---
language: 
  - en
tags: 
  - transformers
  - llama
  - qgis
  - geospatial
  - instruction-following
  - conversational
license: apache-2.0
datasets:
  - custom-qgis-dataset
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
library_name: transformers
inference: true
quantized: true
---


# Model Card: Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct

## Overview

**Model Name**: `Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct`  
**Developer**: `boadisamson`  
**Base Model**: `unsloth/llama-3.2-3b-instruct-bnb-4bit`  
**License**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)  
**Primary Use Case**: QGIS-related tasks, conversational applications, and instruction-following in English.  

This model is fine-tuned for QGIS workflows, geospatial data handling, and instructional conversational capabilities. Optimized using the Hugging Face TRL library and accelerated by Unsloth, it achieves efficient inference while maintaining high-quality responses.

---

## Key Features

- **Domain-Specific Expertise**: Trained on QGIS-specific tasks, making it ideal for geospatial workflows.
- **Instruction Following**: Excels in providing clear, step-by-step guidance for GIS-related queries.
- **Optimized Performance**: Fine-tuned with 4-bit quantization (`bnb-4bit`) for faster performance and reduced memory requirements.
- **Conversational Abilities**: Suitable for interactive, conversational applications related to GIS.

---

## Technical Specifications

- **Model Architecture**: LLaMA-based (3 billion parameters).  
- **Frameworks Used**: Transformers, GGUF, and Hugging Face TRL library.  
- **Quantization**: Q4_K_M (4-bit quantization for efficient memory usage).  
- **Language**: English.

---

## Training Details

This model was trained using:

- **Fine-Tuning**: Utilized the Hugging Face TRL library for efficient instruction-based adaptation.
- **Acceleration**: Achieved 2x faster training through Unsloth optimizations.
- **Dataset**: Tailored datasets for QGIS-related queries, workflows, and instructional scenarios.

---

## Use Cases

- **Geospatial Analysis**: Answering GIS-related questions and offering guidance on geospatial workflows.  
- **QGIS Tutorials**: Providing step-by-step instructions for beginners and advanced users.  
- **Conversational Applications**: Supporting natural dialogue for instructional and technical purposes.

---

## Inference

This model is compatible with:

- **Hugging Face Inference Endpoints**: For seamless deployment and scalable use.  
- **Text-Generation-Inference**: Efficient handling of input queries.  
- **GGUF Format**: Optimized for low-latency, high-performance inference.

---

## How to Use

Load the model using Hugging Face’s `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("boadisamson/Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct")
model = AutoModelForCausalLM.from_pretrained("boadisamson/Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct", device_map="auto")
```

Generate text:

```python
input_text = "How do I add a layer in QGIS?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))
```

---

## Limitations

- **Domain-Specific Focus**: While optimized for QGIS tasks, performance may degrade on unrelated topics.  
- **Resource Constraints**: Despite 4-bit quantization, larger contexts or prolonged sessions may require more resources.  

---

## Acknowledgments

- Base model: `unsloth/llama-3.2-3b-instruct-bnb-4bit`.  
- Training accelerations provided by Unsloth and Hugging Face TRL library.  

For questions or suggestions, contact `boadisamson` on Hugging Face.