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I will fine tune llm, train a custom ai model, and evaluate a dataset
Data scientist
👋 Hi there! I'm Muhammed Ali Şahan, a skilled data scientist and engineer with deep expertise in Python, machine learning, data analytics, and big data projects. From data processing and analysis to ...
À propos de ce service
Generic AI models give generic answers. A model fine-tuned on your data speaks your domain, follows your format, and costs a fraction of constant API calls. I fine-tune open-source LLMs on your custom data with full evaluation, not guesswork.
What I deliver:
- Fine-tune Llama, Mistral, Qwen, Gemma, Phi, DeepSeek, and GPT models
- LoRA and QLoRA fine-tuning for efficient training on your task
- Dataset preparation, cleaning, deduplication, format conversion
- Instruction tuning, classification, domain adaptation, style matching
- Rigorous evaluation: accuracy, perplexity, hallucination rate, custom benchmarks
- Comparison against the base model so you can see the real improvement
- Quantization (GGUF, GPTQ) for cheaper, faster deployment
- Deployment guidance for vLLM, Ollama, Hugging Face Endpoints
- Experiment tracking with Weights and Biases or MLflow
Stack: Python, PyTorch, Hugging Face Transformers, PEFT, TRL, LoRA, QLoRA, Unsloth, vLLM, Ollama.
I will tell you up front whether fine-tuning is even the right move for your use case or whether prompt engineering or RAG would serve you better and more cheap. Honest scoping, no overselling.
Message me with your task and dataset
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FAQ
Do I actually need fine tuning, or is RAG enough?
Honest answer: many use cases do not need fine-tuning. If you want the model to know facts from your documents, RAG is usually better and cheaper. Fine-tuning is right when you need a specific output format, a domain tone, a classification task, or lower inference cost at scale.
What models can you fine tune?
Open-source models: Llama, Mistral, Qwen, Gemma, Phi, DeepSeek, and others on Hugging Face. I can also fine-tune OpenAI models (GPT) through their fine-tuning API. I will recommend the best base model for your task, budget, and deployment target.
How much data do I need to fine tune?
It depends on the task. Style or format matching can work with a few hundred good examples. Domain adaptation or classification usually needs 1,000 to 10,000+ examples. Quality matters more than quantity. If you do not have enough data, I can help create or augment a dataset (available as an extra).
Will the fine tuned model be better than GPT?
Not at general intelligence. A fine tuned small model wins on a specific narrow task: your format, your domain, lower cost, faster speed, and full data privacy since it runs on your own hardware. I always benchmark the fine tuned model against the base and against a strong API model
Do you provide evaluation, not just training?
Yes, and this is what separates a real fine tune from a guess. Standard and Premium include evaluation: accuracy, perplexity, hallucination rate, and a comparison against the base model.
Who pays for the GPU and compute costs?
Compute (GPU rental on Colab, RunPod, Vast, or cloud) is separate from my service fee, usually $5 to $50 depending on model size and dataset. I will estimate it upfront so there are no surprises. For small models, costs are minimal. I optimize training to keep compute low.
Can I run the fine tuned model myself after?
Yes. You own the model weights and the code. Premium includes a deployment guide for vLLM, Ollama, or Hugging Face Endpoints, plus quantization (GGUF, GPTQ) so it runs cheaply on modest hardware. You are never locked into me for inference.
What do you need from me to start?
Your dataset (or a description so I can help build one), the task you want the model to do, and your deployment target (cloud, local, edge). API docs or examples of the ideal output help a lot. I handle the training, evaluation, and delivery.
