Model Description

• Developed by: khazarai
• License: apache-2.0
• Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth

This model is a QLoRA fine-tuned version of unsloth/qwen3-14b-unsloth-bnb-4bit, originally based on the Qwen3-14B architecture developed by the Qwen Team. The model has been fine-tuned on the Chain of Thought – Heartbreak & Breakups Dataset (MIT Licensed), consisting of 9.8k high-quality Q&A pairs focused on emotional processing, coping strategies, and relationship dynamics following breakups. The goal of this fine-tuning is to enhance:

• Emotional reasoning capability
• Structured chain-of-thought generation
• Empathetic and psychologically grounded responses
• Relationship pattern analysis
• Identity reconstruction & self-esteem rebuilding guidance

🧠 Base Model

• Base architecture: Qwen3-14B
• Variant: unsloth/qwen3-14b-unsloth-bnb-4bit
• Quantization: 4-bit (bitsandbytes)
• Fine-tuning method: QLoRA
• Adapter type: LoRA
• Training precision: 4-bit base + 16-bit adapters

🎯 Intended Use

This model is intended for:

• Mental health–adjacent AI assistants
• Relationship guidance systems
• Emotional reasoning research
• Chain-of-thought alignment experiments
• NLP research on structured reasoning in affective domains

The model aims to produce:

• Step-by-step reasoning
• Balanced perspectives
• Reduced reactive or extreme advice

⚠️ Limitations

• Not a substitute for licensed therapy
• May generate plausible but non-clinically validated advice
• Trained on synthetic / curated emotional scenarios
• Chain-of-thought exposure may increase verbosity
• Emotional nuance outside breakup domain may be limited

This model should not be used for crisis intervention or high-risk mental health scenarios.

How to get started with Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel


tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen3-14b-unsloth-bnb-4bit")
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/qwen3-14b-unsloth-bnb-4bit",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/Med-R1-14B")

question = """
How can someone work through and move past deeply painful memories associated with trauma, understanding that "moving past" doesn't mean forgetting but rather integrating the experience in a healthy way?
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2048,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧪 Future Work

• Domain expansion to broader emotional intelligence tasks
• Controlled reasoning output (hidden CoT vs visible CoT)
• Evaluation via human annotation
• Cross-cultural emotional adaptation