Our agency will fine tune custom open source llm on your proprietary business data

Fine-tuning depends heavily on your dataset size, format, and target use case. Messaging first ensures we verify your data feasibility and pick the most cost-effective open-source architecture.

Your data is strictly yours. We execute enterprise NDAs, use private air-gapped compute environments, and delete all training data immediately after project completion.

We work with top open-source models including Llama 3/3.1, Mistral/Mixtral, Qwen 2.5, Phi-3, Gemma 2, and specialized code/embedding models.

Compute costs (e.g., RunPod, Lambda Labs, AWS EC2, or Colab Pro) are paid directly by the client. We optimize training using QLoRA to keep compute costs typically under $10–$50.

We accept JSONL, CSV, raw text, Markdown, PDFs, or instruction-response pairs. If your data is unstructured, we can clean and format it into high-quality instruction datasets.

We provide PEFT/LoRA adapter weights, merged safetensors, quantized GGUF files for Ollama/LM Studio, or a containerized vLLM/FastAPI service.

We need your sample/full dataset and access to your preferred cloud GPU instance (RunPod, Hugging Face, or AWS) or local environment.

Revisions cover hyperparameter adjustments (learning rate, epochs, rank), re-evaluation on custom validation sets, and export format conversion.

Yes. As a full-stack engineering agency, we build FastAPI microservices, Docker containers, and Next.js/Node.js frontends to serve your model smoothly.Yes. As a full-stack engineering agency, we build FastAPI microservices, Docker containers, and Next.js/Node.js frontends to serve your model smoothl

Yes. The Premium tier includes 14 days of support. We also provide ongoing maintenance packages for retraining, dataset updates, and server scaling.