Update README.md

README.md

@@ -7,4 +7,115 @@ language:
 base_model:
 - meta-llama/Meta-Llama-3-8B-Instruct
 pipeline_tag: text-generation
----
+arxiv: 2509.04183
+---
+# Model Card for Llama3-MAGneT
+ 
+Llama-3-8B-Instruct fine-tuned on the [MAGneT dataset](https://huggingface.co/datasets/UKPLab/MAGneT) for mental health counseling dialogue generation. MAGneT sessions are generated using a coordinated multi-agent framework incorporating specialized response agents (reflection, questioning, solutions, normalizing, psycho-education), a technique agent, a CBT agent, and a response generation agent — resulting in more nuanced and clinically grounded counseling dialogues than single-agent approaches.
+ 
+## Model Details
+ 
+`Base model`: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+ 
+`Input`: A system prompt with a fixed counselor role instruction, followed by the client's demographic information, reason for seeking counseling, and the dialogue history.
+ 
+`Output`: The next counselor turn in the therapeutic dialogue.
+ 
+`Training data`: [MAGneT](https://huggingface.co/datasets/UKPLab/MAGneT) — 442 synthetic multi-turn counseling sessions grounded in client profiles from the [CACTUS](https://huggingface.co/datasets/LangAGI-Lab/cactus) dataset, generated using a coordinated multi-agent framework.
+ 
+`Fine-tuning method`: QLoRA
+ 
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+ 
+model_id = "UKPLab/Llama3-MAGneT"
+ 
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+ 
+system_prompt = (
+    "You are playing the role of a counselor in a psychological counseling session. "
+    "Your task is to use the provided client information to generate the next counselor "
+    "response in the dialogue by combining psychological techniques like reflections, "
+    "questioning, providing solutions, normalizing and psychoeducation. The goal is to "
+    "create a natural and engaging response that builds on the previous conversation. "
+    "Please ensure that the response is empathetic and understanding of the client's issues "
+    "and builds trust between the counselor and the client. Please be mindful to only generate "
+    "the counselor response for a single turn, and do not include extra text like \"here is the "
+    "next counselor utterance\" or \"Here is a possible next utterance\" or anything mentioning "
+    "the used technique."
+)
+ 
+client_information = (
+    "Name:\nLaura Saunders\nAge:\n45\nGender:\nfemale\nOccupation: Office Job\n"
+    "Education: College Graduate\nMarital Status: Single\nFamily Details: Lives alone"
+)
+ 
+reason_counseling = (
+    "I decided to seek counseling because this negative belief is hindering my enjoyment "
+    "of running and affecting my overall mood."
+)
+ 
+history = (
+    "Counselor: Hello, Laura. It's nice to meet you. What brings you in today?\n"
+    "Client: Hi, thank you. I've been struggling with the thought that I can't run far, "
+    "despite enjoying running as a hobby. It's really getting me down."
+)
+ 
+user_content = (
+    f"Client Information:\n{client_information}\n"
+    f"Reason for seeking counseling:\n{reason_counseling}\n"
+    f"Counseling Dialogue:\n{history}"
+)
+ 
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user",   "content": user_content},
+]
+ 
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+ 
+with torch.no_grad():
+    output = model.generate(
+        input_ids,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+    )
+ 
+response = tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+ 
+## Citation
+ 
+Please cite this model using:
+ 
+```bibtex
+@misc{mandal2025magnetcoordinatedmultiagentgeneration,
+      title={MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions}, 
+      author={Aishik Mandal and Tanmoy Chakraborty and Iryna Gurevych},
+      year={2025},
+      eprint={2509.04183},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2509.04183}, 
+}
+```
+ 
+## Contact
+ 
+For questions or feedback, please contact: [aishik.mandal@tu-darmstadt.de](mailto:aishik.mandal@tu-darmstadt.de)