Instructions to use olamide226/ofin-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use olamide226/ofin-model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="olamide226/ofin-model",
	filename="ofin-model.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use olamide226/ofin-model with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf olamide226/ofin-model
# Run inference directly in the terminal:
llama cli -hf olamide226/ofin-model

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf olamide226/ofin-model
# Run inference directly in the terminal:
llama cli -hf olamide226/ofin-model

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf olamide226/ofin-model
# Run inference directly in the terminal:
./llama-cli -hf olamide226/ofin-model

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf olamide226/ofin-model
# Run inference directly in the terminal:
./build/bin/llama-cli -hf olamide226/ofin-model

Use Docker

docker model run hf.co/olamide226/ofin-model

LM Studio
Jan
Ollama
How to use olamide226/ofin-model with Ollama:
```
ollama run hf.co/olamide226/ofin-model
```

Unsloth Studio

How to use olamide226/ofin-model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for olamide226/ofin-model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for olamide226/ofin-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for olamide226/ofin-model to start chatting

How to use olamide226/ofin-model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf olamide226/ofin-model

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "olamide226/ofin-model"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use olamide226/ofin-model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf olamide226/ofin-model

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default olamide226/ofin-model

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use olamide226/ofin-model with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf olamide226/ofin-model

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "olamide226/ofin-model" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use olamide226/ofin-model with Docker Model Runner:
```
docker model run hf.co/olamide226/ofin-model
```

Lemonade

How to use olamide226/ofin-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull olamide226/ofin-model

Run and chat with the model

lemonade run user.ofin-model-{{QUANT_TAG}}

List all available models

lemonade list

ofin-model / README.md

olamide226

Upload README.md with huggingface_hub

441db3a verified 9 days ago

preview code

Raw

History Blame Contribute Delete

3.83 kB

	---
	license: llama3.2
	language:
	- en
	- pcm
	tags:
	- legal
	- nigeria
	- llama.cpp
	- GGUF
	- offline
	- chat-template
	- pidgin
	base_model: bartowski/Llama-3.2-3B-Instruct-GGUF
	---

	# Òfin — Offline Nigerian Legal Companion

	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](https://github.com/olamide226/ofin) system built for
	the Africa Deep Tech Challenge 2026 (Laptop LLM track, corporate enterprise).

	## What makes this model different

	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:

	- Nigerian legal framing (labour, tenancy, and tax law)
	- Nigerian Pidgin capability (matches the question language)
	- Statutory citation format (`[Act Name, s.X(Y)]`)
	- Scope-awareness (refuses questions outside its three domains)

	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.

	## How the bake works

	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.

	## Quick start

	```bash
	# 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?"
	```

	## Model details

	\| 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 \|

	## Limitations (honest)

	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.

	## Part of the Òfin system

	- Repo: [github.com/olamide226/ofin](https://github.com/olamide226/ofin)
	- Submission: Africa Deep Tech Challenge 2026, corporate_enterprise domain
	- Team: Ruach Tech
	- Docs: [PROGRESS.md](https://github.com/olamide226/ofin/blob/main/docs/PROGRESS.md) · [DECISIONS.md](https://github.com/olamide226/ofin/blob/main/docs/DECISIONS.md)