Model Card for Llama3-G2C

Llama-3-8B-Instruct fine-tuned on the Graph2Counsel dataset 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

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 — a dataset of synthetic counseling sessions grounded in CPGs derived from real counseling sessions.

Fine-tuning method: QLoRA

Usage

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:

@misc{PLACEHOLDER,
      title     = {TITLE},
      author    = {AUTHOR(S)},
      year      = {YEAR},
      eprint    = {ARXIV_ID},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CL},
      url       = {https://arxiv.org/abs/ARXIV_ID},
}