kshitizkhanal7
/

flan-t5-climate-qlora

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+tags:
+- climate-change
+- flan-t5
+- qlora
+- instruction-tuning
 ---
+# Model Card for FLAN-T5 Climate Action QLoRA
+This is a QLoRA-finetuned version of FLAN-T5 specifically trained for climate action content analysis and generation. The model is optimized for processing and analyzing text related to climate change, sustainability, and environmental policies.
 ## Model Details
 ### Model Description
+- **Developed by:** Kshitiz Khanal
+- **Shared by:** kshitizkhanal7
+- **Model type:** Instruction-tuned Language Model with QLoRA fine-tuning
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** google/flan-t5-base
+### Model Sources
+- **Repository:** https://huggingface.co/kshitizkhanal7/flan-t5-climate-qlora
+- **Training Data:** FineWeb dataset (climate action filtered)
 ## Uses
 ### Direct Use
+The model is designed for:
+- Analyzing climate policies and initiatives
+- Summarizing climate action documents
+- Answering questions about climate change and environmental policies
+- Evaluating sustainability measures
+- Processing climate-related research and reports
+### Downstream Use
+The model can be integrated into:
+- Climate policy analysis tools
+- Environmental reporting systems
+- Sustainability assessment frameworks
+- Climate research applications
+- Educational tools about climate change
 ### Out-of-Scope Use
+The model should not be used for:
+- Critical policy decisions without human oversight
+- Generation of climate misinformation
+- Technical climate science research without expert validation
+- Commercial deployment without proper testing
+- Medical or legal advice
 ## Bias, Risks, and Limitations
+- Limited to climate-related content analysis
+- May not perform well on general domain tasks
+- Potential biases from web-based training data
+- Should not be the sole source for critical decisions
+- Performance varies on technical climate science topics
 ### Recommendations
+- Always verify model outputs with authoritative sources
+- Use human expert oversight for critical applications
+- Consider the model as a supplementary tool, not a replacement for expert knowledge
+- Regular evaluation of outputs for potential biases
+- Use in conjunction with other data sources for comprehensive analysis
 ## Training Details
 ### Training Data
+- Source: FineWeb dataset filtered for climate content
+- Selection criteria: Climate-related keywords and quality metrics
+- Processing: Instruction-style formatting with climate focus
 ### Training Procedure
+#### Preprocessing
+- Text cleaning and normalization
+- Instruction templates for climate context
+- Maximum input length: 512 tokens
+- Maximum output length: 128 tokens
 #### Training Hyperparameters
+- Training regime: QLoRA 4-bit fine-tuning
+- Epochs: 3
+- Learning rate: 2e-4
+- Batch size: 4
+- Gradient accumulation steps: 4
+- LoRA rank: 16
+- LoRA alpha: 32
+- Target modules: Query and Value matrices
+- LoRA dropout: 0.05
 ## Environmental Impact
+- **Hardware Type:** Single GPU
+- **Hours used:** ~4 hours
+- **Cloud Provider:** Local
+- **Carbon Emitted:** Minimal due to QLoRA efficiency
+## Technical Specifications
 ### Model Architecture and Objective
+- Base architecture: FLAN-T5
+- Objective: Climate-specific text analysis
+- QLoRA adaptation for efficient fine-tuning
+- 4-bit quantization for reduced memory usage
 ### Compute Infrastructure
+- Python 3.8+
+- PyTorch
+- Transformers library
+- bitsandbytes for quantization
+- PEFT for LoRA implementation
+### Hardware
+Minimum requirements:
+- 16GB GPU memory for inference
+- 24GB GPU memory recommended for training
+- CPU inference possible but slower
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{khanal2024climate,
+  title={FLAN-T5 Climate Action QLoRA},
+  author={Khanal, Kshitiz},
+  year={2024},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/kshitizkhanal7/flan-t5-climate-qlora}}
+}