Qwen 2.5 7B Instruct - Counseling Fine-tuned (LoRA)
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct using LoRA on a counseling conversations dataset.
π― Model Description
This model has been fine-tuned to provide empathetic and professional counseling responses. It's designed to assist with emotional support and guidance in conversations.
β οΈ Important Disclaimer: This model is for educational and research purposes only. It should NOT replace professional mental health services, therapy, or crisis intervention.
π Training Details
- Base Model: Qwen/Qwen2.5-7B-Instruct
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Training Framework: Unsloth
- Hardware: Single 16GB VRAM GPU
- Quantization: 4-bit (QLoRA)
- Training Date: 2025-10-20
- LoRA Rank: 16
- LoRA Alpha: 16
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
π Usage
Installation
pip install unsloth transformers
Basic Usage
from unsloth import FastLanguageModel
# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Ibrahim-AI-dev/mental-health-counseling-chatbot",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
# Enable inference mode
FastLanguageModel.for_inference(model)
# Create messages
messages = [
{"role": "system", "content": "You are a professional counselor providing empathetic and helpful responses."},
{"role": "user", "content": "I'm feeling anxious about my future. What should I do?"}
]
# Generate response
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
do_sample=True,
top_p=0.9,
)
response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)
Advanced Usage with Custom Parameters
# More creative responses
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.9, # Higher = more creative
top_p=0.95,
top_k=50,
repetition_penalty=1.1,
)
# More focused responses
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.3, # Lower = more focused
top_p=0.85,
do_sample=True,
)
πΎ Model Size
- LoRA Adapters: ~50-100 MB
- Full Base Model: ~14 GB (required separately)
- Merged Model: Available at
Ibrahim-AI-dev/mental-health-counseling-chatbot-merged(if uploaded)
π Training Configuration
- Learning Rate: 2e-4
- Batch Size: 2 (effective: 8 with gradient accumulation)
- Gradient Accumulation Steps: 4
- Optimizer: AdamW 8-bit
- Max Sequence Length: 2048
- Training Precision: Mixed (FP16/BF16)
π Intended Use Cases
- Research in conversational AI for mental health
- Educational demonstrations of fine-tuning techniques
- Prototyping counseling chatbot applications
- Studying empathetic response generation
β οΈ Limitations
- NOT a replacement for professional mental health care
- May not handle crisis situations appropriately
- Trained on specific conversation patterns and may not generalize to all scenarios
- Should be used with human oversight
- May occasionally generate inappropriate or incorrect responses
- Limited to text-based interaction
π Ethical Considerations
- Always inform users they are interacting with an AI
- Provide crisis hotline information for emergencies
- Monitor outputs for harmful content
- Do not use for diagnosis or treatment
- Respect user privacy and confidentiality
π Citation
If you use this model in your research, please cite:
@misc{qwen2.5-counseling-lora,
author = {Your Name},
title = {Qwen 2.5 7B Counseling Fine-tuned (LoRA)},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/Ibrahim-AI-dev/mental-health-counseling-chatbot}
}
π Acknowledgments
- Base Model: Qwen Team for Qwen2.5-7B-Instruct
- Training Framework: Unsloth for efficient fine-tuning
- LoRA Method: Microsoft Research
π Emergency Resources
If you or someone you know is in crisis:
- US: National Suicide Prevention Lifeline: 988 or 1-800-273-8255
- UK: Samaritans: 116 123
- International: Find local resources at befrienders.org
π License
This model inherits the Apache 2.0 license from Qwen2.5-7B-Instruct.
π Links
- Base Model: Qwen/Qwen2.5-7B-Instruct
- Training Code: Unsloth GitHub
- LoRA Paper: arXiv:2106.09685
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