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  library_name: transformers
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- tags: []
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
 
 
 
 
 
 
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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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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- ### Model Sources [optional]
 
 
 
 
 
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- <!-- Provide the basic links for the model. -->
 
 
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
 
 
 
 
 
 
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- ## Uses
 
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
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- [More Information Needed]
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- ### Downstream Use [optional]
 
 
 
 
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
 
 
 
 
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
 
 
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
 
 
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
 
 
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
 
 
 
 
 
 
 
 
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- ### Training Data
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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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- [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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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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 Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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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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+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ *Fine-tuned with ❤️ for better sleep health*