Custom LLM Fine-Tuning
Don't rely on generic AI. We fine-tune open-source Large Language Models exclusively on your proprietary data to perform highly specialized business tasks.
Off-the-shelf models like GPT-4 are too generic, expensive at scale, and fail to understand your company's highly specific industry jargon or unique formatting requirements.
Impact
High API costs for prompts that require massive context windows, and AI outputs that sound generic or lack the precise formatting your engineers require.
We provide end-to-end LLM fine-tuning services. We take open-source models and train them on thousands of your successful examples (Instruction Tuning) to create a proprietary model you own.
Technical Approach
Utilizing LoRA/QLoRA on cloud GPUs (AWS EC2, RunPod) to fine-tune Llama 3, Mistral, or BERT. We deploy the resulting weights via vLLM or Ollama for blazing-fast inference.
Trying to force GPT-4 to output a specific JSON schema using a 2,000-word system prompt, resulting in high latency and frequent formatting errors.
A fine-tuned 8B parameter model running locally that instantly outputs the exact JSON structure required, with 99% accuracy and near-zero ongoing API costs.
Strict adherence to global data privacy laws. We never train public AI models on your proprietary data.
Architecture designed to meet rigorous healthcare and enterprise security compliance standards natively.
Scalable cloud-native deployments via AWS and Vercel Edge networks ensuring 99.99% uptime.
Everything you need to know about our Custom LLM Fine-Tuning process.
You do. When we fine-tune an open-source model (like Llama 3), the resulting model weights belong entirely to your company. You are not locked into any vendor API.
No. With modern Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA, we can often achieve excellent results with as few as 1,000 to 5,000 highly curated examples.
Let's discuss your fine-tuning project.
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