🧠 Zigroo Mental Consultant 2 (Merged)

alibidaran/Zigroo-Mental_consultant2-merged

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.

⚠️ 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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🔍 Model Details

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🏋️ Training Pipeline

Stage 1 — Supervised Fine-Tuning (SFT)

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:

Stage 2 — Direct Preference Optimization (DPO)

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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🚀 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "alibidaran/Zigroo-Mental_consultant2-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "I've been feeling very anxious lately and I don't know how to cope."

messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)

4-bit Quantized Inference (recommended for limited VRAM)

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

model_id = "alibidaran/Zigroo-Mental_consultant2-merged"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto"
)

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⚙️ LoRA Configuration

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:

• Rank (r): 32
• Base model: Qwen3-8B (via Unsloth 4-bit quantized variant)
• Merged: ✅ LoRA weights fully merged into base model

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🎯 Intended Use

• Research into LLM-based therapeutic conversational agents
• Prototyping mental health support chatbots
• Studying multi-stage fine-tuning pipelines (SFT + DPO) for sensitive domains
• Educational exploration of therapeutic dialogue generation

❌ Out-of-Scope Use

• Clinical diagnosis or treatment decisions
• Crisis intervention without human oversight
• Replacement of licensed therapists or psychiatrists
• Any deployment involving vulnerable populations without professional supervision

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⚠️ Ethical Considerations & Safety

Mental health is a sensitive domain. Please be aware of the following:

• Not a therapist. This model does not possess clinical judgment and should never be used as a standalone mental health service.
• Hallucinations. Like all LLMs, this model can generate plausible-sounding but incorrect or harmful content.
• Bias. Training datasets may reflect biases in how mental health topics are framed; outputs should be reviewed critically.
• Crisis situations. This model is not equipped to handle acute crisis situations. Always redirect users in crisis to emergency services or licensed professionals.

Built with ❤️ using Unsloth and TRL.