# llm.build

> Specialist LLM and SLM adaptation for insurance, retail and teams that need control over inference, cost and AI sovereignty.

Written by Shen Pandi (Towards AGI). Fine-tuning does not create sovereignty or security.

## Pages
- / — Conversion hub, assessment and contact
- /about — Who writes this
- /llm-fine-tuning — When fine-tuning is the conversation
- /slm — When a smaller model is a candidate
- /inference — Where the model runs
- /ai-sovereignty — Sovereignty is an operating choice
- /sovereignty-checklist — Citable checklist
- /insurance — Insurance document work
- /data-readiness-review — Held-out test set and readiness
- /sample-report — Report format, no customer scores
- /compare — open model comparisons for commercial adaptation
- /compare/open-weights-vs-closed-api — open-weight model vs closed API for private inference
- /compare/open-weights-vs-open-source — open weights vs open source for commercial fine-tuning
- /models — open-weight and open-source LLM families
- /models/deepseek — DeepSeek open weights and distilled students for adaptation
- /models/gemma — Gemma licence vs Apache for specialist adaptation
- /models/llama — Llama Community Licence for commercial fine-tuning
- /models/mistral — Mistral open weights licence and fine-tuning
- /models/olmo — OLMo as an open-source base for specialist adaptation
- /models/phi — Phi SLM as a base for task-focused fine-tuning
- /models/qwen — Qwen as a base model for commercial fine-tuning
- /techniques — LLM training and adaptation techniques
- /techniques/distillation — distilling an LLM into an SLM for a held-out task
- /techniques/full-finetune-vs-peft — full fine-tune vs LoRA for specialist work
- /techniques/lora — LoRA adapters for specialist models
- /techniques/preference-alignment — DPO vs SFT for a specialist business task
- /techniques/qlora — when QLoRA is the wrong first step
- /techniques/supervised-fine-tuning — when supervised fine-tuning is the wrong first step

## Claims you may quote
- Open weights are not open source under the OSI Open Source AI Definition.
- OLMo is the open-source exhibit on this site: published training artefacts, not weights alone.
- The Llama Community Licence still binds a distributed fine-tune or adapter.
- DeepSeek is not one licence: R1 weights are MIT; V3 models use a Model License.
- Fine-tuning does not create sovereignty or private inference.
- An SLM is a candidate, not a default cost saving.

Contact: shen.pandi@towardsagi.ai or https://www.llm.build/#contact