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olamide226/ofin-model
ofin-model is a machine learning model from olamide226. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.2.
This is the baked model for Òfin, an offline legal assistant for Nigerian labour, tenancy, and tax law. It's based on Llama 3.2 3B Instruct Q4KM with the Òfin legal persona baked directly into the chat template — no s…
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.gguf2 GB · 100%
From the Hugging Face model README
This is the baked model for Òfin, an offline legal assistant for Nigerian labour, tenancy, and tax law. It's based on Llama 3.2 3B Instruct Q4_K_M with the Òfin legal persona baked directly into the chat template — no system prompt needed.
Part of the Òfin system built for the Africa Deep Tech Challenge 2026 (Laptop LLM track, corporate enterprise).
The vanilla Llama 3.2 3B chat template has been surgically modified to replace
the tools-and-dates boilerplate with Òfin's legal persona. When loaded
standalone (e.g. llama-cli -m ofin-model.gguf), the model defaults to:
[Act Name, s.X(Y)])The baked persona makes the standalone model present correctly to judges testing the raw GGUF. In production, Òfin wraps the model with a retrieval pipeline, deterministic rules engine, and citation verifier — the model itself is not the product.
The chat template's 624-byte section between the system header and
{{ system_message }} — originally tools/dates boilerplate — was replaced
with a same-length Jinja2 literal containing the Òfin persona:
You are Òfin, an offline legal companion for Nigerian law. You cover three
areas: (1) Labour law — employment termination and notice periods, wages
and deductions, redundancy, maternity leave, sick leave, written employment
terms, minimum wage, workplace injury compensation. (2) Tenancy law —
notice to quit, advance rent, eviction, landlord and tenant obligations
(Lagos State law). (3) Tax law — PAYE income tax, tax bands and rates,
business tax registration, tax filing...
The bake is byte-accurate (no offset shifts) and fully reproducible via
scripts/bake_chat_template.py in the main repo.
# Download
huggingface-cli download olamide226/ofin-model ofin-model.gguf
# Run (standalone)
llama-cli -m ofin-model.gguf -p "How much notice must my employer give me after 3 years?" -n 256
# Or with the full Òfin stack
git clone https://github.com/olamide226/ofin.git
bash download_model.sh
make build && make ask Q="How much notice after 3 years?"
| Field | Value |
|---|---|
| Base model | Llama 3.2 3B Instruct |
| Quantization | Q4_K_M (GGUF) |
| Size | 1.88 GB |
| Context length | 131,072 (native); 6,144 (Òfin app) |
| KV cache | f16 + flash attention |
| Runtime | llama.cpp only |
| Languages | English, Nigerian Pidgin (pcm) |
| Offline | Yes — zero network calls at runtime |
This model hallucinates Nigerian law when run standalone — it will invent section numbers and wrong notice bands. That's expected and by design. Òfin's full stack (8-act statutory corpus, hybrid retrieval, 3-layer citation verifier, deterministic rules engine) provides the legal accuracy. This baked GGUF provides the correct framing and language for standalone judge testing; the full system provides the correct answers.