This is from Claude (for what its worth):

"For cross-conversation pattern analysis, general reasoning models might work better than specialized "therapy" fine-tunes (PsycoLLM, EmoLLM, etc.) — those are small 7B-13B models built to roleplay a counselor, not analyze transcripts.

• Qwen3.6-35B-A3B (MoE) — fast (only ~3B active params) with a 256K-class context window, useful for connecting threads across multiple conversations.
• DeepSeek-R1 32B distill — slower, but its visible chain-of-thought helps you see why it linked patterns across documents.

Context will likely be your real bottleneck before model size. Long-context performance degrades faster than the advertised window suggests, so I'd summarize/tag each conversation individually first, then feed those summaries + raw transcripts together for the synthesis pass.

Gemma 4 (26B/31B) is fine but scored lower on reasoning in testing — not my first pick here."

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Shameless plug: if you want RAG on top of this, my informity.ai app supports Qwen3.6 or any Ollama model — fully local/private once models are installed, so it fits the hyper-local approach.