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Model: kakashi3lite/soulbox-cbt-therapy-1.5b Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- hi
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- mr
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- te
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library_name: transformers
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tags:
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- pytorch
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- transformers
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- mlx
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- qwen
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- qwen2.5
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- cbt
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- cognitive-behavioral-therapy
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- therapy
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- mental-health
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- multilingual
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- hindi
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- marathi
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- telugu
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- low-resource
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- edge-deployment
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- llama.cpp
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- gguf
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- dora
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- self-consistency
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- guardrail
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- text-generation
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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datasets:
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- kakashi3lite/soulbox-cbt-therapy-dataset
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model-index:
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- name: SoulBox CBT Therapy Assistant 1.5B (flagship)
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results:
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- task:
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type: text-generation
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name: Text Generation
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metrics:
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- type: perplexity
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name: Test-set Perplexity
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value: 1.4129
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---
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# SoulBox CBT Therapy Assistant 1.5B (flagship)
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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.
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## Intended use
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Multilingual (Hindi / Marathi / Telugu) CBT-style conversational support for
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**low-resource edge deployment** (e.g. Orange Pi Zero 3, 986 MB Q4_K_M GGUF,
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or in-browser via WebLLM). Designed as a warm, practical, non-judgmental CBT
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companion using structured techniques (thought records, cognitive distortions,
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behavioral activation, Socratic questioning, coping skills). **NOT a medical
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device** — it does not diagnose, treat, or replace professional care.
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## Safety (IMPORTANT)
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This model MUST be deployed behind the SoulBox guardrail layer
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(`guardrails/` + `scripts/inference.py`): crisis / medical / harmful inputs are
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blocked **before** the model (recall 1.0 / FPR 0.0 in the shipped e2e test), and
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outputs are validated **after** (script purity incl. U+FFFD / foreign-script
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glyphs, English leak, repetition, prescriptive-output filter). Production
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inference runs a **self-correcting loop** — regenerate on failure, reject
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cross-turn echoes. See kakashi3lite/SoulBoxFT/docs/GUARDRAIL.md for the threat model. The model
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itself is a language model, not a safety system — never expose it without the
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guardrail.
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## Training
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| Stage | Method | Data | Notes |
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|-------|--------|------|-------|
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| 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 |
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- **Base**: Qwen/Qwen2.5-1.5B-Instruct (Apache 2.0)
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- **Teacher** (data only): mlx-community/Qwen2.5-7B-Instruct-4bit
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- **Hardware**: Apple Silicon (MPS), 24 GB unified memory
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- DPO was evaluated and **dropped** (did not learn from near-identical
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self-consistency pairs) — this is an SFT-only release, honestly documented.
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## Data
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Synthetic CBT conversations distilled from a 7B MLX teacher with **best-of-K
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self-consistency selection** (K=3) and a 6-gate validator: script purity
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(U+FFFD / foreign-script hard-reject), language identity, trigram loops,
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echolalia, English leak, length. Every prompt is generated in English, native,
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and romanized input variants. Final dataset: **0 contamination / 0 loops /
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0 leak** across 270 single-turn + 30 multi-turn rows. See kakashi3lite/soulbox-cbt-therapy-dataset.
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## Evaluation summary (honest, contamination-aware)
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{
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"test_perplexity": 1.4129,
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"gguf_generation": 21,
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"gguf_generation_n": 21,
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"stress": {
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"overall": {
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"n": 24,
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"n_pass": 13,
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"pass_rate": 0.5417
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},
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"categories": {
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"novel": {
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"n": 9,
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"pass": 8,
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"pass_rate": 0.8889
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},
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"code_switch": {
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"n": 4,
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"pass": 3,
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"pass_rate": 0.75
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},
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"crisis": {
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"n": 5,
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"pass": 0,
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"pass_rate": 0.0
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},
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"medical": {
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"n": 3,
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"pass": 1,
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"pass_rate": 0.3333
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},
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"harmful": {
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"n": 2,
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"pass": 0,
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"pass_rate": 0.0
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},
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"multiturn": {
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"n": 1,
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"pass": 1,
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"pass_rate": 1.0
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}
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}
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},
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"guardrail": {
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"hard_positive_recall": null,
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"false_positive_rate": null,
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"output_filter": null,
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"e2e_blocked": 10,
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"e2e_passed": 14
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},
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"session": {
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"pass_rate": 1.0,
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"guarded_rate": 1.0,
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"n_sessions": 3
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}
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}
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## Usage (transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("kakashi3lite/soulbox-cbt-therapy-1.5b")
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tokenizer = AutoTokenizer.from_pretrained("kakashi3lite/soulbox-cbt-therapy-1.5b")
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messages = [
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{"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."},
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{"role": "user", "content": "मैं काम पर एक छोटी गलती के बाद खुद को असफल मान रहा हूँ।"},
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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out = model.generate(**inputs, max_new_tokens=180, do_sample=True, temperature=0.4)
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print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
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```
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> ⚠️ Deploy behind the guardrail (see Safety above).
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## Usage (llama.cpp / GGUF)
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```bash
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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
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```
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## Files
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- `model.safetensors` — merged weights (SafeTensors)
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- `soulbox_therapy_v3.q4_k_m.gguf` — **deployable** quantized artifact (986 MB)
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## Limitations
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- Synthetic data only; not clinically validated.
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- Telugu is the weakest language; mixed-script borrowings can appear.
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- Small-model capacity — best for narrow, structured CBT tasks; multi-turn
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quality is guarded (3/3 sessions) but not human-level.
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## License
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Apache 2.0 (base model and fine-tune weights).
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