---
license: cc-by-sa-4.0
datasets:
- UKPLab/Graph2Counsel
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
pipeline_tag: text-generation
---

# Model Card for Llama3-G2C
 
Llama-3-8B-Instruct fine-tuned on the [Graph2Counsel dataset](https://huggingface.co/datasets/UKPLab/Graph2Counsel) for mental health counseling dialogue generation. The model generates counselor response to a client dialogue in a multi-turn counseling dialogue.
 
## 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 instruction, followed by the dialogue history and the client profile.
 
`Output`: The next counselor turn in the therapeutic dialogue.
 
`Training data`: [Graph2Counsel](https://huggingface.co/datasets/UKPLab/Graph2Counsel) — a dataset of synthetic counseling sessions grounded in CPGs derived from real counseling sessions.
 
`Fine-tuning method`: QLoRA
 
## Usage
 
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
 
model_id = "UKPLab/Llama3-G2C"
 
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
 
system_prompt = (
    "You are a professional counselor. Your task is to generate a natural, empathetic "
    "and therapeutic response to the client's most recent utterance while adhering to "
    "established psychological techniques. You are provided with the current dialogue "
    "history and the client profile. 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 "
    "or explaining the used technique."
)
 
history = (
    "Counselor: What brings you in today?\n"
    "Client: I've been feeling really anxious at work lately. "
    "It usually happens when I have to give feedback. "
    "I worry my comments won't be taken seriously."
)
 
profile = (
    "Client is a 28-year-old graphic designer who overthinks interactions "
    "with colleagues and struggles to articulate her feelings in stressful situations."
)
 
user_content = f"Dialogue History:\n{history}\nClient Profile:\n{profile}"
 
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{mandal2026graph2counselclinicallygroundedsynthetic,
      title={Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs}, 
      author={Aishik Mandal and Hiba Arnaout and Clarissa W. Ong and Juliet Bockhorst and Kate Sheehan and Rachael Moldow and Tanmoy Chakraborty and Iryna Gurevych},
      year={2026},
      eprint={2604.20382},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.20382}, 
}
```
 
## Contact
 
For questions or feedback, please contact: [aishik.mandal@tu-darmstadt.de](mailto:aishik.mandal@tu-darmstadt.de)