Fine-Tuning a Large Language Model for Retro-Style Documentation
A recent project demonstrates how to fine-tune a large language model (LLM) to generate technical documentation reminiscent of 1990s style guides. This creative application showcases the versatility of LLMs in adopting specific stylistic conventions beyond standard text generation, offering a guide for developers interested in custom model behaviors.
Why it matters
Learn practical fine-tuning techniques to imbue LLMs with unique stylistic outputs, useful for branding, creative content, or specialized document generation.
TL;DR
- 01Fine-tuning allows LLMs to adopt specific stylistic outputs.
- 02Curate a targeted dataset to teach custom writing styles.
- 03Apply this to branding, creative content, or specialized documentation.
Key facts
- Training Corpus Size
- 37+ million words (Bitsavers Microsoft Collection)
- Dataset Sample Count
- 192,456 JSONL examples
- Compute Hardware
- Nvidia B200 (192GB memory) via Runpod
- Total Fine-tuning Cost
- $50 ($8 for pre-cleaning, <$6/hour B200)
Building the 1990s Corpus
To emulate the style of late-20th-century software manuals, the researcher utilized Bitsavers' Microsoft collection, a repository of out-of-print documentation published between 1977 and 2005 containing over 37 million words. After raw Python script cleaning, a cheap classification pass using gemma-4-26b via OpenRouter processed each paragraph into a 'keep' or 'drop' designation, costing approximately $8. The final dataset comprised 192,456 paragraph-aligned JSONL training examples capped at 512 tokens.
Fine-Tuning with QLoRA on Runpod
Rather than retraining from scratch, the author executed QLoRA (Quantized Low-Rank Adaptation) on Nvidia B200 GPUs (192GB memory) leased via Runpod for less than $6 per hour. The model configurations tested included Llama 3.1 8B Instruct and Qwen 2.5 7B Instruct. The entire training run across several configurations cost $50 and took roughly one day. Adapters were then converted to GGUF format for local inference inside Ollama.
Benchmark and Style Success
The models were evaluated on three test prompts: documenting the C function malloc(), documenting a fictitious ConnectWifi() Win32 function, and describing a REST API in 1990s style. Unmodified models outputted modern README-style Markdown, whereas the fine-tuned versions successfully generated historical 'Synopsis' sections and maintained the documentation style of early Windows SDKs.
Try it in 2 minutes
ollama run qwen2.5-7b-retro-docsbash
✓ When to use
- Stylistic adaptations requiring a specific voice or persona (e.g., brand-specific documentation)
- Fine-tuning relatively small open models (7B/8B) on a local GPU or Runpod
✕ When NOT to use
- Scenarios where factual retrieval accuracy is paramount (use RAG instead)
- High-cost real-time low-latency requirements without GGUF optimizations
What the community says
“Out of curiosity i just tried having the LLM generate a document describing the style, then (after a memory reset) i asked it to use the document and it did more or less the same job”