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

Libraries

How to use developmentseed/gazet-model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="developmentseed/gazet-model",
	filename="models/ckpt-q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use developmentseed/gazet-model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf developmentseed/gazet-model:Q8_0
# Run inference directly in the terminal:
llama-cli -hf developmentseed/gazet-model:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf developmentseed/gazet-model:Q8_0
# Run inference directly in the terminal:
llama-cli -hf developmentseed/gazet-model: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 developmentseed/gazet-model:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf developmentseed/gazet-model: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 developmentseed/gazet-model:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf developmentseed/gazet-model:Q8_0

Use Docker

docker model run hf.co/developmentseed/gazet-model:Q8_0

LM Studio
Jan

vLLM

How to use developmentseed/gazet-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/developmentseed/gazet-model:Q8_0

Ollama
How to use developmentseed/gazet-model with Ollama:
```
ollama run hf.co/developmentseed/gazet-model:Q8_0
```

Unsloth Studio new

How to use developmentseed/gazet-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 developmentseed/gazet-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 developmentseed/gazet-model to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use developmentseed/gazet-model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf developmentseed/gazet-model:Q8_0

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": "developmentseed/gazet-model:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use developmentseed/gazet-model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf developmentseed/gazet-model: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 developmentseed/gazet-model:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use developmentseed/gazet-model with Docker Model Runner:
```
docker model run hf.co/developmentseed/gazet-model:Q8_0
```

Lemonade

How to use developmentseed/gazet-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull developmentseed/gazet-model:Q8_0

Run and chat with the model

lemonade run user.gazet-model-Q8_0

List all available models

lemonade list

gazet-model

File size: 1,758 Bytes

---
license: mit
datasets:
- developmentseed/gazet-dataset
language:
- en
base_model:
- Qwen/Qwen3.5-0.8B
pipeline_tag: text-generation
---

# Gazet Model

LoRA-finetuned [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) for natural-language geocoding over [Overture Maps](https://overturemaps.org/) and [Natural Earth](https://www.naturalearthdata.com/) parquet datasets.

Two tasks:
- **Place extraction**: Given a user query, extract structured place names with optional country codes and subtypes
- **Text-to-SQL**: Given a user query and fuzzy-matched candidate entities, generate a DuckDB spatial SQL query

## Files

| File | Description |
|---|---|
| [ckpt-q8_0.gguf](https://huggingface.co/developmentseed/gazet-model/resolve/main/models/ckpt-q8_0.gguf) | Q8_0 quantized GGUF (812 MB), ready for llama-server |
| [merged/](https://huggingface.co/developmentseed/gazet-model/tree/main/merged) | Full merged safetensors (for re-quantization or further finetuning) |

## Usage

Serve with [llama-server](https://github.com/ggml-org/llama.cpp):

```bash
# Download
hf download developmentseed/gazet-model ckpt-q8_0.gguf

# Serve
llama-server -m ckpt-q8_0.gguf -ngl 99 --port 9000 --ctx-size 2048
```

The model exposes `/v1/chat/completions` on port 9000.

Or use with the full gazet stack via Docker Compose (see [gazet](github.com/developmentseed/gazet) repo).

## Training
```yaml
Base model: unsloth/Qwen3.5-0.8B
Method: LoRA (r=16, alpha=32) via Unsloth
Data: developmentseed/gazet-dataset
Hardware: Single H200 on Modal (~2 hrs/epoch)
Optimizer: AdamW 8-bit, lr=1e-4, linear schedule
Max sequence length: 2048
Loss: Train on assistant responses only (Unsloth train_on_responses_only)
Full training code: github.com/developmentseed/gazet
```