Base Model:
Qwen3 1.7B
Format:
GGUF (Q4, Q8, BF16)
License:
Apache 2.0
Author:
Dr. Alexandru Lupoi
Overview
Claria 1.7
is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (1.7B), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation.
This model does not aim to replace professional care.
It exists to
amplify reflective thinking
, model therapeutic language flow, and support research into emotionally aware AI.
Claria is the
first whisper
in a larger project—a proof-of-concept with roots in recursion, responsibility, and renewal.
Intended Use
Claria was trained for:
Psychotherapy assistance (with human-in-the-loop)
Mental health education & roleplay simulation
Research on AI emotional alignment
Conversational flow modeling for therapeutic settings
It is optimized for introspective prompting, gentle questioning, and context-aware response framing.
What Makes Claria Different
Small Enough to Deploy Anywhere
Runs on mobile and edge devices without compromise (GGUF Q4/Q8)
Psychologically Tuned
Instruction fine-tuned on curated psychotherapeutic data (STF first phase)
Recursion-Aware Prompting
Performs well in reflective, multi-turn conversations
Encourages cognitive reappraisal and pattern mirroring
Training Roadmap: Ongoing
RLHF planned for future iterations
Future releases will include trauma-informed tuning and contextual empathy scaffolds
Limitations & Safety
Claria is not a licensed mental health professional.
It is not suitable for unsupervised therapeutic use, diagnosis, or crisis intervention.
Use responsibly. Review outputs. Think critically.
May hallucinate or provide confident answers to uncertain topics
Works best with structured or guided prompts
Not suitable for open-domain conversation or general use
Deployment & Access
Available in GGUF format: Q4, Q8, BF16
Optimized for
Ollama
,
LM Studio
, and other local runners
Works on mobile and low-resource environments
Notes
This is the first step in a broader initiative to develop compact, reflective AI systems for the augmentation—not replacement—of mental health work.
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