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--- |
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base_model: unsloth/qwen3-14b-unsloth-bnb-4bit |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen3 |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- mattwesney/CoT_Heartbreak_and_Breakups |
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pipeline_tag: text-generation |
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library_name: peft |
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--- |
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# Model Description |
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- **Developed by:** khazarai |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit |
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This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) |
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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. |
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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. |
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The goal of this fine-tuning is to enhance: |
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- Emotional reasoning capability |
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- Structured chain-of-thought generation |
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- Empathetic and psychologically grounded responses |
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- Relationship pattern analysis |
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- Identity reconstruction & self-esteem rebuilding guidance |
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# 🧠 Base Model |
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- Base architecture: Qwen3-14B |
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- Variant: unsloth/qwen3-14b-unsloth-bnb-4bit |
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- Quantization: 4-bit (bitsandbytes) |
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- Fine-tuning method: QLoRA |
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- Adapter type: LoRA |
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- Training precision: 4-bit base + 16-bit adapters |
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# 🎯 Intended Use |
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This model is intended for: |
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- Mental health–adjacent AI assistants |
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- Relationship guidance systems |
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- Emotional reasoning research |
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- Chain-of-thought alignment experiments |
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- NLP research on structured reasoning in affective domains |
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The model aims to produce: |
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- Step-by-step reasoning |
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- Balanced perspectives |
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- Reduced reactive or extreme advice |
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⚠️ Limitations |
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- Not a substitute for licensed therapy |
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- May generate plausible but non-clinically validated advice |
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- Trained on synthetic / curated emotional scenarios |
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- Chain-of-thought exposure may increase verbosity |
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- Emotional nuance outside breakup domain may be limited |
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This model should not be used for crisis intervention or high-risk mental health scenarios. |
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# How to get started with Model |
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``` Python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen3-14b-unsloth-bnb-4bit") |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"unsloth/qwen3-14b-unsloth-bnb-4bit", |
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device_map={"": 0} |
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) |
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model = PeftModel.from_pretrained(base_model,"khazarai/Med-R1-14B") |
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question = """ |
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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? |
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""" |
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messages = [ |
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{"role" : "user", "content" : question} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize = False, |
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add_generation_prompt = True, |
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enable_thinking = True, |
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) |
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from transformers import TextStreamer |
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_ = model.generate( |
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**tokenizer(text, return_tensors = "pt").to("cuda"), |
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max_new_tokens = 2048, |
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temperature = 0.6, |
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top_p = 0.95, |
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top_k = 20, |
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streamer = TextStreamer(tokenizer, skip_prompt = True), |
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) |
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``` |
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# 🧪 Future Work |
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- Domain expansion to broader emotional intelligence tasks |
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- Controlled reasoning output (hidden CoT vs visible CoT) |
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- Evaluation via human annotation |
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- Cross-cultural emotional adaptation |
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