Instructions to use developmentseed/gazet-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
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_0Use 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_0Build 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_0Use Docker
docker model run hf.co/developmentseed/gazet-model:Q8_0Gazet 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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Install from brew
# 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