# Training - [The Fine Tuning Process](https://training.continuumlabs.ai/training/the-fine-tuning-process.md): Fine tuning deep learning models is completely different to fine tuning machine learning models - [Why fine tune?](https://training.continuumlabs.ai/training/the-fine-tuning-process/why-fine-tune.md) - [Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?](https://training.continuumlabs.ai/training/the-fine-tuning-process/why-fine-tune/does-fine-tuning-llms-on-new-knowledge-encourage-hallucinations.md) - [Explanations in Fine Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/why-fine-tune/explanations-in-fine-tuning.md) - [Tokenization](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization.md) - [Tokenization Is More Than Compression](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenization-is-more-than-compression.md): Craig W. Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine, Omri Uzan, Yuval Pinter, Chris Tanner - [Tokenization - SentencePiece](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenization-sentencepiece.md): The Unsupervised Text Tokenizer for Neural Networks - [Tokenization explore](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenization-explore.md) - [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenizer-choice-for-llm-training-negligible-or-crucial.md) - [Getting the most out of your tokenizer for pre-training and domain adaptation](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/getting-the-most-out-of-your-tokenizer-for-pre-training-and-domain-adaptation.md) - [TokenMonster](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenmonster.md) - [Parameter Efficient Fine Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning.md) - [P-Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/p-tuning.md): The highly cited "GPT Understands Too" paper first submitted March 2021, introducing P-Tuning - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/p-tuning/the-power-of-scale-for-parameter-efficient-prompt-tuning.md) - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/prefix-tuning-optimizing-continuous-prompts-for-generation.md) - [Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/harnessing-the-power-of-peft-a-smarter-approach-to-fine-tuning-pre-trained-models.md): Parameter-Efficient Fine-Tuning (PEFT) is a technique used to fine tune neural language models - [What is Low-Rank Adaptation (LoRA) - explained by the inventor](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/what-is-low-rank-adaptation-lora-explained-by-the-inventor.md): Edward Hu - [Low Rank Adaptation (Lora)](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/low-rank-adaptation-lora.md) - [Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/practical-tips-for-fine-tuning-lms-using-lora-low-rank-adaptation.md) - [QLORA: Efficient Finetuning of Quantized LLMs](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/qlora-efficient-finetuning-of-quantized-llms.md) - [Bits and Bytes](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/bits-and-bytes.md): Tim Dettmers (PhD candidate, University of Washington) presents "8-bit Methods for Efficient Deep Learning" in this Cohere For AI Technical Talk. - [The Magic behind Qlora](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/the-magic-behind-qlora.md) - [Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/practical-guide-to-lora-tips-and-tricks-for-effective-model-adaptation.md): A range of practical tips and questions around using Lora - [The quantization constant](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/the-quantization-constant.md) - [QLORA: Efficient Finetuning of Quantized Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/qlora-efficient-finetuning-of-quantized-language-models.md) - [QLORA and Fine-Tuning of Quantized Language Models (LMs)](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/qlora-and-fine-tuning-of-quantized-language-models-lms.md) - [ReLoRA: High-Rank Training Through Low-Rank Updates](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/relora-high-rank-training-through-low-rank-updates.md) - [SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/slora-federated-parameter-efficient-fine-tuning-of-language-models.md): Leveraging Lora - [GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/galora-memory-efficient-llm-training-by-gradient-low-rank-projection.md) - [Hyperparameters](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters.md): Art and science - [Batch Size](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/batch-size.md): Choosing the right batch size is critical - [Padding Tokens](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/padding-tokens.md) - [Mixed precision training](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/mixed-precision-training.md) - [FP8 Formats for Deep Learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/fp8-formats-for-deep-learning.md) - [Floating Point Numbers](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/floating-point-numbers.md) - [Batch Size and Model loss](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/batch-size-and-model-loss.md) - [Batch Normalisation](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/batch-normalisation.md) - [Rethinking Learning Rate Tuning in the Era of Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/rethinking-learning-rate-tuning-in-the-era-of-language-models.md): One of the most important hyperparameters - [Sample Packing](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/sample-packing.md) - [Gradient accumulation](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/gradient-accumulation.md) - [A process for choosing the learning rate](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/a-process-for-choosing-the-learning-rate.md) - [Learning Rate Scheduler](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/learning-rate-scheduler.md): Key Considerations with Learning Rate Scheduling in Neural Network Training - [Checkpoints](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/checkpoints.md) - [A Survey on Efficient Training of Transformers](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/a-survey-on-efficient-training-of-transformers.md) - [Sequence Length Warmup](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/sequence-length-warmup.md) - [Understanding Training vs. Evaluation Data Splits](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/understanding-training-vs.-evaluation-data-splits.md) - [Cross-entropy loss](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/cross-entropy-loss.md) - [Weight Decay](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/weight-decay.md) - [Optimiser](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/optimiser.md) - [Caching](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/caching.md) - [Training Processes](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes.md) - [Extending the context window](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/extending-the-context-window.md) - [PyTorch Fully Sharded Data Parallel (FSDP)](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/pytorch-fully-sharded-data-parallel-fsdp.md) - [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/train-short-test-long-attention-with-linear-biases-enables-input-length-extrapolation.md): Ofir Press, Noah A. Smith, Mike Lewis - [YaRN: Efficient Context Window Extension of Large Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/yarn-efficient-context-window-extension-of-large-language-models.md): Nous Research, EleutherAI, University of Geneva - [Sliding Window Attention](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/sliding-window-attention.md): Iz Beltagy, Matthew E. Peters, and Arman Cohan - [LongRoPE](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/longrope.md) - [Reinforcement Learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/reinforcement-learning.md) - [An introduction to reinforcement learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/an-introduction-to-reinforcement-learning.md) - [Reinforcement Learning from Human Feedback (RLHF)](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/reinforcement-learning-from-human-feedback-rlhf.md): Most often useful when creating domain specific models - [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/direct-preference-optimization-your-language-model-is-secretly-a-reward-model.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://training.continuumlabs.ai/training.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.