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I will build custom llm and slm with qlora
AI Engineer and Full Stack Developer: Expert in Scalable AI Solutions!
Experienced AI Engineer & Full Stack Developer with 7+ years of expertise in building intelligent, scalable, and efficient solutions. I specialize in AI-powered applications using GPT-4/3.5, OpenAI AP...
About this Gig
Fine-tune a custom LLM that knows YOUR domain not the whole internet.
I'm Raihan, an AI/ML engineer & CTO at ClarioScope AI. I train small language models from scratch (ORCH 350M3B, MedLLM, ILMA Lang) and fine-tune open LLMs with QLoRA on your real data.
What you get: Fine-tuned LLM/SLM on your dataset Llama, Mistral, Qwen, Gemma, Phi LoRA / QLoRA / full fine-tune (I pick what fits your data & budget) Dataset cleaning, formatting + synthetic data generation Evaluation report vs the base model (perplexity, accuracy) Inference-ready: Hugging Face, GGUF for Ollama, or an API endpoint Clean PyTorch code + documentation
Why me, not a $90 gig? Most "fine-tuning" gigs just wrap the OpenAI API. I build real SLMs from scratch so I choose the right base model & LoRA rank and ship a model that actually beats the base. Portfolio: raihan-js.github.io
️Process: Free scoping chat data prep training evaluation vs base delivery + handoff.
Your data stays private. Full weights + commercial-use rights available.
Message me your use case first for an accurate quote. Let's build it right!
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I designed and developed Full WordPress Website for this client.
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FAQ
Do you train models from scratch or only fine-tune existing ones?
Both. I've trained the ORCH series (350M–3B) and MedLLM from scratch, and I fine-tune open LLMs daily. For most use cases, QLoRA fine-tuning a strong base (Llama 3.1, Mistral, Qwen) gives 80–90% of the benefit at a fraction of the cost — I'll recommend honestly based on your data.
Which base models can you fine-tune?
All major open-source LLMs: Llama 3.1/3.2 (1B–13B), Mistral 7B / Mixtral, Qwen 2.5, Gemma 2, Phi-3, DeepSeek, and Code Llama / Code Qwen. I can also fine-tune OpenAI (GPT-4o-mini, GPT-4.1) and Gemini via their tuning APIs.
How much training data do I need?
For LoRA/QLoRA, as few as 500 high-quality examples can work; 2,000–10,000 is the sweet spot. Have less? I generate synthetic data for you (Standard & Premium). Training a small model from scratch needs a substantial corpus — we'll confirm on the scoping call.
What hardware is used, and who pays for compute?
I use Runpod / Vast.ai (A100 / H100 GPUs). Compute for standard runs is included in all packages. For very large datasets or long pre-training, GPU cost may be billed at-cost as a small extra — always agreed upfront (typically $20–$120).
Will my data and trained model stay private?
Yes. Your data is used only for your project and never reused. You receive the full weights, code, and commercial-use rights (included in Premium; +$180 on Basic/Standard).
Can you deploy the model so my app can call it via API?
Yes — Premium includes a FastAPI + Docker container with an OpenAI-compatible endpoint, so your existing code just swaps the base URL. Standard buyers can add deployment for +$250.
What's the difference between fine-tuning and RAG?
Fine-tuning changes the model's behavior and knowledge in its weights. RAG retrieves answers from your documents at query time. Need RAG instead? I offer that as a separate gig — or message me and I'll tell you which one actually fits your goal.
Why should I hire you over a cheaper fine-tuning gig?
Most low-priced gigs are thin wrappers around the OpenAI API. I'm a CTO who trains real SLMs from scratch (portfolio: raihan-js.github.io) — so I'll tell you when fine-tuning is the wrong answer, pick the right base model and LoRA config, and deliver a model that measurably beats the base.
