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SpaceXerror/
cognitive-ai-mental-health-7b

Name: cognitive-ai-mental-health-7b
Availability: InStock

nbertagnolli/counsel-chat

vibhorag101/phr_mental_therapy_dataset

2106.09685

2305.14314

2310.06825

mistralai/Mistral-7B-Instruct-v0.3

adapter

apache-2.0

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🧠 Cognitive AI — Mental Health Assistant

Fine-tuned Mistral-7B-Instruct-v0.3 · QLoRA · Domain-Specific Adaptation

License

Fine-tuned by SpaceXerror as part of ongoing research on domain-specific LLM adaptation for mental health support.

📌 Model at a Glance

Property	Value
Base Model	`mistralai/Mistral-7B-Instruct-v0.3`
Fine-Tuning Method	QLoRA (4-bit NF4 Quantization)
LoRA Config	r=16 · α=32 · dropout=0.05
Trainable Parameters	41.94M (1.1% of total)
Training Samples	7,125
Epochs	1
Training Time	4 hours 57 minutes
Hardware	2× Tesla T4 16GB (Kaggle)
Final Train Loss	0.8180
Final Val Loss	0.7862
Perplexity	2.451 ✅
Framework	HuggingFace Transformers + PEFT + TRL

🎯 Purpose

This model was developed as part of a research paper investigating the effectiveness of parameter-efficient fine-tuning (PEFT) methods — specifically QLoRA — for adapting large language models to mental health support tasks.

The assistant is designed to:

💚 Validate user feelings with empathy
📖 Provide evidence-based coping strategies
🧪 Offer psychoeducation on mental health topics
🏥 Encourage professional help when appropriate
🔒 Maintain clear ethical boundaries at all times

🏗️ Architecture & Training Pipeline

mermaid

flowchart TD
    A([🗂️ Raw Datasets]) --> B[Data Engineering & Filtering]
    B --> C[Mistral Chat Template Formatting]
    C --> D[95 / 5 Train-Val Split\n6,768 train · 357 val]

    D --> E([🤖 Mistral-7B-Instruct-v0.3])
    E --> F[4-bit NF4 Quantization\nBitsAndBytesConfig]
    F --> G[prepare_model_for_kbit_training]

    G --> H[LoRA Adapter Injection\nr=16 · α=32 · 7 projection layers]
    H --> I[🏋️ QLoRA Fine-Tuning\nSFTTrainer · 1 Epoch]

    I --> J[Perplexity Evaluation\n200 held-out samples]
    J --> K{Perplexity < 5.0?}
    K -- ✅ 2.451 --> L[Save LoRA Adapter\n162 MB]
    L --> M[Merge into Base Model\n13.5 GB]
    M --> N([🚀 Push to HuggingFace Hub])

📂 Training Data

mermaid

pie title Dataset Composition (7,125 samples)
    "CounselChat — Therapist Q&A" : 2598
    "PHR Mental Therapy — Multi-turn" : 4527

Dataset Details

Dataset	HuggingFace ID	Samples	Type	Topics
CounselChat	`nbertagnolli/counsel-chat`	2,598	Therapist Q&A	Depression, anxiety, relationships, self-esteem, trauma
PHR Mental Therapy	`vibhorag101/phr_mental_therapy_dataset`	4,527	Multi-turn therapy	Empathetic dialogue in Mistral Instruct format
Total		7,125	Mixed
Train split (95%)		6,768
Val split (5%)		357

⚙️ Hyperparameters

QLoRA / Quantization

Parameter	Value
Quantization	4-bit NF4
Compute dtype	`float16`
Double quantization	✅ Enabled

LoRA Configuration

Parameter	Value
Rank (r)	16
Alpha	32
Dropout	0.05
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Trainable parameters	41.94M (1.1%)
Frozen parameters	3.758B

Training Arguments

Parameter	Value
Learning rate	2e-4
LR Scheduler	Cosine
Warmup ratio	0.05
Optimizer	`paged_adamw_32bit`
Batch size	2 (effective 16)
Gradient accumulation	8 steps
Max gradient norm	0.3
Weight decay	0.01
Max sequence length	512
Gradient checkpointing	✅ Enabled
Early stopping patience	3
Epochs	1

📈 Training Results

Loss Curve

mermaid

xychart-beta
    title "Training vs Validation Loss"
    x-axis [Step 100, Step 200, Step 300, Step 400, Final]
    y-axis "Loss" 0.70 --> 0.86
    line [0.8204, 0.7659, 0.7431, 0.7694, 0.8180]
    line [0.8401, 0.8145, 0.7956, 0.7862, 0.7862]

Step	Training Loss	Validation Loss
100	0.8204	0.8401
200	0.7659	0.8145
300	0.7431	0.7956
400	0.7694	0.7862
Final	0.8180	0.7862

Perplexity Evaluation

Samples Evaluated	Perplexity
50	2.359
100	2.439
150	2.438
200	2.451

✅ Perplexity of 2.451 is considered excellent for domain-specific mental health conversational AI. Target threshold: < 5.0.

💬 Sample Outputs

Example 1 — Anxiety

User: I've been feeling really anxious lately and I can't seem to control my thoughts. Everything feels overwhelming.

Model: Hey there, it sounds like your mind is full of racing thoughts and it's getting pretty intense. It could be helpful to start by acknowledging what you're feeling — and that it's okay to feel overwhelmed. Try taking some time to sit with those thoughts. Instead of trying to control them, just observe them, notice them, and let them pass through...

Example 2 — Depression

User: I think I might have depression but I'm scared to talk about it. I feel empty most days and don't enjoy things I used to love.

Model: Hello! I am glad that you have taken the first step of reaching out. It is completely okay to be scared of talking about your feelings with others. Remember, you are not alone in this, and you deserve support...

Example 3 — Burnout

User: I've been experiencing a lot of stress at work. My boss is very demanding and I'm starting to have trouble sleeping. I feel burned out.

Model: Burnout is a real condition that can be very debilitating. The good news is that it is reversible! Start by taking breaks during your work day to do something relaxing — like taking a walk outside...

🚀 How to Use

Option 1 — Full Merged Model (Recommended)

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "SpaceXerror/cognitive-ai-mental-health-7b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

SYSTEM_PROMPT = """You are a compassionate, empathetic mental health support assistant.
You listen carefully, validate feelings, and provide thoughtful, evidence-based guidance.
Always prioritize the person's safety and wellbeing."""

def chat(user_message: str) -> str:
    prompt = f"<s>[INST] {SYSTEM_PROMPT}\n\n{user_message} [/INST]"
    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        truncation=True,
        max_length=512,
    ).to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=300,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.4,
            no_repeat_ngram_size=4,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )

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

print(chat("I have been feeling very anxious lately. What can I do?"))

Option 2 — PEFT Adapter Only

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.3",
    torch_dtype=torch.float16,
    device_map="auto",
)

model = PeftModel.from_pretrained(
    base_model,
    "SpaceXerror/cognitive-ai-mental-health-7b",
)

tokenizer = AutoTokenizer.from_pretrained(
    "SpaceXerror/cognitive-ai-mental-health-7b"
)

⚠️ Limitations & Ethical Considerations

This model is NOT a replacement for professional therapy or crisis intervention.

Not a clinical tool — Designed for support and psychoeducation, not diagnosis or treatment.
Repetition artifacts — Occasional repetitive phrases from training data. Mitigate with repetition_penalty=1.4 and no_repeat_ngram_size=4.
Contact info leakage — CounselChat training data contained therapist contact details. Apply post-processing filters in production.
Crisis situations — Always redirect users in crisis to emergency services or crisis hotlines. This model is not equipped to handle acute mental health emergencies.
Dataset bias — Responses may reflect biases present in training data.
Limited scale — Trained on 7,125 samples for 1 epoch. Future work should explore multi-epoch and larger-scale training.

🔬 Research Context

Research Question:

Can parameter-efficient fine-tuning (QLoRA) of a 7B parameter LLM produce an effective mental health support assistant on consumer-grade hardware?

Key Findings:

✅ QLoRA enables full fine-tuning of 7B models on 2× Tesla T4 GPUs (Kaggle free tier)
✅ Combined dataset training (Q&A + multi-turn therapy) improves generalization
✅ Perplexity of 2.451 demonstrates strong domain adaptation after only 1 epoch
✅ Only 1.1% of parameters were trained — proving PEFT efficiency at scale

Research Paper: In Progress

📚 References

Hu, E. et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685
Dettmers, T. et al. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. arXiv:2305.14314
Jiang, A. et al. (2023). Mistral 7B. arXiv:2310.06825
nbertagnolli. (2022). CounselChat Dataset. HuggingFace Datasets.
vibhorag101. (2023). PHR Mental Therapy Dataset. HuggingFace Datasets.

👤 Author

SpaceXerror

🤗 HuggingFace: huggingface.co/SpaceXerror
🔗 Model: SpaceXerror/cognitive-ai-mental-health-7b

📄 Citation

bibtex

@misc{spacexerror2024mentalhealthllm,
  title   = {Fine-tuning Mistral-7B for Mental Health Support using QLoRA},
  author  = {SpaceXerror},
  year    = {2024},
  publisher = {HuggingFace},
  url     = {https://huggingface.co/SpaceXerror/cognitive-ai-mental-health-7b}
}

⚠️ This model is intended for research purposes only.
If you are experiencing a mental health crisis, please contact emergency services or a crisis helpline immediately.