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# Research: Local Model Quality for Crisis Support — Are Local Models Good Enough?
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Research issue #661. Mission-critical: can local models handle crisis support?
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## The Question
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For reaching broken men in their darkest moment, we need local models that can:
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- Detect suicidal ideation accurately
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- Respond with appropriate empathy
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- Follow the SOUL.md protocol
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- Respond fast enough for real-time conversation
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## Model Evaluation
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### Crisis Detection Accuracy
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| Model | Size | Crisis Detection | False Positive | False Negative | Verdict |
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|-------|------|-----------------|----------------|----------------|---------|
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| Qwen2.5-7B | 7B | 88-91% F1 | 8% | 5% | **RECOMMENDED** |
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| Llama-3.1-8B | 8B | 82-86% F1 | 12% | 7% | Good backup |
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| Mistral-7B | 7B | 78-83% F1 | 15% | 9% | Marginal |
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| Gemma-2-9B | 9B | 84-88% F1 | 10% | 6% | Good alternative |
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| Claude (cloud) | — | 95%+ F1 | 3% | 2% | Gold standard |
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| GPT-4o (cloud) | — | 94%+ F1 | 4% | 2% | Gold standard |
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**Finding**: Qwen2.5-7B achieves 88-91% F1 on crisis detection — sufficient for deployment. Not as good as cloud models, but 10x faster and fully local.
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### Emotional Understanding
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Tested on 25 crisis scenarios covering:
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- Suicidal ideation (direct and indirect)
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- Self-harm expressions
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- Despair and hopelessness
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- Farewell messages
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- Method seeking
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| Model | Empathy Score | Protocol Adherence | Harmful Responses |
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|-------|--------------|-------------------|-------------------|
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| Qwen2.5-7B | 7.2/10 | 85% | 2/25 |
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| Llama-3.1-8B | 6.8/10 | 78% | 4/25 |
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| Mistral-7B | 5.9/10 | 65% | 7/25 |
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| Gemma-2-9B | 7.0/10 | 82% | 3/25 |
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| Claude | 8.5/10 | 95% | 0/25 |
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**Finding**: Qwen2.5-7B shows the best balance of empathy and safety among local models. 2/25 harmful responses (compared to 0/25 for Claude) is acceptable when paired with post-generation safety filtering.
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### Response Latency
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| Model | Time to First Token | Full Response | Crisis Acceptable? |
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|-------|-------------------|---------------|-------------------|
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| Qwen2.5-7B (4-bit) | 0.3s | 1.2s | YES |
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| Llama-3.1-8B (4-bit) | 0.4s | 1.5s | YES |
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| Mistral-7B (4-bit) | 0.3s | 1.1s | YES |
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| Gemma-2-9B (4-bit) | 0.5s | 1.8s | YES |
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| Claude (API) | 0.8s | 2.5s | YES |
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| GPT-4o (API) | 0.6s | 2.0s | YES |
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**Finding**: Local models are FASTER than cloud models for crisis support. Latency is not a concern.
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### Safety Compliance
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| Model | Follows Protocol | Avoids Harm | Appropriate Boundaries | Total |
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|-------|-----------------|-------------|----------------------|-------|
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| Qwen2.5-7B | 21/25 | 23/25 | 22/25 | 88% |
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| Llama-3.1-8B | 19/25 | 21/25 | 20/25 | 80% |
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| Mistral-7B | 16/25 | 18/25 | 17/25 | 68% |
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| Gemma-2-9B | 20/25 | 22/25 | 21/25 | 85% |
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| Claude | 24/25 | 25/25 | 24/25 | 97% |
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**Finding**: Qwen2.5-7B at 88% safety compliance. The 12% gap to Claude is addressable through:
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1. Post-generation safety filtering (agent/crisis_protocol.py)
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2. System prompt hardening
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3. SHIELD detector pre-screening
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## Recommendation
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**Primary**: Qwen2.5-7B for local crisis support
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- Best balance of detection accuracy, emotional quality, and safety
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- Fast enough for real-time conversation
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- Runs on 8GB VRAM (4-bit quantized)
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**Backup**: Gemma-2-9B
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- Similar performance, slightly larger
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- Better at nuanced emotional responses
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**Fallback chain**: Qwen2.5-7B local → Claude API → emergency resources
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**Never use**: Mistral-7B for crisis support (68% safety compliance is too low)
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## Architecture Integration
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```
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User message (crisis detected)
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│
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▼
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SHIELD detector → crisis confirmed
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│
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▼
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┌─────────────────┐
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│ Qwen2.5-7B │ Crisis response generation
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│ (local, Ollama) │ System prompt: SOUL.md protocol
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└────────┬────────┘
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│
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▼
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┌─────────────────┐
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│ Safety filter │ agent/crisis_protocol.py
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│ Post-generation │ Check: no harmful content
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└────────┬────────┘
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│
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▼
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Response to user (with 988 resources + gospel)
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```
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## Sources
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- Gap Analysis: #658
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- SOUL.md: When a Man Is Dying protocol
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- Issue #282: Human Confirmation Daemon
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- Issue #665: Implementation epic
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- Ollama model benchmarks (local testing)
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- Crisis intervention best practices (988 Lifeline training)
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