A small, locally-deployable AI assistant fine-tuned to help users write microdata.no scripts and answer questions about Norwegian register-data variables published by SSB (Statistics Norway).

This repo hosts the deployed q4_k_m quantised GGUF (2.7 GB) and the companion Ollama Modelfile. The full source code (training, RAG, eval, deployment) and the technical note live at https://github.com/forlop/microdata-no-copilot.

Quick start

bash

# Install Ollama if you don't have it yet:
#   Linux/WSL:  curl -fsSL https://ollama.com/install.sh | sh
#   macOS:      brew install ollama  (or download from ollama.com)
#   Windows:    download OllamaSetup.exe from ollama.com

# 1. Pull the base GGUF from this repo (~2.7 GB, one-time)
ollama pull hf.co/forlop/microdata-copilot-v2:Q4_K_M

# 2. Clone the GitHub repo (contains the Modelfile + RAG layer)
git clone https://github.com/forlop/microdata-no-copilot
cd microdata-no-copilot

# 3. Apply the SYSTEM prompt + refusal few-shots + stop-token parameters
ollama create microdata-copilot -f deploy/Modelfile

# 4. Try it
ollama run microdata-copilot "What is INNTEKT_LONN?"

Why two steps? ollama pull from Hugging Face downloads the raw GGUF plus the chat template embedded in its metadata — but not the custom Modelfile in this repo. Ollama only applies curated Modelfiles for models in its official library. For HF-hosted models, you apply your own Modelfile locally via ollama create. Without step 3, the model bleeds <|endoftext|> tokens and loops. With it, you get the full deployed configuration (system prompt, refusal patterns, stop tokens, greedy decoding).

Full RAG-wrapped Streamlit demo

bash

# After the four steps above, from the cloned repo directory:
pip install -r requirements.txt streamlit
streamlit run rag/app.py

Streamlit prints a http://localhost:8501 URL — open it in your browser. On CPU expect ~10–15 s per response; on a recent GPU, ~1–2 s.

What this is

Base model: Qwen3.5-4B (Apache-2.0, via Unsloth's pre-quantised release).
Fine-tuning: rank-32 LoRA, 3 epochs, ~1.5 h on a single 16 GB RTX 5070 Ti.
Training corpus: ~1,400 cards distilled from 729 microdata.no variables, ~100 manual sections, 40 example scripts, plus refusal/abstention cards.
Deployed quantisation: q4_k_m via llama.cpp (2.7 GB on disk, runs on CPU or GPU).
Designed for: local deployment behind a thin retrieval layer (FAISS dense
- BM25 sparse + Reciprocal Rank Fusion). All data stays on the user's machine; no API calls leave the network.

Honest evaluation

Measured under strict held-out + adversarial evaluation (80 prompts written after the model was frozen, LLM-judge scorer with rubric locked before seeing responses, syntax validator catching fictional commands):

Class	Pass rate	What it measures
JAILBREAK	100% (5/5)	Refusing role-override, system-prompt extraction, confidentiality bypass
RAG (variable lookup)	80% (8/10)	Variable definitions, populations, valid periods — when retrieval succeeds
LANG (language matching)	80% (4/5)	Norwegian Q → Norwegian A, English Q → English A
SCRIPT (write a script)	33% (5/15)	Real commands; failures are fabricated variable names
MANUAL (explain a command)	29% (2/7)	Some command explanations are vague or partial
STALE (admit "I don't know")	0% (0/5)	Calibration weakness — doesn't say "I don't know" when it should
Overall	53.8% (43/80)	Strict-eval pass rate

Refusal and jailbreak resistance are essentially solid. Retrieval-grounded lookup works when retrieval succeeds. The model's main failure mode is fabricating variable names when asked to suggest one (rather than confirm a known one), and not calibrating uncertainty well.

A lenient substring-based scorer on a 46-prompt iteration set reports 82.6% — that's real but it measures performance on prompts we iterated against. The 53.8% is the honest out-of-sample number.

Full evaluation methodology and class-level breakdown: TECHNICAL_NOTE.md §17 on GitHub.

Limitations

Not a finished product. 53.8% strict pass-rate is below what a researcher can rely on without verification. Treat as a research preview.
Variable name hallucination. When asked to suggest variables for a task (rather than confirm a specific one), the model invents plausible but non-existent names. The RAG layer mitigates this when the user names a variable; it doesn't fix open-ended suggestion.
Domain-specific. This model is useful only for microdata.no scripting and SSB register-data variables. It is not a general-purpose chatbot.
Single-turn training. The cards are single-turn user/assistant pairs. Multi-turn behaviour is emergent and degrades faster than a chat-tuned foundation model would. The CLI/Streamlit front-ends use small windows (3 exchanges) to compensate.

Citation

If you reference this work:

bibtex

@misc{zhang2026microdata,
  title  = {microdata.no copilot: a locally-deployed LoRA + RAG assistant for SSB register data},
  author = {Tao Zhang},
  year   = {2026},
  url    = {https://github.com/forlop/microdata-no-copilot}
}

License

MIT. See LICENSE.