Instructions to use sazid63/archresidencebd-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sazid63/archresidencebd-models 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 sazid63/archresidencebd-models:Q8_0
# Run inference directly in the terminal:
llama cli -hf sazid63/archresidencebd-models:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sazid63/archresidencebd-models:Q8_0
# Run inference directly in the terminal:
llama cli -hf sazid63/archresidencebd-models:Q8_0

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 sazid63/archresidencebd-models:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf sazid63/archresidencebd-models:Q8_0

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 sazid63/archresidencebd-models:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sazid63/archresidencebd-models:Q8_0

Use Docker

docker model run hf.co/sazid63/archresidencebd-models:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use sazid63/archresidencebd-models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sazid63/archresidencebd-models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sazid63/archresidencebd-models",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sazid63/archresidencebd-models:Q8_0

Ollama
How to use sazid63/archresidencebd-models with Ollama:
```
ollama run hf.co/sazid63/archresidencebd-models:Q8_0
```
Unsloth Desktop

How to use sazid63/archresidencebd-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sazid63/archresidencebd-models:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sazid63/archresidencebd-models:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sazid63/archresidencebd-models with Docker Model Runner:
```
docker model run hf.co/sazid63/archresidencebd-models:Q8_0
```

Lemonade

How to use sazid63/archresidencebd-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sazid63/archresidencebd-models:Q8_0

Run and chat with the model

lemonade run user.archresidencebd-models-Q8_0

List all available models

lemonade list

Hermes Agent

How to use sazid63/archresidencebd-models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sazid63/archresidencebd-models:Q8_0

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 sazid63/archresidencebd-models:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sazid63/archresidencebd-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sazid63/archresidencebd-models:Q8_0

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 "sazid63/archresidencebd-models:Q8_0" \
  --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"

ArchResidenceBD Model Fleet

This repository contains the model fleet for ArchResidenceBD - an AI civil engineer for Bangladeshi residential buildings.

Models

Model	Role	Base	Quantization	Size	Purpose
rchie-chat-v0-Q4_K_M.gguf	Chat	Qwen3-4B-Instruct-2507	Q4_K_M	2.33 GB	Interview, parsing, explanations
rchres-repair-v0-Q4_K_M.gguf	Repair	Qwen2.5-Coder-3B-Instruct	Q4_K_M	1.80 GB	CAD script repair
Qwen3-Embedding-0.6B-Q8_0.gguf	Embedding	Qwen3-Embedding-0.6B	Q8_0	0.60 GB	RAG retrieval
Qwen3-Reranker-0.6B-Q8_0.gguf	Reranker	Qwen3-Reranker-0.6B	Q8_0	0.60 GB	RAG reranking

Usage

All models are in GGUF format, quantized for llama.cpp.

`ash

Chat model

llama-server -m archie-chat-v0-Q4_K_M.gguf -ngl 99 -c 8192

Repair model

llama-server -m archres-repair-v0-Q4_K_M.gguf -ngl 99 -c 8192

Embedding

llama-server -m Qwen3-Embedding-0.6B-Q8_0.gguf -ngl 0 --embeddings --pooling last

Reranker

llama-server -m Qwen3-Reranker-0.6B-Q8_0.gguf -ngl 0 --reranking `

Licenses

Base models: Apache-2.0 (Qwen family)
Fine-tunes: Apache-2.0 (derivative)

Project

Part of ArchResidenceBD - AI Civil Engineer for Bangladeshi Residential Buildings.

Citation

ibtex @misc{archresidencebd-models, title = {ArchResidenceBD Model Fleet}, author = {ArchResidenceBD Team}, year = {2026}, url = {https://huggingface.co/sazid63/archresidencebd-models} }

Downloads last month: 154

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit