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Model Details
Model Description
This model is based on DeepSeek-llm-7B-chat, fine-tuned using LoRA (Low-Rank Adaptation).
The training data consists of multi-turn psychotherapy conversations between therapists and patients, aiming to enhance the model’s dialogue capability in mental health consultation scenarios.
- Developed by: [Elearon]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [Your Hugging Face ID]
- Model type: Causal Language Model (Chat-oriented)
- Language(s): Chinese
- License: [To Be Specified, e.g. Apache-2.0 or DeepSeek license compliance]
- Finetuned from model: deepseek-ai/deepseek-llm-7b-chat
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
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- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
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Uses
This model can be used for generating multi-turn psychotherapy-style dialogues, simulating interactions between therapists and patients.
Direct Use
[More Information Needed]
Downstream Use [optional]
- Virtual assistants for mental health support
- Research on psychotherapy dialogue modeling
- Emotion-support chatbots
[More Information Needed]
Out-of-Scope Use
⚠️ This model is not a medical device and must not replace licensed psychologists or psychiatrists. It must not be used for:
- Emergency mental health interventions (e.g., suicide prevention)
- Prescribing medication or treatment
- High-risk or clinical decision-making contexts
[More Information Needed]
Bias, Risks, and Limitations
- The dataset is limited and may not cover all psychological conditions.
- The model may generate biased or potentially harmful advice.
- The model should not be used as a substitute for professional therapy.
[More Information Needed]
Recommendations
Users should be aware of the biases, limitations, and risks of this model. It is intended for research and educational purposes only, not for clinical deployment.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
1from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
2
3model_id = Elearon/deepseek-medical-lora"
4tokenizer = AutoTokenizer.from_pretrained(model_id)
5model = AutoModelForCausalLM.from_pretrained(model_id)
6
7chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer)
8
9query = "I’ve been feeling anxious and having trouble sleeping. What should I do?"
10response = chatbot(query, max_length=512, do_sample=True, top_p=0.9, temperature=0.7)
11print(response[0]["generated_text"])
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Software
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