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Model Fine-Tuning Services
General AI models are made for many tasks. They can do many things, but they are not trained for your exact business work. Fine tuning helps a model do your task much better. It trains an already trained language model on your own data, terms, workflow, and style. This helps the model understand your business better than prompts alone.
Ahex Technologies offers complete model fine-tuning services for businesses that need AI to give accurate results, steady performance, and a better fit for real business use.
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WHAT IS MODEL FINE-TUNING?
What Is Model Fine-Tuning?
Model fine-tuning means taking an already trained large language model, also called an LLM, and training it more on a dataset made for your task, domain, or business use. The model keeps its general language ability, but it also learns the terms, knowledge, and goals that matter to your business.
Some common examples are LLaMA 3, Mistral, GPT-3.5, and Falcon.
Fine-tuning is not the same as prompt engineering, where the model is used without changing it. It is also not the same as training a model from the beginning. Fine-tuning is a practical middle option. It uses the model’s existing language knowledge and adds the business knowledge your use case needs.
Full fine-tuning updates all model weights by using your dataset. It gives a deeper level of change, but it needs more computing power. It is often used for smaller models or by companies that have strong GPU systems.
Parameter-Efficient Fine-Tuning (PEFT), mainly LoRA and QLoRA, updates only a very small part of the model. This lowers memory use and compute cost, while still giving strong results for many tasks.
FINE-TUNING VS PROMPT ENGINEERING VS RAG
Fine-Tuning vs Prompt Engineering vs RAG: When to Use Each
One common question in discovery calls is this: do you need fine-tuning, RAG, or better prompt engineering? The answer depends on your exact need.
In real use, many production AI systems use both fine-tuning and RAG. Fine-tuning helps the model understand the domain, language, and task style. RAG brings in fresh or private data when needed.
WHY FINE-TUNE YOUR AI MODEL?
Make The Right Call
| Approach | What It Does | Best When | Limitations |
|---|---|---|---|
| Prompt Engineering | Guides the model by using instructions in the prompt | Good for fast testing, general tasks, and cases where the model already knows the task | It may fail in hard cases, has token limits, and does not change the model’s knowledge |
| RAG (Retrieval-Augmented Generation) | Pulls useful documents at runtime and sends them to the model as context | Good for private data, live data, large knowledge bases, and fact-based answers | Search quality can change, it can make answers slower, and setup is more complex |
| Model Fine-Tuning | Adds new behaviour and task knowledge into the model itself | Good for fixed tone, fixed output style, domain work, faster answers, and shorter prompts | Needs good training data, costs more to start, and may need updates when data changes |
| Fine-Tuning + RAG | Uses a fine-tuned model with a retrieval layer for live or private data | Good for business AI that needs both deep task knowledge and up-to-date information | This is the most complex setup and can cost more, but it can also give the best results |
Why Fine-Tune Your AI Model?
Our Fine-Tune Services
There are six cases where fine tuning can work much better than using a general model with prompts only
Proprietary Domain Knowledge
Your business may work in a special field such as legal, medical, finance, manufacturing, or niche SaaS. These fields use special terms, logic, and workflows that general models often do not understand well. Fine-tuning helps the model learn your domain language and work style better.
Consistent Output Format & Tone
You may need the model to always answer in a fixed format, such as JSON, medical reports, legal drafts, or brand-based customer messages. Prompting may work at first, but it often becomes less stable at scale. Fine-tuning helps keep the output more steady.
Faster Inference at Lower Cost
A fine-tuned smaller model can often do a specific task as well as a much larger general model. This can reduce cost a lot in high-volume use cases.
Shorter, More Efficient Prompts
When the model already knows your domain, you do not need long prompts, many examples, or large system instructions every time. This reduces delay, lowers token cost, and makes the system easier to manage.
Behavioural Alignment & Safety
You may want the model to follow strict rules, avoid some topics, keep a fixed tone, or meet compliance needs. RLHF and DPO-based fine-tuning help shape the model’s behaviour in a more reliable way.
Data Privacy & On-Premise Deployment
Some businesses cannot send data to third-party model APIs. Fine-tuning an open-source model and running it on your own servers or private cloud helps keep your data and IP inside your own system.
OUR MODEL FINE-TUNING SERVICES
Our Model Fine-Tuning Services
We provide complete LLM fine-tuning services, from data preparation to deployment and model monitoring in production.
Training Frameworks
TRL
PyTorch
FSDP
DeepSpeed ZeRO
Hugging Face Transformers
PEFT Methods
LoRA
QLoRA
AdaLoRA
Prefix Tuning
Quantisation
bitsandbytes
GPTQ
AWQ
GGUF
Data Pipelines
Hugging Face Datasets
Custom Python ETL pipelines
LlamaFactory
Axolotl
Synthetic Data Gen
Claude 3.5 Sonnet
GPT-4o
Evaluation
ROUGE
BERTScore
MT-Bench
lm-evaluation-harness
Custom domain evaluation
Alignment (RLHF / DPO)
Model Serving
Ollama
FastAPI
Experiment Tracking
Containerisation
Docker
Kubernetes
NVIDIA NGC containers
HuggingFace Inference Endpoints
AI Business Transformation Services
We provide complete LLM fine-tuning services, from data preparation to deployment and model monitoring in production.
This is the most common fine-tuning method. We train the model on task-based input and output examples, such as question-answer pairs, instruction-response examples, and completion datasets. This helps the model learn the right output for your business domain. It can be used with almost any base model and for many task types.
We train models to follow complex and multi-step instructions more well. This works well for internal tools, automated workflows, code work, and document processing where the model must follow a fixed structure.
This is a more efficient fine tuning method. With Low-Rank Adaptation, we update only a very small part of the model, which lowers GPU memory needs. QLoRA makes this even lighter by using 4-bit quantisation. This helps fine-tune even large models with less hardware.
We build RLHF pipelines that help align model behaviour with human choices and feedback. This is useful for improving answer quality, lowering unsafe outputs, and helping the model match human judgment better.
DPO is a simpler and more stable choice than RLHF. It trains the model on preference data, such as chosen and rejected answer pairs, without using a separate reward model. We use DPO for alignment, safety tuning, and tone adjustment work.
We help adapt foundation models for high-risk and specialised industries such as legal, healthcare, finance, and engineering. In these fields, general models often do not perform well enough. Our work includes training on domain terms, adjusting professional tone, and improving task understanding.
The quality of the training data has a big effect on the final model. We manage the full data process, including data collection, cleaning, deduplication, formatting, synthetic data creation, and quality checks. This helps create datasets that lead to strong and stable results.
We fine-tune models to handle many tasks at the same time while keeping strong performance. We also support continual fine-tuning, where the model is updated over time as new business data becomes available.
Fine-Tuned Model Deployment & Serving
We deploy fine-tuned models to production on private cloud platforms such as AWS, GCP, and Azure, on-premise GPU systems, or efficient serving stacks such as vLLM, TGI, and Triton Inference Server. We also set up APIs, access control, auto-scaling, and monitoring.
MODELS WE FINE-TUNE
Base Models We Fine-Tune
We work with major open source and API-based model families. We choose the best base model based on your task, data size, and deployment setup.
| Model Family | Variants | Best For |
|---|---|---|
| Meta LLaMA 3 / 3.1 | 8B, 70B, 405B | Strong open-source performance and broad task support |
| Mistral / Mixtral | 7B, 8x7B, 8x22B MoE | Fast inference, multilingual work, code, and reasoning |
| Google Gemma 2 | 2B, 9B, 27B | Small and fast deployment, mobile, and edge use cases |
| Microsoft Phi-3 / 3.5 | Mini (3.8B), Small (7B) | Small-device use and strong reasoning for the model size |
| Falcon | 7B, 40B, 180B | Research and enterprise use with a permissive licence |
| Qwen 2.5 | 0.5B–72B | Multilingual support and long context use |
| OpenAI GPT-3.5 Turbo | Fine-tuning API | Closed-source option and structured task learning |
| Code Llama / DeepSeek | 7B–34B | Code generation, code review, and technical writing |
Model selection depends on task difficulty, speed goals, available GPU systems, data size, deployment setup, and total cost. We follow a structured model selection process during discovery.
Industries We Cater to
Industries
We provide comprehensive machine learning development services to businesses and startups in the following industry verticals.
Healthcare
As a provider of custom Artificial Intelligence development services in USA, we help healthcare organizations enhance diagnostics, automate workflows, and deliver data-driven patient care solutions.
- AI-powered diagnostic and imaging solutions
- Predictive analytics for better patient care
- AI-powered tools for medical data analysis
- Intelligent patient engagement platforms
Real Estate
As a top AI development company in USA, we develop custom AI solutions for real estate businesses. These improve property valuation accuracy, streamline transactions, and enhance customer experiences.
- Property price prediction systems
- AI-powered property recommendation platforms
- Lead management and CRM automation tools
- Market trend analysis solutions
Manufacturing
As a trusted manufacturing chatbot development company, manufacturers can unlock custom Artificial Intelligence solutions designed to optimize production lines, minimize operational expenses, and enable continuous real-time monitoring.
- Predictive maintenance systems
- AI-driven quality inspection solutions
- Production planning tools
- Smart factory automation platforms
Banking & Finance
For banking and finance, we develop AI-powered systems to automate processes and help financial institutions improve risk management and customer engagement.
- Fraud detection and risk analysis solutions
- AI-based credit scoring systems
- Tools for automated compliance and reporting
- Financial advisory platforms
Travel & Hospitality
We offer the best AI development services that help travel and hospitality businesses enhance customer experiences and optimize their day-to-day and time-consuming operations.
- AI-powered travel recommendation engines
- Dynamic pricing and revenue management systems
- AI-driven booking assistants and AI agents
- Customer sentiment analysis platforms
Energy
For the energy sector, we develop custom AI-powered apps, solutions, and agents that optimize the consumption of resources, monitor infrastructure, and enhance operational efficiency.
- Smart energy management systems
- Grid performance analysis tools
- Predictive maintenance systems for machines
- AI demand forecasting solutions
Public Sector
Being a trusted AI development company in India, we develop AI-driven systems that help with improving citizen services.
- AI platforms for citizen service
- Document processing apps
- AI-powered public safety monitoring solutions
- AI assistants for grievance redressals
Logistics
Our AI developers for hire build AI-powered solutions for the logistics sector that improve route planning, supply chain visibility, forecasting demand, and more.
- Intelligent route optimization systems
- Real-time shipment tracking platforms
- Warehouse automation solutions
- Demand prediction and inventory planning tools
Retail & E-Commerce
By providing AI development services, we transform the retail and e-commerce businesses. We build AI solutions that personalize shopping experiences, improve demand forecasting, and optimize digital commerce performance.
- Recommendation engine development
- AI-powered inventory management systems
- Customer behavior analytics platforms
- Intelligent chatbot and virtual assistant solutions
Education
Our custom AI solutions empower schools, coaching centers, and other educational institutions to personalize learning, automate administrative tasks, and improve academic results of the students.
- Adaptive learning platforms
- AI-powered grading and assessment systems
- Student performance analytics tools
- Virtual tutoring solutions
OUR FINE-TUNING PROCESS
Our Step Development Process
A clear and repeatable process helps turn a model from something that works only in testing into something that works well in real use. Here is our full method.
Step 1
Discovery & Use Case Definition
We first define the task clearly. We look at the input the model will get, the output it should give, the quality level needed, and how success will be measured. We also review your current data, check its size and quality, and find any gaps where synthetic data may be needed.
Step 2
Base Model Selection
We compare base models based on your task, infrastructure, speed needs, and budget. Before fine tuning starts, we run zero-shot and few-shot tests to set a baseline and understand how much value fine-tuning may add.
Step 3
Training Data Preparation
We collect, clean, remove duplicates, and format your training data in the right structure, such as instruction response pairs, preference pairs, or completion format. If your data is limited, we create high-quality synthetic examples using larger models and check them against your quality standards.
Step 4
Fine-Tuning Experiment Design
We design the training setup, including the right method such as SFT, DPO, or RLHF, along with LoRA settings, learning rate, batch size, gradient accumulation, and number of epochs. We also set up experiment tracking from the start so every run is recorded and easy to compare.
Step 5
Training & Iteration
We run training jobs on suitable GPU systems and track training signals such as loss curves, gradient norms, and validation metrics in real time. We improve the setup step by step based on early results, not only on final scores.
Step 6
Comprehensive Evaluation
We test the fine tuned model across key areas, including task accuracy, instruction following, safety behaviour, output format consistency, and performance on domain-specific test sets. We compare all results with the original baseline.
Step 7
Alignment & Safety Tuning (if required)
For customer-facing or high risk use cases, we apply extra alignment work using DPO or RLHF. This helps improve quality, safety, and rule following based on your company’s own guidelines and preference data.
Step 8
Model Quantisation & Optimisation
We use post training quantisation methods to reduce model size and memory use. This makes deployment more cost effective while keeping loss in accuracy low.
Step
Production Deployment & Monitoring
We deploy the final model with fast serving systems. We set up auto-scaling, track latency, throughput, and error rates, and provide full documentation, including the model card, training report, and deployment runbook.
WHY CHOOSE AHEX TECHNOLOGIES
Why Choose Ahex Technologies for Model Fine-Tuning?
End-to-End Ownership
We manage the full fine tuning process from start to end. This includes data work, training, testing, alignment, compression, deployment, and monitoring. You get one team that handles everything.
Evaluation First Methodology
We start every project by deciding how success will be measured. Before training begins, we prepare test sets and use them to track progress. This helps you clearly see how the model improves.
Model Agnostic Expertise
We are not limited to one model or one cloud provider. We choose the model that fits your use case best, whether it is open source or commercial. We also deploy it on the setup that fits your budget, speed, and compliance needs.
Data Quality Comes First
Good results depend on good data. That is why we focus strongly on data preparation. We clean, organise, format, remove duplicates, and create extra data where needed before training starts.
On-Premise & Air Gapped Deployment
If your business has strict data security needs, we can deploy fine tuned open-source models inside your own environment. Your data stays in your system, with no outside API calls and no shared model access.
Unique Odoo + AI Expertise
We understand both Odoo ERP and AI development. This helps us fine tune models on your business data while also understanding your daily work. As a result, the AI fits better into your ERP workflows.
Our Engagement Models
Flexible models built around how enterprises actually procure AI development services.
24/7 Operations
We provide expert AI Solutions in which our team assesses your existing workflows and identifies automation opportunities. Based on your goals, we design a foolproof roadmap for high-impact automation implementation.
Min 3 months · 2–10 engineers
Project-Based
Fixed scope, timeline, and price. Perfect for well-defined agent builds — a specific automation workflow, RAG system, or multi-agent customer support solution.
From $15,000 · 6–14 weeks
Popular
Staff Augmentation
Ahex AI engineers join your existing team on contract. We bring LangGraph, RAG, and multi-agent expertise your team lacks without the cost of senior AI hiring.
Month-to-month · Individual experts
Retainer
Monthly retainer for continuous agent development, optimisation, and monitoring. Best for companies with an active deployment needing ongoing iteration.
From $5,000/month · Ongoing
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Ready to Unlock Your AI's Full Potential with Expert Fine-Tuning?
If you need AI that goes beyond generic, out-of-the-box models—AI that is highly specialized, accurate, and aligned with your business—Custom Fine-Tuning is the right foundation. And Ahex Technologies is the right team to build it. We have delivered custom AI models across industries. We understand both the technical complexity and the business needs that make a Fine-Tuning project succeed.
Get in touch with us today to start your model optimization journey!
Testimonials
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Frequently Asked Question
Related to fine-tuning
Model fine-tuning means training an already trained language model on your own task or your own data. The model keeps its general language knowledge, but it also learns your business terms, style, and needs.
Prompt engineering tells the model what to do through instructions in the prompt. It does not change the model itself. Fine-tuning trains the model on your own data, so the model changes and learns from it.
RAG is useful when the model needs to read live or changing data from a knowledge base. Fine tuning is better when you need a fixed tone, set format, domain knowledge, or stable behaviour built into the model. Many businesses use both together. We help choose the right setup based on your use case.
It depends on the task. For a small and focused task, 500 to 5,000 good examples may be enough. For wider domain training, 10,000 to 100,000 or more examples may be needed. For preference-based tuning, even 1,000 to 3,000 strong answer pairs can improve model behaviour.
LoRA is a lighter way to fine tune a model. It trains only a small extra part and keeps the main model mostly unchanged. This saves a lot of memory and cost. It still gives very good results for many tasks. That is why many teams prefer it.
RLHF is a method that improves a model by using human choices and feedback. It helps the model give better, safer, and more helpful answers. It is useful when answer quality and behaviour matter a lot. Some teams also use simpler methods that can give similar results.
A focused fine tuning project usually takes about 4 to 8 weeks. This includes data work, training, testing, and deployment. If the project needs more data cleaning, more training rounds, or more alignment work, it may take 8 to 16 weeks. We share a clear timeline during planning.
Yes. You can fine-tune an open-source model on your own GPU systems or private cloud this overall keeps your data under your control.
We decide the evaluation rules before training starts and measure them clearly. This includes accuracy, correct format, domain test scores, instruction following, safety behaviour, and human review. We also compare the new model with the original version.
We fine-tune many leading open-source and closed-source models. The best choice depends on your task, setup, and budget. We help choose the right one during the project.
After fine tuning, we deploy the model in a fast and secure setup. We also set up scaling, access control, and monitoring. For private or lighter setups, we can also deploy it inside your own environment.
Yes. We offer fine tuning services for businesses that use Odoo ERP. We train models on your ERP structure, business rules, product data, customer records, and reporting needs. This helps create AI tools that answer natural language questions, generate Odoo style reports, and automate ERP tasks while keeping your data inside your system.
Let's Talk
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- sales@ahex.co.in
- (India) +91 - 8885564224
- (USA) +1 - 6503197394
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