Custom LLM Fine-Tuning
Custom LLM Fine-Tuning Services
Fine-tuning trains an existing language model further on your own examples so it follows your format, tone and domain language more consistently. We help you decide whether fine-tuning is needed, prepare the training data, train and evaluate the model, and deploy it.
Fine-tuning is not always the answer. If the model needs your facts, retrieval (RAG) is usually better and cheaper. Fine-tuning shines when you need a consistent style, a fixed output format or specialised classification at scale. We test both before recommending.
What we offer
- Fit assessmentClear advice on fine-tuning versus RAG versus better prompts.
- Training data preparationCleaning, labelling and formatting your examples, with sensitive data removed.
- TrainingFine-tuning hosted models or open-source models such as Llama and Mistral.
- EvaluationBefore-and-after tests on held-out examples to prove the improvement.
- DeploymentHosting, versioning and rollback for the tuned model.
Common use cases
- Customer replies in your brand voice
- Extracting fields from industry-specific documents
- Classifying tickets, leads or products into your categories
- Smaller, cheaper models that match larger ones on one task
Technology
- OpenAI fine-tuning
- Hugging Face
- PyTorch
- LoRA / QLoRA
- Llama
- Mistral
- AWS / Azure / Google Cloud GPUs
When fine-tuning makes sense
- You need a consistent style, format or tone that prompting cannot achieve reliably
- You want a smaller, cheaper model to match a larger one on a narrow task
- You have hundreds or thousands of good examples of the desired output
- You need the model to run privately on your own infrastructure
Why work with Webtech Evolution
- 10+ years of delivery272+ projects for 118+ clients in 12+ countries since 2014.
- One team, end to enddesign, front end, back end, mobile, QA and DevOps under one roof, so nothing gets lost between vendors.
- Clear estimatesa written scope, timeline and price before work starts, and demos throughout the build.
- You own everythingcode, designs and documentation are handed over in full and covered by an NDA.
- Working hours that overlap with yoursMonday to Friday, 10:00–19:00 IST, with extended hours available for clients in the USA, Canada and New Zealand.
How we deliver
Discovery
agree the goal, users, data and how success will be measured.
Prototype
a working version on real examples within the first weeks.
Build and integrate
connect to your systems, add security, testing and monitoring.
Pilot
launch to a small group, measure results and improve.
Scale and support
roll out widely, with ongoing monitoring and updates.
Need people rather than a project? Hire AI developers.
Frequently asked questions
They solve different problems. RAG gives the model up-to-date facts from your documents; fine-tuning changes how the model behaves. Many projects need only RAG; some benefit from both.
Useful results can come from a few hundred high-quality examples for narrow tasks. More varied tasks need more. Quality matters more than quantity.
With open-source models, yes, the tuned weights can be yours and hosted on your infrastructure. Hosted provider models remain on the provider's platform.
RAG gives a model up-to-date knowledge from your documents; fine-tuning changes how it behaves and writes. Most knowledge problems are solved with RAG; fine-tuning suits style, format and narrow tasks.
Often a few hundred to a few thousand high-quality examples. Quality matters more than quantity.
OpenAI models through their API, and open-source models such as Llama, Mistral and Qwen using LoRA or QLoRA.
Let's Talk!
Have a question about Custom LLM Fine-Tuning? Send us a message and our team will reply with next steps.
- Reply within a few hours (Mon–Fri)
- NDA available
- You own the code
Prefer to talk? Call +91-9601965456WhatsApp ushello@webtech-evolution.com
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