LLM Fine Tuning
Custom-tuned models for teams whose prompts have hit a ceiling.
We adapt open and hosted models with LoRA, QLoRA, DPO and RAFT so they hold your format, tone and task behavior in production, without a GPU cluster or a million-example dataset. And when fine-tuning isn't the right tool, we'll say so before you spend.
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Creators of RustyRAG
Realtime RAG, built in Rust · Sub-200ms end-to-end
Fine-tuning services for teams whose prompts hit a ceiling
Your prompts got you to eighty percent. The last stretch (a format that never drifts, a tone that holds under weird inputs, a task the base model half-understands) is exactly what prompt engineering alone can't lock in. That's the problem fine-tuning solves: it moves your requirements out of the prompt and into the model's weights.
AlphaCorp AI designs, trains and ships fine-tuned large language models for engineering teams. We work across Hugging Face PEFT and TRL, Google Vertex AI, Microsoft Foundry and Together AI, and we build hybrid retrieval-plus-tuning systems when one method alone falls short. Stanford HAI's 2026 AI Index reports that 88 percent of organizations now use AI; the teams pulling ahead are the ones whose models behave like theirs, not like everyone's.
What our LLM fine tuning work actually builds
Six ways we adapt a base model to your task, each shipping a concrete artifact you own. Pick the one that matches your problem, or let us compose them into a pipeline.
Supervised fine-tuning (SFT)
We train the model on curated examples of your task until the desired output style is the default, not a lucky sample. Checkpointing and holdout evals guard against overfitting.
Outputtuned checkpoint
Parameter-efficient fine-tuning (LoRA, QLoRA, DoRA)
Instead of retraining billions of weights, we inject small trainable adapters. The original LoRA paper (Microsoft, 2021) cut trainable parameters by up to roughly 10,000x with no added inference latency once merged. Cheaper to train, cheaper to store, easier to swap.
basis · LoRA — Microsoft, 2021
Outputadapter file
Quantized training on modest hardware
Using the QLoRA method (University of Washington, 2023), a 65B-parameter model can be fine-tuned on a single 48GB GPU while preserving full 16-bit task performance. Your budget goes to data quality, not compute rental.
basis · QLoRA — Univ. of Washington, 2023
Outputefficient run
Preference alignment (DPO, ORPO)
When “correct” isn't enough and outputs must match human judgment, we apply Direct Preference Optimization: a stable, lightweight replacement for the fragile RLHF pipeline, or ORPO to fold alignment into a single training step.
Outputaligned model
Hybrid RAG plus fine-tuning (RAFT)
For domain-specific retrieval, we combine tuning with RAG Development using UC Berkeley's RAFT approach, which trains the model to ignore distractor documents that pollute answers.
basis · RAFT — UC Berkeley, 2024
Outputhybrid pipeline
Dataset construction and evaluation
Small and clean beats big and noisy. Meta's LIMA study tuned a 65B model on just 1,000 carefully chosen examples and matched or beat GPT-4 responses in 43 percent of cases. We build the evaluation suite before we build the dataset.
basis · LIMA — Meta, 2023
Outputdataset + evals
Not sure which method fits? We scope it against your data, budget, and latency targets. We build only what moves your metric.
Book a scoping call →How we run an LLM fine-tuning engagement
Five stages, one owner per stage, and a go / no-go checkpoint before anything ships. Structured, not ad hoc.
- 01
Scope
We test whether fine-tuning is even the right lever, benchmarking prompt and retrieval baselines first so you never pay for training you didn't need.
- 02
Curate
We build the training set with proven prompts baked into every example and multi-turn data handled correctly, following the practices OpenAI documents for supervised fine-tuning.
- 03
Train
SFT, PEFT or preference alignment, on your infrastructure or ours, with checkpoints at every stage.
- 04
Evaluate
Held-out task evals plus regression checks for forgetting and safety drift, before anything ships.
- 05
Operate
Handover to a retraining loop through our MLOps practice, because base models keep moving and yesterday's checkpoint goes stale.
Why teams pick AlphaCorp AI for fine-tuning
We'll argue against fine-tuning when the evidence does.
A peer-reviewed EMNLP 2024 study (Ovadia et al.) found that for injecting new factual knowledge, retrieval consistently outperformed unsupervised fine-tuning, and that models struggle to absorb new facts through weight updates alone. Fine-tuning wins at behavior, format, tone and task specialization. We scope against that split honestly. Vendors who pitch fine-tuning as the answer to everything are selling you their invoice.
| Approach | Best at | Backed by |
|---|---|---|
| Prompt engineering | First, cheapest lever for output quality | OpenAI's model optimization guidance |
| RAG | New or fast-changing factual knowledge | Ovadia et al., EMNLP 2024 |
| Fine-tuning | Behavior, format, tone, task specialization | Ovadia et al.; InstructGPT, 2022 |
| RAFT hybrid | Domain-specific retrieval at scale | UC Berkeley, 2024 |
Safety doesn't quietly erode on our watch.
Princeton and Virginia Tech researchers showed (ICLR 2024) that tuning on even a small number of examples can degrade a model's safety alignment, often unintentionally. Our evals test for exactly this before release.
We plan for forgetting.
Catastrophic forgetting shows up across models from 1B to 7B parameters (Luo et al., 2023) and can worsen with scale. Data mixing and parameter-efficient methods are how we contain it, and they're in the plan from day one, not bolted on after a regression.
Platform churn is our problem, not yours.
Here's a friction point only teams doing this weekly notice: OpenAI's legacy fine-tuning platform is no longer accessible to new users as of 2026, Azure's tuning docs now live under the Microsoft Foundry brand, and Google's Vertex AI documentation is mid-migration. We track these moves so your training pipeline doesn't break against a renamed endpoint.
Models, frameworks and platforms we train on
We fine-tune where your stack already lives — open-source tooling or managed cloud platforms, whichever fits your infrastructure and compliance needs.
Hugging Face
PEFT, TRL and AutoTrain for open-weight model fine-tuning.
Google Vertex AI
Supervised tuning for Gemini on Google Cloud.
Microsoft Foundry
Managed fine-tuning inside the Azure ecosystem.
Together AI
Fast fine-tuning and serving for open-source LLMs.
Training frameworks
- Hugging Face PEFT
- LoRA
- QLoRA
- Prompt & prefix tuning
- TRL — SFT
- DPO
- PPO
- ORPO trainers
- AutoTrain
Platforms
- Google Vertex AI — Gemini tuning
- Microsoft Foundry fine-tuning
- Together AI fine-tuning
Methods
- SFT
- LoRA
- QLoRA
- DoRA
- LoRA+
- Prefix tuning
- Prompt tuning
- DPO
- ORPO
- RAFT
Questions buyers ask before a fine-tuning project
What is LLM fine tuning?
Fine-tuning continues training a pretrained large language model on your own examples, so your task, format and tone live in the model's weights rather than in a fragile prompt. It ranges from full supervised fine-tuning to parameter-efficient methods like LoRA that train only a small fraction of parameters.
Should we fine-tune or use RAG?
Fine-tune for behavior, format and task specialization; use retrieval for facts that change. The EMNLP 2024 comparison by Ovadia et al. found RAG consistently beat unsupervised fine-tuning at injecting knowledge, old and new. The methods also combine: RAFT trains a model to use retrieved documents better.
How much training data do we need?
Often far less than you expect, if it's curated well. LIMA (Meta AI, 2023) fine-tuned a 65B model on 1,000 hand-picked examples and its answers were judged equal to or better than GPT-4's in 43 percent of cases. Quality of examples beats quantity, which is why dataset construction is a first-class step in our process.
Do we need a GPU cluster?
No. QLoRA showed a 65B model fine-tuning on a single 48GB GPU with full 16-bit task performance preserved; its Guanaco models reached 99.3 percent of ChatGPT's score on the Vicuna benchmark after 24 hours on one GPU. Most projects we scope run on one or a few GPUs.
Can a smaller fine-tuned model beat a bigger one?
Yes, and it's the core ROI case. OpenAI's 2022 InstructGPT work found outputs from a tuned 1.3B model were preferred over the 175B GPT-3, despite 100x fewer parameters, and the tuned 175B version won head-to-head 85 percent of the time. Smaller tuned models also cut latency and inference cost.
What are the main risks?
Three failure modes dominate: catastrophic forgetting of general skills, erosion of safety alignment, and overfitting to the training set. Each has published evidence behind it (Luo et al. 2023; Qi et al., ICLR 2024) and each has a known mitigation: data mixing, safety regression evals, and checkpointed holdout testing. That's the work you're paying a specialist for.
Ready to see if fine-tuning fits?
One free call with our engineers. We'll benchmark your prompt baseline, tell you whether tuning, retrieval or a hybrid earns its cost, and map the dataset you'd need, or point you to a cheaper fix through an AI consultation instead.
No pitch theater. Just a scoped answer.