I will fine tune gpt, llama, or mistral on your custom dataset for better ai results
Machine Learning Engineer
Informazioni su questo servizio
Want an AI model that speaks YOUR language, knows YOUR domain, and gives responses tailored to YOUR business? I fine-tune large language models (LLMs) on your custom data so the AI works specifically for you.
What I Can Do:
Fine-tune GPT-3.5/GPT-4 via OpenAI's fine-tuning API
Fine-tune open-source models: LLaMA 3, Mistral, Phi-3, Gemma
LoRA / QLoRA fine-tuning for efficient training on consumer GPUs
Instruction tuning for custom Q&A, chatbots, or assistants
Domain adaptation for medical, legal, financial, or technical text
RLHF (Reinforcement Learning from Human Feedback) alignment
What You'll Need to Provide:
Training data in a format I specify (I'll help you prepare it)
Examples of desired input-output pairs (at least 50-100 for basic fine-tuning)
Description of your use case and desired behavior
What You'll Receive:
Fine-tuned model weights (hosted on Hugging Face or your preferred platform)
Training script with full documentation
Evaluation results comparing base vs. fine-tuned performance
Inference script / API for using the model
Data preparation guide for future updates
Tools: Hugging Face Transformers, PEFT, Axolotl, Unsloth, OpenAI API, PyTorch, Weights & Biases
Linguaggio di programmazione:
Python
•
Colab
Framework:
Scikit-learn
•
DeepPy
•
keras
•
PyTorch
•
Panda
•
tensorflow
Altri servizi della categoria Data science e ML offerti da me
FAQ
How much training data do I need?\nA:
For OpenAI fine-tuning, 50-100 high-quality examples can already show improvement. For open-source models, 500-5,000 examples give best results. Quality matters more than quantity — I'll help you curate the best training set.
Which model should I fine-tune?\nA
It depends on your needs. For easy deployment and high quality, OpenAI's GPT-3.5 is great. For privacy and full control, open-source models like LLaMA 3 or Mistral are better. Message me and I'll recommend the best option for your use case and budget.
