Fine tune custom LLMs and open source AI models

Need a large language model that speaks your company's tone, adheres strictly to proprietary business logic, or masters domain-specific terminology? I provide end-to-end LLM fine-tuning, training dataset curation, and deployment for open-source and proprietary foundation models.

What this offer includes for the base price:
- Data auditing, formatting, and conversion into standardized JSONL training pairs
- Fine-tuning of 1 OpenAI model (GPT-4o mini / GPT-3.5) or small open-weight LLM (Llama 3 / Mistral)
- Hyperparameter configuration (epochs, learning rate, batch size)
- Pre- and post-training evaluation report showing model accuracy & loss curves
- Inference script and API endpoint setup to query your trained model

Fine-Tuning Capabilities & Tech Stack:
- Techniques: LoRA, QLoRA (Quantized Low-Rank Adaptation), Full Parameter Tuning, DPO/RLHF
- Models: Llama 3/3.1, Mistral/Mixtral, DeepSeek, Gemma, OpenAI custom models
- Data Engineering: Synthetic data generation, prompt-response dataset cleaning, token optimization
- Infrastructure: Hugging Face PEFT/Transformers, RunPod, Lambda Labs, Google Colab Pro
- Deployment: Ollama local serving, vLLM for high-throughput inference, Hugging Face Inference Endpoints

Have a custom enterprise dataset, specialized legal/medical corpus, or self-hosted GPU requirement? Send a message to assess your dataset feasibility!