Create your Own Private LLM : Steps

Creating a private LLM (Large Language Model) involves a number of technical and strategic steps, depending on your goals (e.g., fine-tuning an existing model vs. training from scratch) and constraints (e.g., budget, privacy needs). Here's a step-by-step guide tailored to building a private LLM — meaning one that runs on your infrastructure, with your data, and no outside access.

 

STEP 1: DEFINE THE SCOPE

Decide:

• Purpose (chatbot, summarization, code generation, etc.)
• Model size (e.g., 1B, 7B, 13B parameters)
• Data privacy level (air-gapped, self-hosted, etc.)
• Budget & hardware constraints

 

STEP 2: CHOOSE A BASE MODEL (OR NOT)

Options:

• Fine-tune an existing open-source model (Recommended)
• E.g., LLaMA 3, Mistral, Falcon, Gemma
• Train from scratch (Only if you have millions of $$ and data)
• Use parameter-efficient tuning (PEFT) like LoRA or QLoRA

Recommended base models:

Model	Size	Notes
LLaMA 3	8B, 70B	Best quality (Meta, requires request access)
Mistral	7B	Apache 2.0 licensed, good performance
Phi-3	3.8B	Small, efficient
Gemma	2B, 7B	Good small model, Google-backed
Falcon	7B, 40B	Good for Arabic and multilingual use cases

 

STEP 3: GATHER & PREPARE TRAINING DATA

Private use case:

• Use internal documents, chat logs, customer queries, codebases
• Ensure PII is handled properly (remove or mask sensitive data)

Preprocess:

• Clean formatting, remove duplicates, convert to plain text/JSON
• Tokenize using model's tokenizer
• Optional: Use cleanlab or argilla to curate data

 

STEP 4: TRAIN OR FINE-TUNE THE MODEL

Toolkits:

• Transformers + PEFT (HuggingFace + LoRA/QLoRA)
• Axolotl — easy fine-tuning framework for open LLMs
• DeepSpeed, FSDP — for large-scale distributed training

Training methods:

Method	Cost	Notes
LoRA / QLoRA	Low	Add-on layers, very efficient
Full fine-tune	High	More control, requires more compute
RAG (optional)	Medium	Retrieval-Augmented Generation; no training required

Hardware:

• At least 1x A100 (40GB+) or 4x 3090s for 7B models
• Use Lambda Labs, RunPod, Paperspace, or local GPU servers

 

STEP 5: DEPLOY THE MODEL PRIVATELY

Self-hosted options:

• vLLM — optimized serving of LLMs
• Text Generation Inference (TGI) — Hugging Face inference server
• Ollama — easiest local deployment (for Mac/Linux)
• LMDeploy, Triton, or custom FastAPI wrappers

Containerize:

• Use Docker + Kubernetes if scaling is needed
• Use NVIDIA Triton for high-efficiency serving

 

STEP 6: SECURE THE DEPLOYMENT

• Run air-gapped if high security required
• Add authentication (e.g., API keys, OAuth)
• Limit rate of access to avoid overload
• Log inputs/outputs for auditing, not data collection

 

STEP 7: EVALUATE & IMPROVE

Evaluate on:

• Accuracy, helpfulness, toxicity, bias
• Use OpenLLM Leaderboard, lm-evaluation-harness
• Add feedback loops for RLHF or continual fine-tuning

 

OPTIONAL: ADD RETRIEVAL OR TOOLS

• Add a RAG system using:
• LangChain, LlamaIndex, Haystack
• Vector DBs: Chroma, FAISS, Weaviate, Qdrant
• Connect to tools: databases, web APIs, internal system