Instructions to use kumarprince070107/geobot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use kumarprince070107/geobot 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 kumarprince070107/geobot 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 kumarprince070107/geobot to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kumarprince070107/geobot to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="kumarprince070107/geobot", max_seq_length=2048, )
🗺️ Llama-3.1-8B-GIS-Expert
This model is a fine-tuned version of Llama-3.1-8B-Instruct, specialized for Geospatial Analysis (GIS).
🧠 Capabilities
It has been trained on a multi-task dataset to handle three specific roles:
- PostGIS Expert: Converts natural language questions into valid
PostgreSQL/PostGISSQL queries. - Python GIS Developer: Writes Python scripts using
geopandas,shapely, andrasterio. - Geospatial Analyst: Explains spatial relationships and topology logic.
💻 How to Use
System Prompts (Crucial)
To get the best results, you must use the correct System Prompt for the task:
- For SQL: "You are a PostGIS expert. Convert the question into a SQL query."
- For Python: "You are a Python GIS developer. Write a script to solve the geospatial problem."
- For Reasoning: "You are a Geospatial Analyst. Explain the spatial relationship."
Example Code
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "kumarprince070107/Llama-3.1-8B-GIS-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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