LLM Fine-TuningCustom Model Training & Distillation

LLM Fine-Tuning with Custom Training Pipelines

End-to-end LLM fine-tuning using LoRA, QLoRA, and full fine-tuning with NeMo and evaluation harnesses. We train domain models that outperform generic LLMs on your tasks.

LLM Fine-Tuning Capabilities for Custom Model Training & Distillation

LoRA and QLoRA fine-tuning

NeMo training pipelines

Domain data curation

Continuous evaluation harnesses

Model distillation workflows

Use Cases

1

Legal domain models trained on case law

2

Medical LLMs fine-tuned on clinical data

3

Financial models for risk and compliance

4

Technical documentation models for engineering teams

Integration Details

LLM Fine-Tuning

LLM fine-tuning for domain-specific performance. We train models on your data using LoRA, QLoRA, and full fine-tuning approaches.

OpenAIAnthropicOpen-source modelsCloud providersOn-premise

Custom Model Training & Distillation

Training domain models on curated corpora, applying NeMo and LoRA distillation, and wiring evaluation harnesses so accuracy stays high while latency and spend drop.

NVIDIA NeMo MicroservicesHugging Face TransformersLoRA & QLoRADeepSpeed & MegatronRAG Evaluation HarnessesPromptFlow & TruLensWeights & Biases

Ready to Implement LLM Fine-Tuning for Custom Model Training & Distillation?

Let's discuss how we can help you leverage llm fine-tuning within your custom model training & distillation strategy.

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