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Model: alibidaran/Zigroo-Mental_consultant2-merged Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
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tags:
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- mental-health
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- therapy
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- counseling
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- qwen3
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- lora
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- sft
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- dpo
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- unsloth
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- text-generation
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- conversational
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pipeline_tag: text-generation
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datasets:
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- vibhorag101/phr-mental-therapy-dataset-conversational-format-1024-tokens
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- Ghani69/ACT_therapy_scripts
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- jkhedri/psychology-dataset
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- fadodr/mental_health_therapy
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- arafatanam/Student-Mental-Health-Counseling-50K
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---
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# 🧠 Zigroo Mental Consultant 2 (Merged)
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**`alibidaran/Zigroo-Mental_consultant2-merged`**
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A fine-tuned large language model designed to provide empathetic, therapeutically-informed conversational support. Built on top of Qwen3-8B, this model was trained in two stages — Supervised Fine-Tuning (SFT) across five curated mental health datasets, followed by Direct Preference Optimization (DPO) to align responses toward reliable, compassionate, and therapeutically grounded outputs.
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> ⚠️ **Disclaimer:** This model is intended for research and educational purposes only. It is **not** a substitute for professional mental health care, diagnosis, or treatment. If you or someone you know is in crisis, please contact a licensed mental health professional or a crisis helpline immediately.
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---
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## 🔍 Model Details
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| Property | Value |
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|---|---|
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| **Base Model** | `unsloth/Qwen3-8B-unsloth-bnb-4bit` |
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| **Model Type** | Causal Language Model (Merged LoRA) |
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| **LoRA Rank** | 32 |
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| **Training Stages** | SFT → DPO |
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| **Language** | English |
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| **License** | Apache 2.0 |
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---
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## 🏋️ Training Pipeline
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### Stage 1 — Supervised Fine-Tuning (SFT)
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The model was fine-tuned using a LoRA adapter (rank = 32) on five mental health and therapy datasets covering a wide range of therapeutic modalities:
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| Dataset | Description |
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|---|---|
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| [`vibhorag101/phr-mental-therapy-dataset-conversational-format-1024-tokens`](https://huggingface.co/datasets/vibhorag101/phr-mental-therapy-dataset-conversational-format-1024-tokens) | Conversational mental therapy dialogues formatted to 1024 tokens |
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| [`Ghani69/ACT_therapy_scripts`](https://huggingface.co/datasets/Ghani69/ACT_therapy_scripts) | Acceptance and Commitment Therapy (ACT) scripts |
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| [`to-be/annomi-motivational-interviewing-therapy-conversations`](https://huggingface.co/datasets/to-be/annomi-motivational-interviewing-therapy-conversations) | Motivational interviewing therapy conversations |
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| [`fadodr/mental_health_therapy`](https://huggingface.co/datasets/fadodr/mental_health_therapy) | General mental health therapy dialogues |
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| [`arafatanam/Student-Mental-Health-Counseling-50K`](https://huggingface.co/datasets/arafatanam/Student-Mental-Health-Counseling-50K) | 50K student mental health counseling conversations |
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### Stage 2 — Direct Preference Optimization (DPO)
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Following SFT, the model underwent DPO training to align its outputs with preferred therapeutic response styles, improving reliability, empathy, and safety of generated responses.
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| Dataset | Description |
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|---|---|
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| [`jkhedri/psychology-dataset`](https://huggingface.co/datasets/jkhedri/psychology-dataset) | Psychology-grounded preference pairs for DPO alignment |
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---
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## 🚀 Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "alibidaran/Zigroo-Mental_consultant2-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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prompt = "I've been feeling very anxious lately and I don't know how to cope."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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### 4-bit Quantized Inference (recommended for limited VRAM)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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model_id = "alibidaran/Zigroo-Mental_consultant2-merged"
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config=bnb_config,
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device_map="auto"
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)
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```
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---
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## ⚙️ LoRA Configuration
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This model was trained with a LoRA adapter and the weights have been merged into the base model for ease of deployment. The key LoRA hyperparameters used during training:
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- **Rank (r):** 32
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- **Base model:** Qwen3-8B (via Unsloth 4-bit quantized variant)
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- **Merged:** ✅ LoRA weights fully merged into base model
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---
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## 🎯 Intended Use
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- Research into LLM-based therapeutic conversational agents
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- Prototyping mental health support chatbots
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- Studying multi-stage fine-tuning pipelines (SFT + DPO) for sensitive domains
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- Educational exploration of therapeutic dialogue generation
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## ❌ Out-of-Scope Use
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- Clinical diagnosis or treatment decisions
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- Crisis intervention without human oversight
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- Replacement of licensed therapists or psychiatrists
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- Any deployment involving vulnerable populations without professional supervision
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---
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## ⚠️ Ethical Considerations & Safety
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Mental health is a sensitive domain. Please be aware of the following:
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- **Not a therapist.** This model does not possess clinical judgment and should never be used as a standalone mental health service.
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- **Hallucinations.** Like all LLMs, this model can generate plausible-sounding but incorrect or harmful content.
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- **Bias.** Training datasets may reflect biases in how mental health topics are framed; outputs should be reviewed critically.
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- **Crisis situations.** This model is not equipped to handle acute crisis situations. Always redirect users in crisis to emergency services or licensed professionals.
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*Built with ❤️ using [Unsloth](https://github.com/unslothai/unsloth) and [TRL](https://github.com/huggingface/trl).*
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