AI on homelab

Wiki Pipeline Scripts

Sat, 02 May 2026 00:00:00 +0000

Wiki Pipeline Scripts#

What Was Established#

Eight Python scripts in /opt/wiki/homelab/scripts/ implement the full wiki pipeline: file conversion, document ingestion, conversation crystallization (standard, DeepSeek, and Claude formats), shared LLM infrastructure, wiki health-checking, and knowledge-graph integration. All scripts were ported from the work wiki pipeline (itself developed 2026-04-21 → 2026-04-26) with homelab-specific infrastructure baked in.

crystallize.py (Claude format) uses a two-step LLM approach: gemma4:e2b cleans, qwen3.6:35b crystallizes. crystallize_deepseek.py skips gemma — JSON parsing is handled deterministically in Python (load_conversation + _clean_text), so only qwen is needed.

Wiki System - Architecture

Sat, 02 May 2026 00:00:00 +0000

Wiki System - Architecture#

What Was Established#

The wiki system is designed around the LLM wiki pattern (Karpathy): raw sources (chat transcripts, notes, docs) are crystallized into structured markdown pages, embedded into pgvector, and retrieved semantically by agents in future sessions. A dedicated LXC (nk-wiki) will host the wiki VM, separating wiki infrastructure from other services.

Multi-Wiki Namespace Design#

Three wikis are planned, each with its own namespace in pgvector:

Local Model Training & Fine-Tuning Guide

Thu, 23 Apr 2026 00:00:00 +0000

Local Model Training & Fine-Tuning Guide#

What Was Established#

Guide for fine-tuning local LLMs (DeepSeek) using Hugging Face transformers, with emphasis on VRAM-efficient techniques for single-GPU setups.

Key Decisions#

Framework: Hugging Face transformers + Trainer API for fine-tuning
Model: deepseek-ai/deepseek-llm-7b (example model)
Efficiency: LoRA (Low-Rank Adaptation) + 4-bit quantization via bitsandbytes to fit large models on consumer GPUs

Setup#

pip install torch transformers datasets accelerate peft bitsandbytes

Verify GPU: nvidia-smi — need CUDA 11.8+.

Troubleshooting DeepSeek Language Switching

Tue, 25 Mar 2025 00:00:00 +0000

Troubleshooting DeepSeek Language Switching#

What Was Established#

Local DeepSeek models may intermittently switch from English to Chinese mid-response. This is typically caused by training bias (heavy Chinese dataset influence), loss of context during long conversations, or mixed-language input prompts.

Key Decisions#

To maintain English-only responses, the following parameters and prompting strategies should be applied:

Explicit Instruction: Always include a system-level or initial prompt instruction to respond exclusively in English.
Temperature Control: Use lower temperature settings (e.g., 0.3) to make the model more deterministic and less likely to drift.
Repetition Penalty: Implement a repetition_penalty (e.g., 1.2) to discourage the model from falling into repetitive patterns that might trigger language switching.

Current Configuration#

System Message Pattern#

When using APIs or local inference engines that support system roles: