Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct

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
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.

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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.

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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.

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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.

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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.

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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.

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How to Use

Load the model using Hugging Face’s transformers library:

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:

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]))

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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.

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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.