How to use from
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
Quick Links

Gazet Model

LoRA-finetuned Qwen3.5-0.8B for natural-language geocoding over Overture Maps and Natural Earth 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 Q8_0 quantized GGUF (812 MB), ready for llama-server
merged/ Full merged safetensors (for re-quantization or further finetuning)

Usage

Serve with llama-server:

# 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 repo).

Training

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
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GGUF
Model size
0.8B params
Architecture
qwen35
Hardware compatibility
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8-bit

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