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library_name: transformers
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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license: apache-2.0
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base_model: Mostafa8Mehrabi/qwen3-50m-fp16
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tags:
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- qwen
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- insomnia
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- therapy
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- fine-tuned
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- bf16
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- pytorch
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- text-generation
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library_name: transformers
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pipeline_tag: text-generation
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model_type: qwen
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language:
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- en
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datasets:
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- Mostafa8Mehrabi/insomnia-dataset-with-cot
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---
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# 🧠 Qwen3-50M Insomnia Therapist
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Fine-tuned version of Qwen3-50M specialized for insomnia therapy conversations with Chain of Thought reasoning.
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## 🎯 Model Details
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- **Base Model**: Mostafa8Mehrabi/qwen3-50m-fp16
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- **Fine-tuned on**: Mostafa8Mehrabi/insomnia-dataset-with-cot
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- **Precision**: BF16
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- **Model Size**: ~50M parameters
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- **Specialization**: Insomnia therapy with Chain of Thought reasoning
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## 📊 Training Results
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- **Final Training Loss**: 1.1862
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- **Final Validation Loss**: 1.2026013135910034
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- **Training Epochs**: 3
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- **Batch Size**: 4
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- **Learning Rate**: 2e-05
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- **Max Length**: 1024
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- **Precision Used**: BF16
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## 🚀 Usage
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### Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m-insomnia-therapist")
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model = AutoModelForCausalLM.from_pretrained(
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"Mostafa8Mehrabi/qwen3-50m-insomnia-therapist",
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device_map="auto"
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)
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# Generate therapy response
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prompt = '''<|system|>Generate insomnia therapy conversation. Gather sleep information, establish rapport, introduce sleep diary. Include CoT reasoning. Validate-Educate-Recommend-Check format.<|endoftext|>
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<|patient|>I can't sleep at night. I keep thinking about work and worrying about tomorrow.<|endoftext|>
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<|cot|>'''
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inputs = tokenizer(prompt, return_tensors="pt")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length=400,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## 📝 Format Structure
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The model follows a specific conversation format:
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- **Input**: `<|system|>` + `<|patient|>`
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- **Output**: `<|cot|>` (Chain of Thought reasoning) + `<|therapist|>` (Therapeutic response)
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### Format Example:
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```
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<|system|>Generate insomnia therapy conversation...<|endoftext|>
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<|patient|>I can't sleep at night.<|endoftext|>
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<|cot|>Patient reports sleep difficulty. Need to validate concern, gather more information about sleep patterns, potential causes, and establish therapeutic rapport. Should introduce sleep diary concept and provide initial guidance.<|endoftext|>
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<|therapist|>I understand how frustrating it must be to struggle with sleep. This is a common issue that many people face, and I'm here to help you work through it. Can you tell me a bit more about your sleep patterns? For instance, how long have you been experiencing this difficulty, and what typically happens when you try to fall asleep?<|endoftext|>
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```
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## 🎯 Key Features
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- **Chain of Thought Reasoning**: The model provides transparent reasoning before generating responses
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- **Therapeutic Approach**: Follows evidence-based therapy principles
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- **Validation-Education-Recommendation-Check**: Structured therapeutic format
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- **Optimized Training**: Trained with BF16 precision for efficiency
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- **Specialized Training**: Fine-tuned specifically on insomnia therapy conversations
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## ⚙️ Technical Specifications
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- **Architecture**: Qwen3 (Transformer-based)
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- **Parameters**: ~50M
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- **Training Precision**: BF16
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- **Context Length**: 1024 tokens
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- **Training Framework**: PyTorch + Transformers
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- **Optimization**: AdamW with warmup
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## 🔧 Hardware Requirements
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- **Minimum**: 2GB GPU VRAM
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- **Recommended**: 4GB+ GPU VRAM
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- **CPU**: Compatible with CPU inference (slower)
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## 📚 Training Data
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Trained on curated insomnia therapy conversations with Chain of Thought annotations from the Mostafa8Mehrabi/insomnia-dataset-with-cot dataset.
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## 🚨 Important Notes
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- This model is for educational and research purposes
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- Not a replacement for professional medical advice
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- Always consult healthcare professionals for serious sleep disorders
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- Model outputs should be reviewed by qualified therapists
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## 📄 License
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Apache 2.0 License
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## 🤝 Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{qwen3-50m-insomnia-therapist,
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title={Qwen3-50M Insomnia Therapist},
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author={Mostafa8Mehrabi},
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year={2024},
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publisher={Hugging Face},
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url={https://huggingface.co/Mostafa8Mehrabi/qwen3-50m-insomnia-therapist}
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}
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```
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*Fine-tuned with ❤️ for better sleep health*
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