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Model: kakashi3lite/soulbox-cbt-therapy-1.5b
Source: Original Platform

.gitattributes

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+soulbox_therapy_v3.q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+  - hi
+  - mr
+  - te
+library_name: transformers
+tags:
+  - 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
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+datasets:
+  - kakashi3lite/soulbox-cbt-therapy-dataset
+model-index:
+  - name: SoulBox CBT Therapy Assistant 1.5B (flagship)
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        metrics:
+          - type: perplexity
+            name: Test-set Perplexity
+            value: 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)
+
+```python
+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)
+
+```bash
+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).

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{{\"name\": <function-name>, \"arguments\": <args-json-object>}}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.9.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "transformers_version": "5.9.0",
+  "use_cache": true
+}

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