Model Card for Meta-Llama-3.1-8B-climate-expert

Model Details

Intended Use

• Instruction-following in climate science contexts.
• Generating informative, evidence-based responses to user queries about climate change.
• Educational and research purposes, such as analysing climate-related arguments or claims.

Training Details

Data

• Training Data:
  • climate_argumentation_patterns.jsonl (custom dataset of climate-related claims and responses, derived from the ClimateFever dataset).
  • ClimateFever: A dataset of 1,535 real-world claims about climate change, each annotated with evidence from Wikipedia. This dataset was used as a foundation for identifying argumentation patterns via AI classification, which were then incorporated into the training data:refs[1-user]-provided.
  • evaluation_claims.jsonl (custom evaluation set).
• Data Format: Instruction-tuning format with system, user, and assistant roles.

Limitations

• Bias: The model may reflect biases present in the training data. It is fine-tuned on a specific dataset and may not generalise well to all climate-related topics.
• Knowledge Cutoff: Limited to the knowledge of the base model (Meta-Llama-3.1-8B) and the fine-tuning data (2026).
• Quantization Artifacts: 8-bit quantisation may introduce minor performance trade-offs compared to full precision.
• Context Window: Limited to 256 tokens, which may truncate longer conversations or complex queries.

Ethical Considerations

• Misinformation Risk: Always verify the model’s outputs with authoritative sources.
• Bias and Fairness: The model’s responses should be critically evaluated for fairness and accuracy, especially in sensitive contexts.
• Environmental Impact: Fine-tuning large models consumes significant computational resources.