Llama 2: Open Foundation and Fine-Tuned Chat Models

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

Think you have SolvedQuestion Answering? Try…Think you have Solved Question Answering? Try ARC, the AI2 Reasoning ChallengeScaling Laws for NeuralLanguage ModelsScaling Laws for Neural Language ModelsTraining Verifiers toSolve Math Word ProblemsTraining Verifiers to Solve Math Word ProblemsMeasuring MassiveMultitask Language…Measuring Massive Multitask Language UnderstandingOPT: Open Pre-trainedTransformer Language…OPT: Open Pre-trained Transformer Language ModelsScalingInstruction-Finetuned…Scaling Instruction-Finetuned Language ModelsLLaMA: Open andEfficient Foundation…LLaMA: Open and Efficient Foundation Language ModelsGPT-4 Technical ReportGPT-4 Technical ReportThe Flan Collection:Designing Data and…The Flan Collection: Designing Data and Methods for Effective Instruction TuningLIMA: Less Is More forAlignmentLIMA: Less Is More for AlignmentHelping Cancer Patientsto Choose the Best…Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment OptionsAGIEval: A Human-CentricBenchmark for Evaluatin…AGIEval: A Human-Centric Benchmark for Evaluating Foundation ModelsMindLLM: Pre-trainingLightweight Large…MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain ApplicationsPlan, Verify and Switch:Integrated Reasoning…Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-ThoughtsSelf-DistillationBridges Distribution Ga…Self-Distillation Bridges Distribution Gap in Language Model Fine-TuningInvestigatingRegularization of…Investigating Regularization of Self-Play Language ModelsToolLLM: FacilitatingLarge Language Models t…ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIsAutoDetect: Towards aUnified Framework for…AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language ModelsELLA: Equip DiffusionModels with LLM for…ELLA: Equip Diffusion Models with LLM for Enhanced Semantic AlignmentChain of ThoughtEmpowers Transformers t…Chain of Thought Empowers Transformers to Solve Inherently Serial ProblemsContrastive PreferenceOptimization: Pushing…Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine TranslationMono-InternVL: Pushingthe Boundaries of…Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-trainingSafeChain: Safety ofLanguage Models with…SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning CapabilitiesTULIP: Token-lengthUpgraded CLIPTULIP: Token-length Upgraded CLIPLlama 2: Open Foundationand Fine-Tuned Chat…Llama 2: Open Foundation and Fine-Tuned Chat ModelsEarlier referencesFocus paperCiting papersOlderNewer

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