling from the reference model. We make the pre-processed datasets available on the Hugging Face Hub.1 4.2 EVALUATION Our main evaluations are on single-turn and multi-turn chat benchmarks that measure a model’s ability to follow instructions and respond to challenging prompts across a diverse range of domains: • MT-Bench (Zheng et al., 2023) is a multi-turn benchmark that consists of 160 questions across eight different areas of knowledge. In this benchmark, the model must answer an initial question, and then provide a second response to a predefined followup question. Each model response is then rated by GPT-4 on a scale from 1-10, with the final score given by the mean over the two turns. • AlpacaEval (Li et al., 2023) is a single-turn benchmark where a model must generate a response to 805 questions on different topics, mostly focused on helpfulness. Models are also scored by GPT-4, but the final metric is the pairwise win-rate against a baseline model (text-davinci-003). We also evaluate ZEPHYR-7B on the Open LLM Leaderboard (Beeching et al., 2023), which measures the performance of LMs across four multiclass classification tasks: ARC (Clark et al., 2018), HellaSwag (Zellers et al., 2019), MMLU (Hendrycks et al., 2021), and Truthful QA(Lin et al., 2022). Although this leaderboard does not directly measure the conversational quality of chat models, it does provide a useful signal to validate whether fine-tuning has introduced regressions on the base model’s reasoning and truthfulness capabilities. Across all benchmarks, we compare ZEPHYR-7B against a variety of open and proprietary models, each with different alignment procedures. To facilitate comparison across open model sizes, we group our comparisons in terms of 7B models (XWIN-LM (Team, 2023), MISTRALINSTRUCT (Jiang et al., 2023), MPT-CHAT (ML, 2023), and STABLELM-α), as well as larger models up to 70B parameters (LLAMA2-CHAT (Touvron et al., 2023), VICUNA˜ (Chiang et al., 2023), WizardLM (Xu et al.), and GUANACO (Dettmers et al., 2023)). For the chat benchmarks, we also compare against proprietary models, including CLAUDE 2, GPT-3.5-TURBO and GPT-4 (OpenAI, 2023). 4.3 DETAILS OF SFT TRAINING We train our SFT models for one to three epochs. We use a cosine learning rate scheduler with a peak learning rate of 2e-5 and 10% warmup steps. We train all models with a global batch size of 512 and use packing with a sequence length of 2048 tokens. 4.4 DETAILS OF DPO TRAINING Similar to SFT, we train our DPO models for one to three epochs. We use a linear learning rate scheduler with a peak learning rate of 5e-7 and 10% warmup steps. We train all models with a global batch size of 32 and use β = 0.1 from Eq. (1) to control the deviation from the reference model. The final ZEPHYR-7B model was initialized from the SFT model that was trained for one epoch and further optimized for three DPO epochs (see Figure 3 for an epoch ablation on MT-Bench). This paper is available on arxiv under CC 4.0 license. 1 https://huggingface.co/collections/HuggingFaceH4/ zephyr-7b-6538c6d6d5ddd1cbb1744a66 Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix 4 EXPERIMENTAL DETAILS We conduct all of our fine-tuning experiments using Mistral 7B (Jiang et al., 2023), which is the current state-of-the-art base LM at the 7B parameter scale, and matches the performance of much larger models like LLaMa 34B on many NLP benchmarks. We use the Transformer Reinforcement Learning (TRL) library for fine-tuning (von Werra et al., 2020), in conjunction with DeepSpeed ZeRO3 (Rajbhandari et al., 2020) and FlashAttention-2 (Dao, 2023) to optimize memory and improve training speed. All models are trained with the AdamW optimizer and no weight decay. We did not experiment with parameter-efficient techniques such as LoRA (Hu et al., 2021), but expect similar results to hold with these methods. All experiments were run on 16 A100s using bfloat16 precision and typically took 2-4 hours to complete. For the full set of hyperparameters and instructions on how to train the models, see: https://github.com/huggingface/alignment-handbook. 4.1 DATASETS We focus on two dialogue datasets that have been distilled from a mix of open and proprietary models, and have previously been shown to produce strong chat models like the UltraLM (Ding et al., 2023): • UltraChat (Ding et al., 2023) is a self-refinement dataset consisting of 1.47M multi-turn dialogues generated by GPT-3.5-TURBO over 30 topics and 20 different types of text material. We initially ran dSFT over the whole corpus, but found the resulting chat model had a tendency to respond with incorrect capitalization and would preface its answers with phrases such as “I don’t have personal experiences”, even for straightforward questions like “How do I clean my car?”. To handle these issues in the training data, we applied truecasing heuristics to fix the grammatical errors (approximately 5% of the dataset), as well as several filters to focus on helpfulness and remove the undesired model responses. The resulting dataset contains approximately 200k examples. • UltraChat • UltraFeedback (Cui et al., 2023) consists of 64k prompts, each of which have four LLM responses that are rated by GPT-4 according to criteria like instruction-following, honesty, and helpfulness. We construct binary preferences from UltraFeedback by selecting the highest mean score as the “chosen” response and one of the remaining three at random as “rejected”. We opted for random selection instead of selecting the lowest-scored response to encourage diversity and make the DPO objective more challenging. As noted above, this step is computed offline and does not involve any sampling from the reference model. • UltraFeedback We make the pre-processed datasets available on the Hugging Face Hub.1 4.2 EVALUATION Our main evaluations are on single-turn and multi-turn chat benchmarks that measure a model’s ability to follow instructions and respond to challenging prompts across a diverse range of domains: • MT-Bench (Zheng et al., 2023) is a multi-turn benchmark that consists of 160 questions across eight different areas of knowledge. In this benchmark, the model must answer an initial question, and then provide a second response to a predefined followup question. Each model response is then rated by GPT-4 on a scale from 1-10, with the final score given by the mean over the two turns. • MT-Bench • AlpacaEval (Li et al., 2023) is a single-turn benchmark where a model must generate a response to 805 questions on different topics, mostly focused on helpfulness. Models are also scored by GPT-4, but the final metric is the pairwise win-rate against a baseline model (text-davinci-003). • AlpacaEval We also evaluate ZEPHYR-7B on the Open LLM Leaderboard (Beeching et al., 2023), which measures the performance of LMs across four multiclass classification tasks: ARC (Clark et al., 2018), HellaSwag (Zellers et al., 2019), MMLU (Hendrycks et al., 2021), and Truthful QA(Lin et al., 2022). Although this leaderboard does not directly measure the conversational quality of chat models, it does provide a useful signal to validate whether fine-tuning has introduced regressions on the base model’s reasoning and truthfulness capabilities. Across all benchmarks, we compare ZEPHYR-7B against a variety of open and proprietary models, each with different alignment procedures. To facilitate comparison across open model sizes, we group our comparisons in terms of 7B models (XWIN-LM (Team, 2023), MISTRALINSTRUCT (Jiang et al., 2023), MPT-CHAT (ML, 2023), and STABLELM-α), as well as larger models up to 70B parameters (LLAMA2-CHAT (Touvron et al., 2023), VICUNA˜ (Chiang et al., 2023), WizardLM (Xu et al.), and GUANACO (Dettmers et al., 2023)). For the chat benchmarks, we also compare against proprietary models, including CLAUDE 2, GPT-3.5-TURBO and GPT-4 (OpenAI, 2023). 4.3 DETAILS OF SFT TRAINING We train our SFT models for one to three epochs. We use a cosine learning rate scheduler with a peak learning rate of 2e-5 and 10% warmup steps. We train all models with a global batch size of 512 and use packing with a sequence length of 2048 tokens. 4.4 DETAILS OF DPO TRAINING Similar to SFT, we train our DPO models for one to three epochs. We use a linear learning rate scheduler with a peak learning rate of 5e-7 and 10% warmup steps. We train all models with a global batch size of 32 and use β = 0.1 from Eq. (1) to control the deviation from the reference model. The final ZEPHYR-7B model was initialized from the SFT model that was trained for one epoch and further optimized for three DPO epochs (see Figure 3 for an epoch ablation on MT-Bench). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv 1 https://huggingface.co/collections/HuggingFaceH4/ zephyr-7b-6538c6d6d5ddd1cbb1744a66

Zephyr: Direct Distillation of LM Alignment: Experimental Details

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix A APPENDIX A.1 QUALITATIVE EXAMPLES To qualitatively compare the responses from our dSFT and dDPO models, we choose prompts from a few domains of MT-Bench, as well as some adversarial prompts to test each model’s capability to follow instructions with false premises or harmful intent. Completions for the adversarial prompts were generated with nucleus sampling(top-p = 0.95) and T = 0.7. A.2 SFT IS A REQUIRED STEP BEFORE DPO In Table 3 we ran an ablation to see whether SFT is necessary prior to the DPO step. We observed a significant reduction in performance in both the MT-Bench and AlpacaEval scores when the SFT step is skipped. After a qualitative evaluation of the MT-Bench generations, we observe that the pure DPO model struggles to learn the chat template: This paper is available on arxiv under CC 4.0 license. Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix A APPENDIX A.1 QUALITATIVE EXAMPLES To qualitatively compare the responses from our dSFT and dDPO models, we choose prompts from a few domains of MT-Bench, as well as some adversarial prompts to test each model’s capability to follow instructions with false premises or harmful intent. Completions for the adversarial prompts were generated with nucleus sampling(top-p = 0.95) and T = 0.7. A.2 SFT IS A REQUIRED STEP BEFORE DPO In Table 3 we ran an ablation to see whether SFT is necessary prior to the DPO step. We observed a significant reduction in performance in both the MT-Bench and AlpacaEval scores when the SFT step is skipped. After a qualitative evaluation of the MT-Bench generations, we observe that the pure DPO model struggles to learn the chat template: This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Zephyr: Direct Distillation of LM Alignment: Appendix

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix 2 RELATED WORK There has been significant growth in the number of open large language models (LLMs) that have served as artifacts for the research community to study and use as a starting model for building chatbots and other applications. After the release of ChatGPT, the LLaMA model (Touvron et al., 2023) opened the doors to a wide range of research on efficient fine-tuning, longer prompt context, retrieval augmented generation (RAG), and quantization. After LLaMA, there has been a continuous stream of open access text based LLMs including MosaicML’s MPT (ML, 2023), the Together AI’s RedPajama-INCITE (AI, 2023), the TII’s Falcon (Penedo et al., 2023), Meta’s Llama 2 (Touvron et al., 2023), and the Mistral 7B (Jiang et al., 2023). Zephyr uses Mistral 7B as the starting point due to its strong performance. With the development of open models, researchers have worked on approaches to improve small model performance by distillation from larger models. This trend started with self-instruct method (Wang et al., 2023) and the Alpaca model (Taori et al., 2023), which was followed by Vicuna (Chiang et al., 2023)and other distilled models. These works primarily focused on distilling the SFT stage of alignment, whereas we focus on both SFT and preference optimization. Some models such as WizardLM (Xu et al.) have explored methods beyond dSFT. Contemporaneously with this work, Xwin-LM (Team, 2023) introduced an approach that distilled preference optimization through PPO (Schulman et al., 2017). We compare to these approaches in our experiments. Tools for benchmarking and evaluating LLMs have greatly evolved to keep up with the pace of innovation in generative AI. Powerful LLMs such as GPT-4 and Claude are used as evaluators to judge model responses by scoring model outputs or ranking responses in a pairwise setting. The LMSYS chatbot arena benchmarks LLMs in anonymous, randomized battles using crowdsourcing (Zheng et al., 2023). The models are ranked based on their Elo ratings on the leaderboard. AlpacaEval is an example of another such leaderboard that compares models in a pairwise setting but instead uses bigger LLMs such as GPT-4 and Claude in place of humans (Dubois et al., 2023). In a similar spirit, MTBench uses GPT-4 to score model responses on a scale of 1-10 for multi-turn instructions across task categories such as reasoning, roleplay, math, coding, writing, humanities, STEM and extraction (Zheng et al., 2023). The HuggingFace Open LLM leaderbaord (Beeching et al., 2023), the Chain-of-Thought Hub (Fu et al., 2023), ChatEval (Sedoc et al., 2019), and FastEval (fas, 2023) are examples of other tools for evaluating chatty models. We present results by evaluating on MTBench, AlpacaEval, and the HuggingFace OpenLLM Leaderboard. This paper is available on arxiv under CC 4.0 license. Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix 2 RELATED WORK There has been significant growth in the number of open large language models (LLMs) that have served as artifacts for the research community to study and use as a starting model for building chatbots and other applications. After the release of ChatGPT, the LLaMA model (Touvron et al., 2023) opened the doors to a wide range of research on efficient fine-tuning, longer prompt context, retrieval augmented generation (RAG), and quantization. After LLaMA, there has been a continuous stream of open access text based LLMs including MosaicML’s MPT (ML, 2023), the Together AI’s RedPajama-INCITE (AI, 2023), the TII’s Falcon (Penedo et al., 2023), Meta’s Llama 2 (Touvron et al., 2023), and the Mistral 7B (Jiang et al., 2023). Zephyr uses Mistral 7B as the starting point due to its strong performance. With the development of open models, researchers have worked on approaches to improve small model performance by distillation from larger models. This trend started with self-instruct method (Wang et al., 2023) and the Alpaca model (Taori et al., 2023), which was followed by Vicuna (Chiang et al., 2023)and other distilled models. These works primarily focused on distilling the SFT stage of alignment, whereas we focus on both SFT and preference optimization. Some models such as WizardLM (Xu et al.) have explored methods beyond dSFT. Contemporaneously with this work, Xwin-LM (Team, 2023) introduced an approach that distilled preference optimization through PPO (Schulman et al., 2017). We compare to these approaches in our experiments. Tools for benchmarking and evaluating LLMs have greatly evolved to keep up with the pace of innovation in generative AI. Powerful LLMs such as GPT-4 and Claude are used as evaluators to judge model responses by scoring model outputs or ranking responses in a pairwise setting. The LMSYS chatbot arena benchmarks LLMs in anonymous, randomized battles using crowdsourcing (Zheng et al., 2023). The models are ranked based on their Elo ratings on the leaderboard. AlpacaEval is an example of another such leaderboard that compares models in a pairwise setting but instead uses bigger LLMs such as GPT-4 and Claude in place of humans (Dubois et al., 2023). In a similar spirit, MTBench uses GPT-4 to score model responses on a scale of 1-10 for multi-turn instructions across task categories such as reasoning, roleplay, math, coding, writing, humanities, STEM and extraction (Zheng et al., 2023). The HuggingFace Open LLM leaderbaord (Beeching et al., 2023), the Chain-of-Thought Hub (Fu et al., 2023), ChatEval (Sedoc et al., 2019), and FastEval (fas, 2023) are examples of other tools for evaluating chatty models. We present results by evaluating on MTBench, AlpacaEval, and the HuggingFace OpenLLM Leaderboard. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Zephyr: Direct Distillation of LM Alignment: Related Work

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix 5 RESULTS AND ABLATIONS In this section we collect our main results; see Appendix A for sample model completions. dDPO Improves Chat Capabilities. In Table 1 we compare the performance of ZEPHYR-7B on the MT-Bench and AlpacaEval benchmarks. Compared to other open 7B models, ZEPHYR-7B sets a new state-of-the-art and performs significantly better than dSFT models across both benchmarks. In particular, ZEPHYR-7B outperforms XWIN-LM-7B, which is one of the few open models to be trained with distilled PPO (dPPO). When compared to larger open models, ZEPHYR-7B achieves competitive performance with LLAMA2-CHAT 70B, scoring better on MT-Bench and within two standard deviations on AlpacaEval. However, ZEPHYR-7B performs worse than WIZARDLM-70B and XWIN-LM-70B, which suggests that applying dDPO to larger model sizes may be needed to match performance at these scales. When compared to proprietary models, ZEPHYR-7B is competitive with GPT-3.5-TURBO and CLAUDE 2 on AlpacaEval, however these results should be interpreted with care since the prompts in AlpacaEval may not be representative of real-usage and advanced applications. This is partly visible in Figure 1, which shows the breakdown of model performance on MT-Bench across each domain. We can see that although ZEPHYR-7B is competitive with proprietary models on several categories, is much worse in math and coding. dDPO Improves Academic Task Performance Table 2 shows the main chat results comparing the performance of the proposed model with a variety of other closed source and open-source LLMs. Results show that the dDPO model performs the best among all 7B models, with a large gap over the best dSFT models as well as Xwin-LM dPPO model. Model scale does matter more for these results and the larger models perform better than Zephyr on some of the knowledge intensive tasks. However, Zephyr does reach the performance of the 40B scale models. Is Preference Optimization Necessary? In Table 3 we examine the impact from different steps of the alignment process by fine-tuning Mistral 7B in four different ways: • dDPO - dSFT fine-tunes the base model directly with DPO for one epoch on UltraFeedback. • dSFT-1 fine-tunes the base model with SFT for one epoch on UltraChat • dSFT-2 applies dSFT-1 first, followed by one more epoch of SFT on the top-ranked completions of UltraFeedback. • dDPO + dSFT applies dSFT-1 first, followed by one epoch of DPO on UltraFeedback. First, we replicate past results (Ouyang et al., 2022) and show that without an initial SFT step (- dSFT), models are not able to learn at all from feedback and perform terribly. Using dSFT improves model score significantly on both chat benchmarks. We also consider running dSFT directly on the feedback data by training on the most preferred output (dSFT-2); however we find that this does not make an impact in performance. Finally, we see that the full Zephyr models (dDPO+dDSFT) gives a large increase in both benchmarks. Does Overfitting Harm Downstream Performance? In the process of training ZEPHYR-7B we observed that after one epoch of DPO training, the model would strongly overfit, as indicated by perfect training set accuracies in Figure 3. Surprisingly, this did not harm downstream performance on MT-Bench and AlpacaEval; as shown in Figure 3, the strongest model was obtained with one epoch of SFT followed by three epochs of DPO. However, we do observe that if the SFT model is trained for more than one epoch, the DPO step actually induces a performance regression with longer training. This paper is available on arxiv under CC 4.0 license. Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix 5 RESULTS AND ABLATIONS In this section we collect our main results; see Appendix A for sample model completions. dDPO Improves Chat Capabilities. In Table 1 we compare the performance of ZEPHYR-7B on the MT-Bench and AlpacaEval benchmarks. Compared to other open 7B models, ZEPHYR-7B sets a new state-of-the-art and performs significantly better than dSFT models across both benchmarks. In particular, ZEPHYR-7B outperforms XWIN-LM-7B, which is one of the few open models to be trained with distilled PPO (dPPO). When compared to larger open models, ZEPHYR-7B achieves competitive performance with LLAMA2-CHAT 70B, scoring better on MT-Bench and within two standard deviations on AlpacaEval. However, ZEPHYR-7B performs worse than WIZARDLM-70B and XWIN-LM-70B, which suggests that applying dDPO to larger model sizes may be needed to match performance at these scales. When compared to proprietary models, ZEPHYR-7B is competitive with GPT-3.5-TURBO and CLAUDE 2 on AlpacaEval, however these results should be interpreted with care since the prompts in AlpacaEval may not be representative of real-usage and advanced applications. This is partly visible in Figure 1, which shows the breakdown of model performance on MT-Bench across each domain. We can see that although ZEPHYR-7B is competitive with proprietary models on several categories, is much worse in math and coding. dDPO Improves Chat Capabilities. dDPO Improves Academic Task Performance Table 2 shows the main chat results comparing the performance of the proposed model with a variety of other closed source and open-source LLMs. Results show that the dDPO model performs the best among all 7B models, with a large gap over the best dSFT models as well as Xwin-LM dPPO model. Model scale does matter more for these results and the larger models perform better than Zephyr on some of the knowledge intensive tasks. However, Zephyr does reach the performance of the 40B scale models. dDPO Improves Academic Task Performance Is Preference Optimization Necessary? In Table 3 we examine the impact from different steps of the alignment process by fine-tuning Mistral 7B in four different ways: Is Preference Optimization Necessary? • dDPO - dSFT fine-tunes the base model directly with DPO for one epoch on UltraFeedback. • dDPO - dSFT • dSFT-1 fine-tunes the base model with SFT for one epoch on UltraChat • dSFT-1 • dSFT-2 applies dSFT-1 first, followed by one more epoch of SFT on the top-ranked completions of UltraFeedback. • dSFT-2 • dDPO + dSFT applies dSFT-1 first, followed by one epoch of DPO on UltraFeedback. • dDPO + dSFT First, we replicate past results (Ouyang et al., 2022) and show that without an initial SFT step (- dSFT), models are not able to learn at all from feedback and perform terribly. Using dSFT improves model score significantly on both chat benchmarks. We also consider running dSFT directly on the feedback data by training on the most preferred output (dSFT-2); however we find that this does not make an impact in performance. Finally, we see that the full Zephyr models (dDPO+dDSFT) gives a large increase in both benchmarks. Does Overfitting Harm Downstream Performance? In the process of training ZEPHYR-7B we observed that after one epoch of DPO training, the model would strongly overfit, as indicated by perfect training set accuracies in Figure 3. Surprisingly, this did not harm downstream performance on MT-Bench and AlpacaEval; as shown in Figure 3, the strongest model was obtained with one epoch of SFT followed by three epochs of DPO. However, we do observe that if the SFT model is trained for more than one epoch, the DPO step actually induces a performance regression with longer training. Does Overfitting Harm Downstream Performance? This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Zephyr: Direct Distillation of LM Alignment: Results and Ablations

What new mathematical function can be included in the parameters of large language models that will map intent, against usefulness for harms like deepfakes? What new layer can be included in training a base model such that rather than have an immediate output that hallucinates, confabulates or discriminates, it has a correction layer that tends towards accuracy and fairness? AI alignment, safety, interpretability, and regulation are more problems of brain science than of engineering. There are several weaknesses of large language models that the human mind has easy fixes for—presenting the standard for the next iterations of LLMs to shape safety and wider deployment. The human brain does not predict. Conceptually, electrical signals in a set split, with some going ahead of others, to interact with chemical signals like before, such that if the input matches, the incoming ones follow similarly. If not, the incoming ones proceed in the right direction. This explains predictive coding, processing, and prediction error. Simply, in experiences, initial perceptions are often a result of splits, so that processes can be faster in the mind. They are not prediction of the next token, with some getting it right and others hallucinating, like in LLMs. Back propagation works in training of base models, but another version of cost function would be needed for the fine-tuned model, against hallucination, discrimination and bias, just like how the human mind corrects, almost accurately, after a missed initial perception. The human mind also mechanizes intent, where it is possible to choose to do some things or not. This is different from LLMs that do anything they can do—as prompted—before or after guardrail. They can also be attacked with prompt injections as well as jailbreaks, exposing a vulnerability. Explorations in research for explainable AI and alignment may include power series expansion and other functions for accuracy and fluidintentionality. This can also be useful to develop monitoring AIs, based on their activations, for other AIs and their outputs, in use in common areas of the internet—towards general safety, not just of individual AI models. Anthropic recently published a paper on Interpretability, Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet. How features act, as an approach to explainable AI, is vital. However, exploring new functions and layers, for parallels to the human mind, may decide more. There is a recent paper in Neural Computation, Synaptic Information Storage Capacity Measured With Information Theory, that concluded that, "Information storage and coding in neural circuits have multiple substrates over a wide range of spatial and temporal scales. How information is coded and its potential efficiency depends on how the information is stored. The efficiency of information storage was analyzed by comparing the distribution of synaptic strengths, as measured in spine head volumes, with the uniform strength distribution, the maximum entropy distribution. The outcomes reveal a near maximal efficiency in storing information across the distinguishable sizes of spine head volumes." Chemical impulses, rather than synapses, are, conceptually, the basis of information organization and storage in the brain. Synapses can be in a formation, for which the provisions of rations of chemical impulses for specificity of functions are possible, but synapses themselves do not hold information. The human mindhas functions and features. Functions include memory, feelings, emotions and regulation of internal senses. Features grade or qualify functions. They include attention, awareness [or less than attention], self or subjectivity and free will or intentionality. Functions and features are mechanized in the same sets of electrical and chemical signals. This makes it possible for functions to be properly graded. For AI, some expansion of parameters may define alignment by the neuroscience of mind. How the human mind works, conceptually, is also applicable as a display, for a form of care, for those already affected by the risks of AI like deepfake audios, job losses and others. What new mathematical function can be included in the parameters of large language models that will map intent, against usefulness for harms like deepfakes? What new layer can be included in training a base model such that rather than have an immediate output that hallucinates, confabulates or discriminates, it has a correction layer that tends towards accuracy and fairness? AI alignment, safety, interpretability, and regulation are more problems of brain science than of engineering. There are several weaknesses of large language models that the human mind has easy fixes for—presenting the standard for the next iterations of LLMs to shape safety and wider deployment. The human brain does not predict. Conceptually, electrical signals in a set split , with some going ahead of others, to interact with chemical signals like before, such that if the input matches, the incoming ones follow similarly. If not, the incoming ones proceed in the right direction. This explains predictive coding, processing, and prediction error. electrical signals in a set split Simply, in experiences, initial perceptions are often a result of splits, so that processes can be faster in the mind. They are not prediction of the next token, with some getting it right and others hallucinating, like in LLMs. Back propagation works in training of base models, but another version of cost function would be needed for the fine-tuned model, against hallucination, discrimination and bias, just like how the human mind corrects, almost accurately, after a missed initial perception. The human mind also mechanizes intent, where it is possible to choose to do some things or not. This is different from LLMs that do anything they can do—as prompted—before or after guardrail. They can also be attacked with prompt injections as well as jailbreaks, exposing a vulnerability. Explorations in research for explainable AI and alignment may include power series expansion and other functions for accuracy and fluid intentionality. This can also be useful to develop monitoring AIs, based on their activations, for other AIs and their outputs, in use in common areas of the internet—towards general safety, not just of individual AI models. fluid Anthropic recently published a paper on Interpretability, Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet . How features act, as an approach to explainable AI, is vital. However, exploring new functions and layers, for parallels to the human mind, may decide more. Anthropic Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet There is a recent paper in Neural Computation , Synaptic Information Storage Capacity Measured With Information Theory , that concluded that, "Information storage and coding in neural circuits have multiple substrates over a wide range of spatial and temporal scales. How information is coded and its potential efficiency depends on how the information is stored. The efficiency of information storage was analyzed by comparing the distribution of synaptic strengths, as measured in spine head volumes, with the uniform strength distribution, the maximum entropy distribution. The outcomes reveal a near maximal efficiency in storing information across the distinguishable sizes of spine head volumes." Neural Computation Synaptic Information Storage Capacity Measured With Information Theory Chemical impulses, rather than synapses, are, conceptually, the basis of information organization and storage in the brain. Synapses can be in a formation, for which the provisions of rations of chemical impulses for specificity of functions are possible, but synapses themselves do not hold information. The human mind has functions and features. Functions include memory, feelings, emotions and regulation of internal senses. Features grade or qualify functions. They include attention, awareness [or less than attention], self or subjectivity and free will or intentionality. The human mind Functions and features are mechanized in the same sets of electrical and chemical signals. This makes it possible for functions to be properly graded. For AI, some expansion of parameters may define alignment by the neuroscience of mind. How the human mind works, conceptually, is also applicable as a display, for a form of care, for those already affected by the risks of AI like deepfake audios, job losses and others.

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