Adapter Fine-Tuning Resources

Knowledge

The three methods

• Paper: "LoRA: Low-Rank Adaptation of Large Language Models", Hu et al., arXiv:2106.09685
  The original low-rank adapter method and the intrinsic-dimensionality argument for why it works. Use for: stage 3, and for what rank and alpha actually control.

• Paper: "QLoRA: Efficient Finetuning of Quantized LLMs", Dettmers et al., arXiv:2305.14314
  NF4, double quantisation and paged optimizers, with the memory accounting spelled out. Use for: stage 4, and as the reference for what a quantized base costs in quality. Read the limitations section.

• Paper: "DoRA: Weight-Decomposed Low-Rank Adaptation", Liu et al., arXiv:2402.09353
  Splits weights into magnitude and direction and adapts the direction. Use for: stage 5. Section 4's analysis of how full fine-tuning and LoRA move each quantity is the actual argument.

• Paper: "Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning", Aghajanyan et al., arXiv:2012.13255
  Why adaptation can live in a low-dimensional subspace at all. Use for: the justification underneath LoRA rather than the mechanism.

Current practice

• Docs: "LoRA Without Regret", Hugging Face TRL
  The case that all-linear targets with adequate rank close the gap to full fine-tuning. Use for: target-module and rank choice, and as the baseline any new variant must beat. This supersedes the attention-only convention inherited from the original paper.

• Blog: "Practical Tips for Finetuning LLMs Using LoRA", Sebastian Raschka, Ahead of AI
  Ablations over rank, alpha and target modules, with the experiments shown. Use for: sanity-checking hyperparameter folklore against measurements.

• Docs: PEFT, Hugging Face
  Reference implementation of all three methods and the configuration surface they expose. Use for: what a hyperparameter is called in practice, and which variants are established enough to be a flag. Version-sensitive, so check against the installed release rather than from memory.

• Docs: SFTTrainer, Hugging Face TRL
  The standard supervised fine-tuning loop and its dataset formats. Use for: stage 3 onward. Version-sensitive; parameter names have moved between releases.

Foundations

• Interactive: LLM Visualization, Brendan Bycroft
  Click through a single token's path with per-matrix parameter counts. Use for: stage 1, and the best available answer to "where do the parameters live".

• Blog: "The Illustrated Transformer", Jay Alammar
  The standard visual explanation of the architecture. Use for: stage 1 when the block structure has not landed yet.

• Docs: Chat Templates, Hugging Face Transformers
  How conversations are rendered to token sequences, and how to apply the model's own template. Use for: the single most common silent failure in fine-tuning.

• Paper: "Decoupled Weight Decay Regularization" (AdamW), Loshchilov & Hutter, arXiv:1711.05101
  The optimizer everything here uses, and why its weight decay differs from Adam's. Use for: stage 2's memory argument.

• Paper: "ZeRO: Memory Optimizations Toward Training Trillion Parameter Models", Rajbhandari et al., arXiv:1910.02054
  The canonical accounting of weights, gradients and optimizer state as separate memory categories. Use for: deriving the sixteen-bytes-per-parameter figure.

• Paper: "Training Deep Nets with Sublinear Memory Cost", Chen et al., arXiv:1604.06174
  Gradient checkpointing, and the compute-for-memory trade it makes. Use for: stage 2, and for the setting you will leave on permanently.

• Blog: "A Recipe for Training Neural Networks", Andrej Karpathy
  How to run experiments so their results mean something. Use for: stage 3's diagnosis lesson, and the overfit-two-examples smoke test.

Quantisation

• Paper: "LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale", Dettmers et al., arXiv:2208.07339
  The outlier-feature problem that makes transformer quantisation harder than the arithmetic suggests. Use for: why blockwise beats per-tensor.

• Docs: bitsandbytes quantization, Hugging Face Transformers
  The 4-bit configuration surface in practice. Use for: stage 4 setup.

• Code: bitsandbytes, bitsandbytes-foundation
  The backends/ directory is the authority on which hardware supports which operation. Use for: settling a hardware-support question against the source rather than a tutorial, which in this area is usually stale.

Data and evaluation

• Paper: "LIMA: Less Is More for Alignment", Zhou et al., arXiv:2305.11206
  Evidence that a small carefully curated dataset outperforms a large careless one, and that SFT largely teaches format. Use for: stage 6's opening argument.

• Paper: "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena", Zheng et al., arXiv:2306.05685
  Model-as-judge evaluation with its biases characterised: position, verbosity, self-preference. Use for: free-form generation metrics, and for why the judge needs calibrating against human labels.

• Paper: "An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning", Luo et al., arXiv:2308.08747
  That forgetting is measurable, common, and not confined to edge cases. Use for: justifying the regression suite to someone who thinks it is overhead.

• Paper: "Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!", Qi et al., arXiv:2310.03693
  Safety behaviour degrading from fine-tuning on benign data. Use for: why the regression suite must include refusal cases.

• Code: lm-evaluation-harness, EleutherAI
  Standard benchmark implementations and the contamination problems they document. Use for: building an eval, and for reading published scores sceptically.

Serving

• Docs: LoRA adapters, vLLM
  Serving adapters in production, including multi-adapter routing. Use for: stage 7. Check maximum-rank and maximum-adapter limits against the installed version.

• Paper: "S-LoRA: Serving Thousands of Concurrent LoRA Adapters", Sheng et al., arXiv:2311.03285
  How many adapters get batched against one shared base efficiently. Use for: understanding why multi-adapter serving is a solved problem rather than a research idea.

• Blog: "LLM Inference Performance Engineering: Best Practices", Databricks
  Prefill versus decode, and what each phase is bound by. Use for: stage 7's cost arithmetic.

The alternative

• Paper: "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", Lewis et al., arXiv:2005.11401
  The canonical statement of the approach that should usually be preferred when the problem is knowledge. Use for: the final lesson's argument, and for making the case against fine-tuning credibly.

Wisdom (Communities)

• r/LocalLLaMA
  The largest concentration of people fine-tuning on consumer hardware, across every backend. Use for: searching what already worked within a memory budget, which folklore is worth testing, and replication reports on new methods. Read the archive; posting is a rep, not a source.

• Hugging Face Forums
  Searchable and archived, with library authors present. Use for: PEFT, TRL and transformers behaviour that the docs leave ambiguous.

• EleutherAI Blog
  Write-ups from a group that reads its own field critically, including negative results. Use for: stage 5 and 7 judgment calls on whether a claimed result generalises. Their Discord is where this gets discussed first, and is deliberately not listed: nothing said there is retrievable later.

• MLX Discussions, ml-explore/mlx
  Public, searchable, and answered by the maintainers. Use for: reading whether a given operation is supported on Apple Silicon yet, usually already asked.

Gaps

• No source tracks which torch, PEFT, TRL and quantisation-library versions actually work together on a given Python version and platform. This blocks a first run more often than any concept does, and each environment has to be verified against release notes rather than assumed.
• Hardware backend coverage for 4-bit operations changes release to release. No document is reliable here; the library source is the only authority, and any tutorial's claim about hardware requirements should be treated as expired.
• No trusted source yet for evaluation design specific to small-model task adaptation. This is the weakest link in stage 6 and the hardest part of the mission. The general evaluation literature is aimed at benchmarking foundation models, not at proving a narrow adapter helped.
• Most published LoRA hyperparameter advice targets 7B models and above. Little addresses whether it transfers to the 1B to 3B class, which is where most people actually start.
• No independent replication surveyed for DoRA at the scale LoRA and QLoRA enjoy. Treat its reported gains as needing local measurement.
• No source chosen for constrained decoding, which Lesson 27 recommends as the correct answer to schema-validity problems. It is named without a reference behind it.