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Fiverr Pro
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$
USD
I will fine tune open source llms for your chatbot or internal ai agent
AI Voice and Chat Agents, API Integrations and MCP Servers
Clearly scoped work, documented handoff, and practical systems that fit your business.
I'm an AI engineer and independent software developer based in Chicago. I connect AI voice and chat agents to bu...
About this Gig
Adapt an LLM to the behavior, language and outputs your chatbot or internal agent needs.
I fine-tune suitable open-source and open-weight language models using your authorized data, with a defined training plan and evaluation against the original model.
Each project is custom-scoped around one use case, a selected model, dataset readiness and a compute budget. LoRA/QLoRA or full-parameter fine-tuning is chosen where appropriate; these are training methods, not pricing tiers.
Deliverables can include:
- Dataset preparation and train/validation/test splits
- Reproducible training code and configuration
- Trained adapters or model checkpoints, subject to licensing
- Held-out evaluation, baseline comparison and documented limitations
- Model-loading instructions and technical handover
This is model training, not just prompt writing. Training from scratch, unlimited experiments, a complete chatbot application and ongoing hosting are not included by default. The package includes up to $150 of GPU rental; compute beyond that cap, storage and other third-party costs are agreed separately.
Message me before ordering with your use case, model, data, budget and deployment target.
Programming Language:
Python
•
Pytorch
Data Type:
Text
•
Images
•
Multimodal
AI Engine:
DeepSeek
•
Llama
•
Falcon
•
PyTorch
•
Other
My Portfolio
FAQ
What is the difference between fine-tuning and prompt engineering?
Fine-tuning trains model or adapter parameters on examples. A prompt supplies instructions at inference time with no training. This gig is an agreed training engagement, not only prompts or uploaded documents.
Do you offer LoRA, QLoRA and full-parameter fine-tuning?
The method is chosen for the model, task, data and compute budget. LoRA/QLoRA train adapters on a frozen base; full-parameter fine-tuning updates the pretrained model itself. These are technical choices within a custom scope, not package levels.
Which models can you work with?
Suitable open-source or open-weight models with accessible weights and compatible training tools. We verify the exact model version, license, architecture and hardware needs before agreeing the work. Availability does not automatically permit every commercial use or redistribution.
What data do I need?
Relevant examples you own or are authorized to use, including examples of desired responses or actions where needed. Quality, consistency, coverage and volume affect feasibility. We review a sanitized sample and agree preparation work before ordering.
What will you deliver?
The agreed trained artifacts, training and configuration code, base-model and environment details, an evaluation report and loading instructions. Adapters require their specified base model. Full or merged checkpoints are delivered only when agreed and permitted by the license.
Will the model be more accurate or stop hallucinating?
No particular improvement is guaranteed without evidence. We define task-specific evaluation, compare with a baseline and report results and limitations. Fine-tuning does not make a model error-free or replace fresh data retrieval in every use case.
Does the price include GPU costs and deployment?
The package includes up to $150 of GPU rental. Compute beyond that cap, storage and other third-party costs are agreed in writing. A hosted endpoint, chatbot UI, business-system integration and ongoing inference are separate unless explicitly included.
Do you train from scratch or keep training until a target score is met?
Not under an ordinary fine-tuning scope. Foundation-model pretraining, continued pretraining, large distributed runs and extra experiments need separate assessment and budget. The agreement defines included runs, acceptance criteria and stop conditions.
What if fine-tuning is not the right approach?
We assess fit before ordering. If prompting, retrieval or a different model is more appropriate, I will say so instead of selling unnecessary training. A purchased training project is not silently replaced with a different service.
