Open-Source LLM Fine-Tuning
Generic models like GPT-4o are great for general tasks, but they lack deep domain expertise and cannot be fine-tuned on highly sensitive data. We take powerful open-source models (Llama 3, Mistral, Qwen) and rigorously fine-tune them on your proprietary datasets, creating a specialized model that outperforms generic APIs for your specific use cases at a fraction of the inference cost.
Core Features
Domain-Specific Accuracy
Injecting deep industry knowledge (medical, legal, financial) into the weights of the model so it naturally understands your specialized jargon.
Instruction & Chat Tuning
Training the model to respond in your exact brand voice, follow strict formatting rules (e.g., specific JSON schemas), or act as a specific persona.
Massive Cost Reduction
A fine-tuned 8B parameter model can often outperform a massive 70B model on a specific task, reducing your API/inference costs by up to 90%.
Data Privacy Guarantee
Because the model is open-source, we train and deploy it entirely within your secure VPC. Your proprietary training data never touches a public API.
Our Process
Dataset Curation & Formatting
Week 1-2The most critical step. We collect your raw data and format it into thousands of high-quality instruction-response pairs.
Base Model Selection
Week 3Benchmarking the latest open-weights models (Llama 3, Mistral, Gemma) to select the optimal architecture for your task and hardware constraints.
LoRA / QLoRA Training
Week 4-5Running parameter-efficient fine-tuning (PEFT) on cloud GPU clusters. We use QLoRA to reduce memory footprint while maintaining high accuracy.
Evaluation & Alignment
Week 6Testing the fine-tuned model against a holdout dataset using LLM-as-a-judge and human alignment (RLHF/DPO) to correct any unwanted behaviors.
Quantization & Deployment
Week 7Compressing the final model weights (GGUF, AWQ, ExLlamaV2) to maximize inference speed before deploying it to an API endpoint like vLLM.
Technologies We Use
FAQ
How much data do we need to fine-tune a model?
Should we use RAG or Fine-Tuning?
What hardware is required to run our own model?
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