Model Card for ministral-civil-war-risk-reason

Model Summary

ministral-civil-war-risk-reason is a domain-adapted language model intended to analyze civil war risk factors and produce structured reasoning about conflict escalation. The model is designed for research, experimentation, and decision-support style analysis in geopolitical and conflict-risk workflows.

This model is best understood as a specialized analytical assistant rather than a general-purpose forecasting engine. Its value is in synthesizing relevant indicators, explaining risk drivers, and supporting human interpretation of conflict-related scenarios.

Intended Use

Primary use cases:

• Civil war risk analysis
• Geopolitical reasoning and explanation
• Structured analytical writeups from conflict-related prompts
• Research workflows involving conflict indicators and escalation patterns

Potential users:

• AI engineers
• Researchers
• Analysts exploring geopolitical risk
• Developers building conflict-analysis prototypes

Out-of-scope use:

• Autonomous national security decision-making
• Real-world operational targeting
• Deterministic prediction of conflict events
• High-stakes decisions without human review

Model Details

• Model type: Causal language model / instruction-tuned LLM
• Base model: mistralai/Ministral-8B-Instruct-2410
• Fine-tuning approach: [Fill in: SFT, DPO, LoRA, QLoRA, full fine-tune, etc.]
• Primary domain: Civil conflict and geopolitical risk reasoning
• Author: Firemedic15

Training Data

This model was fine-tuned for conflict-related analytical reasoning using data focused on civil war risk, escalation indicators, and geopolitical interpretation.

Training data may include:

• Structured civil conflict prediction records
• Instruction-response examples for analytical reasoning
• Domain-specific prompts focused on political instability, escalation, and conflict drivers

Dataset(s):

• Firemedic15/Civil_war_prediction
• [Add any additional datasets used]

Training Procedure

Fine-tuning was performed to improve the model’s ability to:

• Interpret conflict-related indicators
• Produce coherent analytical reasoning
• Explain likely drivers of instability
• Respond in a more domain-consistent way than the base model

Training configuration:

• Framework: [Fill in: transformers / TRL / PEFT / Unsloth / Axolotl / etc.]
• Hardware: [Fill in]
• Epochs: [Fill in]
• Learning rate: [Fill in]
• Batch size: [Fill in]
• Sequence length: [Fill in]
• Precision: [Fill in]
• Adapter method: [Fill in if applicable]

Evaluation

This model should be evaluated on both quality and reliability, not just fluency.

Recommended evaluation dimensions:

• Domain relevance
• Factual consistency
• Analytical coherence
• Calibration of risk language
• Resistance to overclaiming

Current evaluation status:

• Quantitative metrics: [Fill in if available]
• Human evaluation: [Fill in if available]
• Benchmark prompts: [Fill in if available]

Example evaluation questions:

• Does the model distinguish structural risk from immediate triggers?
• Does it avoid presenting speculation as certainty?
• Does it explain uncertainty clearly?
• Does it remain grounded in the provided indicators?

Example Use

Example prompt

Assess the risk of civil conflict escalation in a country with declining GDP growth, increasing political exclusion, recent regime instability, and worsening communal tensions. Explain the main drivers and key uncertainties.