AI solutions
LLM Fine-Tuning
Teach a general model your business first. When you already have a good open-source or commercial base model and only need it to match your scripts, wording and task formats, fine-tuning is the lowest-cost, fastest path. The data factory makes sure it learns from the right data.
Five steps from raw data to a usable model
Turn enterprise data into training assets
Data usually sets the ceiling for fine-tuning results. The data factory turns documents, conversations and tickets into traceable, reproducible training and evaluation sets.
Multi-source collection and import
Pulls in documents, spreadsheets, chat logs, tickets and knowledge base exports into one traceable corpus repository.
- JSONL / CSV / Markdown / PDF / Word
- Batch import of support chats, tickets, FAQs and policy documents
- Traceable source tags and collection batches
Cleaning, deduplication and quality scoring
Automatically filters out dirty data, near-duplicates and low-information samples, and grades quality to decide what enters the training set.
- Format checks; garbled and empty samples removed
- Semantic near-duplicate detection and sample review
- Quality tiers: training / evaluation / needs human review
Instruction and preference data
Builds SFT instruction sets for each task and, where needed, preference pairs (chosen / rejected) to support alignment and a consistent style.
- Q&A pairs / instruction–output / multi-turn dialogue samples
- Task templates for classification, extraction, rewriting, summarization and more
- Labeling guidelines and spot-check workflow
Masking, permissions and versioning
Masking of sensitive data, partitioned permissions and locked dataset versions keep work compliant and reproducible.
- Masking rules for phone numbers, ID numbers, customer identifiers and more
- Training / evaluation sets kept separate to prevent leakage
- Data versions and lineage linked to experiment records
Choose the method by goal, not by parameter count
We recommend lightweight fine-tuning for fast validation by default. For critical tasks with very high quality demands, we then assess full fine-tuning or a combination with agents / RAG.
LoRA fine-tuning
Trains only low-rank adapters, with low VRAM use and fast training. Suits most cases of matching business scripts and tasks.
- Low VRAM
- Fast iteration
- Hot-swappable adapters
QLoRA quantized fine-tuning
4-bit quantization + LoRA lets you fine-tune larger base models on smaller GPUs, cutting pilot costs further.
- 4-bit quantization
- Large models on small GPUs
- Controlled costs
Full-parameter / in-depth optimization
When domain terminology or complex generation quality is critical, upgrade to full-parameter fine-tuning or a staged training strategy.
- Quality first
- Needs more compute
- Expert support throughout
Training visibility and control
Recommended parameter templates plus advanced customization. Watch loss, evaluation metrics and resource use in real time, and stop early if something goes wrong.
- Configurable learning rate, batch size, epochs and early stopping
- Automatic checkpoints and resume from checkpoint
- Exportable training logs for review
Comparison and release
Compare base and fine-tuned models on the same prompts. Once approved, release as an API or private inference endpoint, with version rollback.
- Two-track acceptance: automated metrics + human spot checks
- Multiple versions side by side, staged switchover
- Connects to agents, assistants and business systems
Training compute
Rent training GPUs on demand in a HyperSource facility, or fine-tune on an on-premises AI Compute Appliance so data stays in your environment.
- Billed by actual GPU hours
- High-power cabinets in Guiyang for training clusters
- On-premises training and inference on the appliance
High-volume tasks that suit fine-tuning first
Prioritize scenarios with clear standards, samples you can accumulate and measurable results. A pilot can usually be completed in 2–6 weeks.
Customer service and knowledge Q&A
Lock in product messaging, after-sales scripts and policy FAQs to reduce hallucinations and inconsistent answers.
Official documents, reports and contracts
Draft, rewrite, polish and extract key points using your templates, with a consistent tone and format.
Classification, extraction and review
Ticket classification, field extraction, compliance review and risk tagging that plug into existing workflows.
Industry-specific models
Farming advice, medical triage, education Q&A and tourism narration, quickly adapted to each field's terms and scenarios.
Four steps to a customized model
End to end from assessment to launch. Or use only the data factory or only training compute, delivered in stages.
- 01
Scenario and data assessment
Define the task type, success metrics, base model and available data sources, and set masking and compliance boundaries.
- 02
Data factory processing
Clean and label data, build instruction / preference sets, freeze a trainable version and set aside the evaluation set.
- 03
Fine-tune and evaluate
Run training and compare against the base model with automated metrics + human spot checks. If it falls short, iterate on data or parameters.
- 04
Launch and keep improving
Release an API / private endpoint. Hard cases and production logs flow back to the data factory for the next version.
Deliverables
Data deliverables
Versioned training and evaluation sets, labeling guidelines, masking notes, quality reports and data lineage records.
Model deliverables
Fine-tuned weights / LoRA adapters, training configuration, evaluation report, comparison samples and rollback instructions.
Launch deliverables
Inference API or private endpoint, sample calls, monitoring and alerting recommendations, and a suggested iteration schedule.
Fine-tuning, agents or pre-training?
Most teams start with fine-tuning to prove value, add agents when tools are needed to get work done, and consider pre-training only when they need their own base model for the long term.
| Dimension | LLM Fine-Tuning | Custom AI Agents | LLM development (pre-training) |
|---|---|---|---|
| Core goal | Make the model fluent in your scripts, wording and task formats | Let the system look up knowledge, call tools and follow workflows | Build your own industry base model and long-term model assets |
| Data volume | Moderate volume of high-quality instruction / dialogue data | Mainly knowledge base + tool APIs | Large-scale pre-training corpus |
| Timeline and cost | Relatively low; suits fast pilots | Moderate; depends on integration depth | High; requires compute and engineering investment |
| Typical starting point | Support messaging, official documents, classification and extraction | Support ticket handling, internal assistants, business transactions | Industry base models, continued pre-training |
Fine-tuning gets the model to say the right thing; agents get the work done. You can fine-tune first, then add tools and workflows. Pre-training needs ample data and compute budget, and is assessed project by project.
Delivered with HyperSource products
GPU Compute Rental · GPU servers
Rent training GPUs on demand with fine-tuning job orchestration, billed by actual GPU hours.
Explore GPU rentalAI Compute Appliance
Fine-tune and run inference on-premises. Models and data never leave your premises.
Explore the applianceAI Agents
Fine-tuned models plug directly into agents as the inference and operations layer for your business.
See AI agentsTokenHub – Unified LLM Gateway
Call fine-tuned and public LLMs through one OpenAI-compatible interface, making staged rollouts and rollbacks easy.
Explore TokenHubFAQ
When should we fine-tune, and when is a knowledge base enough?
If your content changes often and you only need to find and cite it, use a knowledge base (RAG). Fine-tune only when you need to lock in a writing style, output format or task standard. The two can be combined.
How much data do we need?
It depends on the task. Matching your scripts usually takes a moderate volume of high-quality instruction or dialogue samples; classification and extraction depend more on coverage. The data factory stage assesses available data before setting the training scope.
What if results get worse after fine-tuning?
All versions are kept, so you can roll back to the base model or the previous version at any time. Acceptance uses automated metrics plus human spot checks, and nothing ships until it passes.
Can training be done in-house?
Yes. You can rent training GPUs by the hour in a HyperSource facility, or fine-tune and run inference on an on-premises AI Compute Appliance, so data stays in your environment.
Other AI solutions
Start with a dataset you can evaluate
Tell us the target task, the form of your existing data and the metrics you expect. We will recommend a base model, data factory scope, fine-tuning approach and pilot acceptance criteria.
- leo@hs.com.cn
- 24/7 technical support