Oodles delivers production-ready LLM Fine-Tuning services that adapt open-source Large Language Models to your business data, workflows, and domain language with high accuracy and cost efficiency. We fine-tune models such as LLaMA 3, Mistral, Mixtral, Gemma, Phi-3, BLOOM, and Falcon using LoRA, QLoRA, PEFT, Instruction Tuning, and RLHF, powered by PyTorch, Hugging Face Transformers, Accelerate, DeepSpeed, CUDA, and distributed GPU infrastructure.
LLM Fine-Tuning is the process of adapting a pre-trained large language model to a specific domain, task, or enterprise dataset by continuing training on curated instruction data, conversations, or domain corpora.
At Oodles, we apply parameter-efficient and instruction-based fine-tuning techniques using PyTorch, Hugging Face Transformers, PEFT, and TRL to improve factual accuracy, reduce hallucinations, align tone, and optimize inference cost.
Fine-tune billion-parameter models on consumer GPUs with minimal VRAM.
Healthcare, Legal, Finance, Customer Support, Technical Documentation.
Your data never leaves your environment. Full model ownership.
Up to 90% cheaper and 10x faster than training from scratch.
Handle increasing workloads with optimized fine-tuning pipelines.
Get expert guidance for model selection, dataset prep, and deployment.
Fine-tune on your support tickets to reduce response time by 80%.
Train models on contracts, regulations, and case law for accurate analysis.
Fine-tune on EHRs, research papers, and clinical guidelines.
Create coding assistants fine-tuned on your codebase and standards.
Build models to analyze market trends, forecasts, and financial reports.
Generate or summarize technical manuals and documentation automatically.