Update README.md

README.md

@@ -1,199 +1,111 @@
 ---
+license: llama3
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+  - AIMH/SQPsychConv_command
+language:
+  - en
+pipeline_tag: text-generation
 library_name: transformers
-tags: []
+tags:
+  - mental-health
+  - psychotherapy
+  - counseling
+  - cbt
+  - conversational
+  - synthetic-data
+  - therapist-roleplay
 ---
 
-# Model Card for Model ID
+# Model Card for SQPsychLLM-8b-command
 
-<!-- Provide a quick summary of what the model is/does. -->
+`SQPsychLLM-8b-command` is a chat model fine-tuned to **roleplay a therapist** in synthetic, Cognitive Behavioral Therapy (CBT)-informed counseling conversations. It is part of the **SQPsychLLM** family released with the paper *Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires*.
 
+This checkpoint is **Llama-3-8B-Instruct** supervised-fine-tuned on **SQPsychConv (Command)**, the synthetic corpus generated by `CohereLabs/c4ai-command-a-03-2025` from real, de-identified structured client profiles and psychological questionnaires (BDI, HAM-D).
 
+> ⚠️ **Research use only.** This model is **not** a medical device and **not** a substitute for professional mental-health care. It must not be deployed to interact with patients or anyone in distress without rigorous further validation and qualified clinical oversight. See [Out-of-Scope Use](#out-of-scope-use).
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** Doan Nam Long Vu and collaborators (Technical University of Darmstadt; Philipps-University Marburg; Justus Liebig University Giessen; University of Münster), released under the AIMH ("AI for Mental Health") organization
+- **Funded by:** LOEWE Center DYNAMIC (Hessian LOEWE program), grant LOEWE1/16/519/03/09.001(0009)/98
+- **Model type:** Decoder-only causal language model (instruction/chat), fine-tuned for therapist roleplay
+- **Language(s):** English
+- **License:** llama3 (inherited from the base model; see [License and data provenance](#license-and-data-provenance))
+- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Model Sources
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository (code):** https://github.com/AI-MH/questionnaire2dialogue
+- **Project page:** https://ai-mh.github.io/SQPsych
+- **Paper:** https://arxiv.org/abs/2510.25384
+- **Datasets:** https://huggingface.co/collections/AIMH/sqpsychconv
+- **Model family:** https://huggingface.co/collections/AIMH/sqpsychllm
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Research on synthetic mental-health dialogue: generating therapist-side turns in CBT-style counseling conversations, studying privacy-preserving synthetic data, and benchmarking counseling-oriented language models. For the full questionnaire-conditioned, dual-agent (therapist + client) generation pipeline, see the [code repository](https://github.com/AI-MH/questionnaire2dialogue).
 
-[More Information Needed]
+### Downstream Use
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+As a starting point for further research fine-tuning, or as a component in supervised, human-in-the-loop training and education settings (e.g., clinician/student practice simulations) under appropriate oversight and ethics approval.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model must **not** be used to:
 
-[More Information Needed]
+- provide therapy, diagnosis, or any clinical decision to real people;
+- act as a crisis, emergency, or safety-critical support system;
+- interact with patients or people in distress without further validation, clinical supervision, and regulatory approval;
+- impersonate, or be presented as, a real licensed clinician.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Hallucination and unsafe output.** Like all LLMs, it can produce incorrect, fabricated, or clinically inappropriate content, including advice that is not evidence-based.
+- **Limited clinical scope.** The conditioning data covers **major depressive disorder**; other conditions, comorbidities, severities, and acute-risk presentations are out of scope and underrepresented.
+- **Limited population coverage.** The source cohort's demographics, language, and cultural context constrain generalization.
+- **Inherited bias.** Outputs reflect biases of the base model (Llama-3-8B-Instruct) and of the model (`CohereLabs/c4ai-command-a-03-2025`) used to generate the training corpus.
+- **Synthetic-vs-real gap.** Generated dialogues may not capture the full complexity or risk dynamics of real clinical interactions.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Keep a qualified human professional in the loop for any applied use, validate on your own population, obtain your own ethics approval before any study involving people, and add explicit safety guardrails and crisis-resource handling in any interactive system. See the project [ETHICS statement](https://github.com/AI-MH/questionnaire2dialogue/blob/main/ETHICS.md).
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+model_id = "AIMH/SQPsychLLM-8b-command"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
 
-#### Metrics
+messages = [
+    {"role": "system", "content": "You are an empathetic therapist conducting a CBT-informed session."},
+    {"role": "user", "content": "I've felt down and unmotivated for weeks and I don't know why."},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
 
-[More Information Needed]
+Serve with vLLM (OpenAI-compatible API):
 
-### Results
+```bash
+vllm serve "AIMH/SQPsychLLM-8b-command"
+```
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+### Training Data
 
-[More Information Needed]
+[AIMH/SQPsychConv_command](https://huggingface.co/datasets/AIMH/SQPsychConv_command), synthetic therapist-client conversations generated by `CohereLabs/c4ai-command-a-03-2025`, conditioned on de-identified structured profiles and questionnaire scores (BDI, HAM-D) from the cohort of Kircher et al. (2019). The instruction-formatted split used for training is `AIMH/SQPsychConv_command_finetune`.
 
-## Model Card Contact
+## License and data provenance
 
-[More Information Needed]
+The model weights derive from **Llama-3-8B-Instruct (Llama 3 Community License)**. The training data was **generated by `CohereLabs/c4ai-command-a-03-2025`**, so **the synthetic data was produced by a model under a non-commercial license ([CC-BY-NC 4.0](https://huggingface.co/CohereLabs/c4ai-command-a-03-2025) plus Cohere's acceptable-use policy)** — review it carefully before any non-research use or redistribution. The source structured data is de-identified and pre-anonymized, and the released conversations are synthetic and contain no personally identifiable information. Released for research only.