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--- |
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language: |
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- en |
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tags: |
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- transformers |
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- llama |
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- qgis |
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- geospatial |
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- instruction-following |
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- conversational |
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license: apache-2.0 |
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datasets: |
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- custom-qgis-dataset |
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base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit |
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library_name: transformers |
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inference: true |
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quantized: true |
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--- |
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# Model Card: Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct |
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## Overview |
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**Model Name**: `Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct` |
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**Developer**: `boadisamson` |
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**Base Model**: `unsloth/llama-3.2-3b-instruct-bnb-4bit` |
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**License**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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**Primary Use Case**: QGIS-related tasks, conversational applications, and instruction-following in English. |
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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 |
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- **Domain-Specific Expertise**: Trained on QGIS-specific tasks, making it ideal for geospatial workflows. |
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- **Instruction Following**: Excels in providing clear, step-by-step guidance for GIS-related queries. |
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- **Optimized Performance**: Fine-tuned with 4-bit quantization (`bnb-4bit`) for faster performance and reduced memory requirements. |
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- **Conversational Abilities**: Suitable for interactive, conversational applications related to GIS. |
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--- |
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## Technical Specifications |
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- **Model Architecture**: LLaMA-based (3 billion parameters). |
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- **Frameworks Used**: Transformers, GGUF, and Hugging Face TRL library. |
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- **Quantization**: Q4_K_M (4-bit quantization for efficient memory usage). |
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- **Language**: English. |
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## Training Details |
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This model was trained using: |
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- **Fine-Tuning**: Utilized the Hugging Face TRL library for efficient instruction-based adaptation. |
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- **Acceleration**: Achieved 2x faster training through Unsloth optimizations. |
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- **Dataset**: Tailored datasets for QGIS-related queries, workflows, and instructional scenarios. |
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## Use Cases |
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- **Geospatial Analysis**: Answering GIS-related questions and offering guidance on geospatial workflows. |
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- **QGIS Tutorials**: Providing step-by-step instructions for beginners and advanced users. |
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- **Conversational Applications**: Supporting natural dialogue for instructional and technical purposes. |
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## Inference |
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This model is compatible with: |
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- **Hugging Face Inference Endpoints**: For seamless deployment and scalable use. |
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- **Text-Generation-Inference**: Efficient handling of input queries. |
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- **GGUF Format**: Optimized for low-latency, high-performance inference. |
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## How to Use |
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Load the model using Hugging Face’s `transformers` library: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("boadisamson/Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("boadisamson/Llama-3.2-3B-Qgis-update1-q4_k_m-Instruct", device_map="auto") |
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``` |
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Generate text: |
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```python |
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input_text = "How do I add a layer in QGIS?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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--- |
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## Limitations |
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- **Domain-Specific Focus**: While optimized for QGIS tasks, performance may degrade on unrelated topics. |
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- **Resource Constraints**: Despite 4-bit quantization, larger contexts or prolonged sessions may require more resources. |
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--- |
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## Acknowledgments |
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- Base model: `unsloth/llama-3.2-3b-instruct-bnb-4bit`. |
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- Training accelerations provided by Unsloth and Hugging Face TRL library. |
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For questions or suggestions, contact `boadisamson` on Hugging Face. |
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