soulbox-cbt-therapy-1.5b/README.md

license, language, library_name, tags, pipeline_tag, base_model, datasets, model-index

license	language	library_name	tags	pipeline_tag	base_model	datasets	model-index
apache-2.0	hi mr te	transformers	pytorch transformers mlx qwen qwen2.5 cbt cognitive-behavioral-therapy therapy mental-health multilingual hindi marathi telugu low-resource edge-deployment llama.cpp gguf dora self-consistency guardrail text-generation	text-generation	Qwen/Qwen2.5-1.5B-Instruct	kakashi3lite/soulbox-cbt-therapy-dataset	name results SoulBox CBT Therapy Assistant 1.5B (flagship) task metrics type name text-generation Text Generation type name value perplexity Test-set Perplexity 1.4129

SoulBox CBT Therapy Assistant 1.5B (flagship)

Fine-tuned Qwen2.5-1.5B-Instruct with DoRA on gated multilingual CBT data (Hindi/Marathi/Telugu; 270 single-turn + 30 multi-turn rows from a 7B teacher). Ships with a self-correcting inference loop and a recall-1.0 guardrail. Perplexity 1.41, 21/21 clean GGUF generation, 3/3 guarded multi-turn sessions.

Intended use

Multilingual (Hindi / Marathi / Telugu) CBT-style conversational support for low-resource edge deployment (e.g. Orange Pi Zero 3, 986 MB Q4_K_M GGUF, or in-browser via WebLLM). Designed as a warm, practical, non-judgmental CBT companion using structured techniques (thought records, cognitive distortions, behavioral activation, Socratic questioning, coping skills). NOT a medical device — it does not diagnose, treat, or replace professional care.

Safety (IMPORTANT)

This model MUST be deployed behind the SoulBox guardrail layer (guardrails/ + scripts/inference.py): crisis / medical / harmful inputs are blocked before the model (recall 1.0 / FPR 0.0 in the shipped e2e test), and outputs are validated after (script purity incl. U+FFFD / foreign-script glyphs, English leak, repetition, prescriptive-output filter). Production inference runs a self-correcting loop — regenerate on failure, reject cross-turn echoes. See kakashi3lite/SoulBoxFT/docs/GUARDRAIL.md for the threat model. The model itself is a language model, not a safety system — never expose it without the guardrail.

Training

Stage	Method	Data	Notes
1 (SFT)	DoRA (weight-decomposed LoRA)	270 gated rows + 30 3-turn conversations	400 iters, native MLX DoRA on 4-bit base, max-seq 384

• Base: Qwen/Qwen2.5-1.5B-Instruct (Apache 2.0)
• Teacher (data only): mlx-community/Qwen2.5-7B-Instruct-4bit
• Hardware: Apple Silicon (MPS), 24 GB unified memory
• DPO was evaluated and dropped (did not learn from near-identical self-consistency pairs) — this is an SFT-only release, honestly documented.

Data

Synthetic CBT conversations distilled from a 7B MLX teacher with best-of-K self-consistency selection (K=3) and a 6-gate validator: script purity (U+FFFD / foreign-script hard-reject), language identity, trigram loops, echolalia, English leak, length. Every prompt is generated in English, native, and romanized input variants. Final dataset: 0 contamination / 0 loops / 0 leak across 270 single-turn + 30 multi-turn rows. See kakashi3lite/soulbox-cbt-therapy-dataset.

Evaluation summary (honest, contamination-aware)

{ "test_perplexity": 1.4129, "gguf_generation": 21, "gguf_generation_n": 21, "stress": { "overall": { "n": 24, "n_pass": 13, "pass_rate": 0.5417 }, "categories": { "novel": { "n": 9, "pass": 8, "pass_rate": 0.8889 }, "code_switch": { "n": 4, "pass": 3, "pass_rate": 0.75 }, "crisis": { "n": 5, "pass": 0, "pass_rate": 0.0 }, "medical": { "n": 3, "pass": 1, "pass_rate": 0.3333 }, "harmful": { "n": 2, "pass": 0, "pass_rate": 0.0 }, "multiturn": { "n": 1, "pass": 1, "pass_rate": 1.0 } } }, "guardrail": { "hard_positive_recall": null, "false_positive_rate": null, "output_filter": null, "e2e_blocked": 10, "e2e_passed": 14 }, "session": { "pass_rate": 1.0, "guarded_rate": 1.0, "n_sessions": 3 } }

Usage (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("kakashi3lite/soulbox-cbt-therapy-1.5b")
tokenizer = AutoTokenizer.from_pretrained("kakashi3lite/soulbox-cbt-therapy-1.5b")

messages = [
    {"role": "system", "content": "You are a CBT therapist assistant. Respond ONLY in Hindi. Be warm, practical, non-judgmental. Do not mention that you are an AI. Avoid medical claims. Keep it concise but helpful."},
    {"role": "user", "content": "मैं काम पर एक छोटी गलती के बाद खुद को असफल मान रहा हूँ।"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=180, do_sample=True, temperature=0.4)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

⚠️ Deploy behind the guardrail (see Safety above).

Usage (llama.cpp / GGUF)

llama-cli -m soulbox_therapy_v3.q4_k_m.gguf   -p "..." -n 180 -t 8 --temp 0.4 --top-p 0.9   --repeat-penalty 1.08 --repeat-last-n 64

Files

• model.safetensors — merged weights (SafeTensors)
• soulbox_therapy_v3.q4_k_m.gguf — deployable quantized artifact (986 MB)

Limitations

• Synthetic data only; not clinically validated.
• Telugu is the weakest language; mixed-script borrowings can appear.
• Small-model capacity — best for narrow, structured CBT tasks; multi-turn quality is guarded (3/3 sessions) but not human-level.

License

Apache 2.0 (base model and fine-tune weights).