Get a production-ready, fine-tuned LLM — GPT, LLaMA3, FALCON, AraBERT and more — trained on your own data using LoRA and QLoRA techniques by a London-based data scientist.
I Will Fine-Tune a Large Language Model on Your Dataset
Fine-tuned LLM for a straightforward single task, with source code and documentation.
- Research into the right model architecture for your task
- Data preprocessing and dataset preparation
- Model creation and LoRA / QLoRA fine-tuning
- Model validation and testing
- Full source code delivered
- Model documentation included
Extended fine-tuning engagement with more iterations and revision rounds for a more refined result.
- Everything in Basic
- Longer delivery window for more thorough training runs
- 3 revision rounds for model adjustments
- Suitable for moderately complex tasks or larger datasets
- Full source code and model documentation
- Model validation and testing report
Full-scope, high-complexity fine-tuning project with maximum revisions and comprehensive delivery.
- Everything in Boost
- Extended 10-day timeline for complex or large-scale fine-tuning
- 4 revision rounds for thorough refinement
- Ideal for advanced architectures, multilingual models, or large datasets
- Comprehensive source code and full model documentation
- Detailed model validation, testing, and performance notes
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Key details about this service to help you decide. Generated by Zinn Hub, not the seller.
Value Position
Supported Models
Optimisation Technique
Deliverables Included
Turnaround & Revisions
What You'll Receive
Full Description
If your business needs an AI model that actually understands your domain, your language, and your data — generic off-the-shelf models simply will not cut it. Fine-tuning a large language model (LLM) on your specific dataset transforms a powerful but general-purpose model into a precision tool built for your exact use case.
This service covers the complete fine-tuning pipeline: from initial research and data preprocessing through to model creation, training, validation, and thorough documentation. You receive working source code and a model ready to integrate into your project — not a vague proof of concept.
Supported models include GPT, LLaMA3, AraBERT, Arabic GPT-2, JAIS, FALCON, QWEN2.5, T5, BLOOM, mBART, and more. Whether your project is in English, Arabic, or another language, there is a suitable architecture here.
The fine-tuning process uses state-of-the-art parameter-efficient techniques — LoRA (Low-Rank Adaptation) and QLoRA (Quantised LoRA) — which dramatically reduce memory consumption and compute costs whilst preserving high model performance. This means you benefit from a custom-trained model without requiring expensive enterprise GPU infrastructure.
All data you share is handled securely and kept strictly confidential throughout the engagement. Your proprietary datasets and business logic remain yours.
Here is exactly what every tier includes:
— Research into the most suitable model architecture for your task
— Data preprocessing to clean and structure your dataset for training
— Model creation and configuration
— Fine-tuning using LoRA or QLoRA as appropriate
— Model validation and testing to confirm performance
— Full source code so you can retrain or adapt the model independently
— Model documentation explaining architecture choices, training details, and usage
This service is ideal for businesses and developers building custom chatbots, document understanding systems, sentiment analysis tools, Arabic-language AI applications, domain-specific text generation, classification pipelines, and similar NLP-driven products.
Zinn Digital is based in London, England, and brings specialist data science expertise to each project. Every engagement begins with a conversation — please get in touch via the order chat before or after placing your order so the scope can be confirmed and the work can begin without delay.
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Compare Packages
| Feature | Basic | Boost | Premium |
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
| Revisions | 1 | 3 | 4 |
| Research into the right model architecture for your task | ✓ | ✕ | ✕ |
| Data preprocessing and dataset preparation | ✓ | ✕ | ✕ |
| Model creation and LoRA / QLoRA fine-tuning | ✓ | ✕ | ✕ |
| Model validation and testing | ✓ | ✕ | ✕ |
| Full source code delivered | ✓ | ✕ | ✕ |
| Model documentation included | ✓ | ✕ | ✕ |
| Everything in Basic | ✕ | ✓ | ✕ |
| Longer delivery window for more thorough training runs | ✕ | ✓ | ✕ |
| 3 revision rounds for model adjustments | ✕ | ✓ | ✕ |
| Suitable for moderately complex tasks or larger datasets | ✕ | ✓ | ✕ |
| Full source code and model documentation | ✕ | ✓ | ✕ |
| Model validation and testing report | ✕ | ✓ | ✕ |
| Everything in Boost | ✕ | ✕ | ✓ |
| Extended 10-day timeline for complex or large-scale fine-tuning | ✕ | ✕ | ✓ |
| 4 revision rounds for thorough refinement | ✕ | ✕ | ✓ |
| Ideal for advanced architectures, multilingual models, or large datasets | ✕ | ✕ | ✓ |
| Comprehensive source code and full model documentation | ✕ | ✕ | ✓ |
| Detailed model validation, testing, and performance notes | ✕ | ✕ | ✓ |
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Fine-Tune a Large Language Model on Your Dataset


Fine-Tune a Large Language Model on Your Dataset

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Frequently Asked Questions
Please get in touch via the order chat before placing your order to briefly describe your project — the task type, your dataset, and the model you have in mind if any. This ensures the correct tier is selected and work can begin immediately after purchase.
Common formats such as CSV, JSON, JSONL, plain text files, or Excel spreadsheets are all acceptable. If you are unsure whether your data is suitable, mention it in the order chat and it can be assessed during the data preprocessing stage.
Supported models include GPT, LLaMA3, AraBERT, Arabic GPT-2, JAIS, FALCON, QWEN2.5, T5, BLOOM, mBART, and similar architectures. If you have a specific model in mind that is not listed, raise it in the chat and it can be discussed.
Yes. Data privacy and confidentiality are a priority. Your datasets and any proprietary business information you share are handled securely and will not be disclosed to any third party.
You receive the fine-tuned model weights or checkpoint, the complete source code used for training, and model documentation covering architecture decisions, training configuration, and guidance on how to run or deploy the model.
A revision covers adjustments to the fine-tuning approach, hyperparameters, or dataset handling based on your feedback after reviewing the initial deliverable. Changes that constitute an entirely new task or a substantially different dataset are outside revision scope.
Deliverables are shared via a cloud link (such as a file-sharing service) through the order manager. Model files can be large, so this is the most practical method. Instructions on accessing and using the files are included in the documentation.
Basic suits straightforward single-task fine-tuning on a clean, moderate-sized dataset. Boost is appropriate when you need more back-and-forth and a slightly longer training window. Premium is designed for complex projects, large or multilingual datasets, or advanced architectures requiring extended development time.
Customer Reviews
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Very prompt and helpful
Very detailed knowledge
I had the pleasure of working with a programmer on a machine learning project, and I can confidently say that he was excellent. He is highly knowledgeable and explained all the concepts clearly, ensuring that I fully understood everything. He is very skilled in the field and never let me leave without grasping the material. He is experienced and always works efficiently within deadlines. He regularly scheduled meetings with me to explain the steps, share progress, and clarify the idea from the very beginning. He is truly excellent, and I highly recommend working with him. This won’t be my last time working with him, inshallah.
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