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I will build, fine tune and deploy custom llms for your business
AI Solutions Architect
I work with enterprises to understand their AI requirements, define the right solution architecture and take the engagement from initial discovery through development, optimisation and deployment.
I w...
About this Gig
Generic models give generic answers. I'll fine-tune an open-source LLM on your data so it speaks your domain, follows your format, and costs far less than constant API calls.
Why BluBridge: we're a frontier AI research team, not a script-runner. We build our own training framework (C++/CUDA), ML compiler and data pipeline, all published on arXiv. We're an NVIDIA Inception member and AWS Consulting Partner.
What you get:
- Fine-tuning of Llama, Mistral, Qwen, Gemma, DeepSeek and more
- LoRA, QLoRA or full fine-tuning, chosen for your budget
- Dataset cleaning, deduplication and formatting
- Evaluation against the base model on your own test set
- Quantized models (GGUF, AWQ) for cheaper inference
- Deployment with vLLM or a private API (Premium)
- Full source code and documentation
Honest scoping: if RAG or prompt engineering will serve you better than fine-tuning, I'll tell you before you spend a dollar.
Basic & Standard cover models up to 8B and datasets up to 10k examples.
Please message me before ordering so I can review your task and data and recommend the right package. Larger models or custom pipelines? Ask for a custom offer.
Programming Language:
Python
•
C++
•
Pytorch
•
Other
Data Type:
Text
AI Engine:
GPT
•
DeepSeek
•
Bert
•
RoBERTa
•
Llama
•
PyTorch
•
Other
FAQ
Do I actually need fine-tuning, or is RAG enough?
If you need the model to know facts from your documents, RAG is often cheaper and easier to update. Fine-tuning is better for teaching a style, format, task or domain language. Message me your use case and I'll recommend the right approach before you order.
Which models can you fine-tune?
Open models including Llama, Mistral, Qwen, Gemma, Phi and DeepSeek, plus encoder models like BERT and RoBERTa for classification. Basic and Standard cover models up to 8B parameters. For larger models, ask for a custom offer.
How much data do I need?
For style or format tasks, a few hundred high-quality examples can work. For domain adaptation, a few thousand is better. Quality matters more than quantity, and in the Standard and Premium packages we clean and structure your data for you.
Who pays for GPU and compute costs?
Compute is included for all orders within the package scope. For larger models or long training runs, we either quote compute separately in a custom offer or train on your own cloud account.
Will my fine-tuned model beat GPT-4 or ChatGPT?
On your specific, narrow task, a well-tuned smaller model can match or beat a general model while being far cheaper and fully private. On general knowledge it won't. We'll set realistic targets with you before training starts.
How do you prove the model actually improved?
Every package includes a side-by-side evaluation of the base model and the fine-tuned model on a held-out test set from your data, using metrics suited to your task (accuracy, F1, perplexity or custom checks).
Who owns the model, and is my data safe?
You own the fine-tuned weights and the source code. Your data is used only for your project and deleted on request, and we're happy to work under an NDA.
Can I run the model myself afterwards?
Yes. You receive the model weights (and quantized versions where included) along with instructions to run them locally, with vLLM or Ollama, or on your cloud. Premium includes deployment for you.
What do you need from me to start?
Your dataset (or sample data), a short description of the task, examples of good outputs, and where you plan to run the model. The more specific you are, the better the result.
