Design a fine-tuning pipeline that adapts a foundation model to a company’s domain, including data curation, training strategy, and evaluation. A large enterprise wants to take a general-purpose LLM (e.g., Llama 3 or Mistral) and specialize it for its own support tickets, internal wikis, product manuals, and domain-specific jargon.
The pipeline must curate and clean training data from noisy heterogeneous sources, choose between full fine-tuning, LoRA/QLoRA, and instruction tuning, avoid catastrophic forgetting of general capabilities, and produce a reproducible evaluation that proves the adapted model is better on domain tasks without regressing on general benchmarks.
How would you design this pipeline? Cover the data curation and deduplication strategy, the fine-tuning approach and hyperparameter choices, the evaluation harness (domain-specific benchmarks plus general-capability guardrails), the infrastructure for reproducible runs, and how you decide when the adapted model is ready to replace the base model in production.
The Problem: the base model speaks fluent English but has never read a single one of your change requests, part numbers, or escalation runbooks, and roughly 900M tokens of company text are sitting unused.
Answer
The design is a curate, adapt, gate pipeline: a versioned data factory turns noisy internal sources into a deduplicated, PII-scrubbed corpus plus a hand-reviewed instruction set, a QLoRA adapter is trained on top of a pinned base checkpoint, and a dual-gate evaluation decides promotion. Two decisions dominate. First, adapt with parameter-efficient tuning on a small high-quality mix (domain corpus, instruction pairs, and 5 to 10 percent general replay data) rather than full fine-tuning on a raw dump, because a raw dump buys memorization and forgetting instead of capability. Second, make the promotion decision a two-sided gate: a required lift on a frozen domain benchmark and a bounded regression budget on general benchmarks, both measured by a versioned harness that any engineer can rerun from a run manifest.
(1) Data Factory Before Modeling: exact plus MinHash near-duplicate removal, PII and secret scrubbing, quality filtering, and access-control filtering, all emitting a content-hashed dataset version.
(2) Two-Stage Adaptation: short continued pretraining on the cleaned domain corpus to absorb jargon, then instruction tuning on curated ticket-resolution and doc-QA pairs to restore and shape behavior.
(3) QLoRA as the Default: 4-bit frozen base with rank-16 adapters on attention and MLP projections, so a run costs a single GPU-hour class of compute and the base weights stay bit-identical.
(4) Replay Against Forgetting: mix general instruction data into the SFT set and gate on general benchmarks, so domain gains are never paid for with instruction-following collapse.
(5) Reproducible Runs: pinned base digest, dataset hash, seed, container image, and config in one manifest; every eval number traces back to one manifest.
(6) Adapter Serving with a Kill Switch: serve base plus hot-swappable adapter behind a registry, roll out shadow → 5 percent canary → full, and roll back by unloading the adapter.
Figure 1: The loop: curated and versioned data feeds a cheap adapter run, the harness decides promotion, and production traffic becomes next month’s training data.
Clarify Before Designing:
(1) Target Tasks: which two or three tasks must improve (ticket triage, draft replies, doc QA), and who signs off on the frozen eval set for them?
(2) Corpus Reality: how many tokens survive after access-control and license filtering, and how much of the ticket volume is template boilerplate?
(3) Knowledge vs Behavior: is the gap stale facts (retrieval work) or unfamiliar jargon, format, and tone (fine-tuning work)?
(4) Regression Budget: how many points of MMLU, IFEval, or safety-refusal rate is the business willing to lose for a domain win?
(5) Serving Constraints: self-hosted weights or a closed API, one adapter for everyone or per-business-unit adapters, and what is the latency budget?
(6) Compliance: can customer PII enter training weights at all, and what is the deletion or right-to-forget story once it does?
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