---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
license: openrail
datasets:
- navidmadani/extended_esc
language:
- en
---

# Overview

The fine-tuned model for [Steering Conversational Large Language Models for Long Emotional Support Conversations
](https://arxiv.org/abs/2402.10453) paper. Code for training and inference can be found in our [github repository](https://github.com/navidmdn/ESConv-SRA).

# Running the model

You can find the scripts to chat with the model in the github repo. The following code shows a sample inference using the model. You need to import the list of
strategies and their description from our github as follows:

```python
from prompting.llama_prompt import modified_extes_support_strategies
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def get_sys_msg_with_strategy(strategy):
    if strategy is None:
        return "You are a helpful, precise, and accurate emotional support expert."

    description = modified_extes_support_strategies.get(strategy, "No description available")

    return (f"You are a helpful and caring AI, which is an expert in emotional support. "
            f"A user has come to you with emotional challenges, distress, or anxiety. "
            f"Use the \"{strategy}\" strategy ({description}) for answering the user. "
            "Make your response short and to the point.")

cur_strategy = "Clarification"

messages = [
    {'role': 'system', 'content': get_sys_msg_with_strategy(cur_strategy)},
    {'role': 'user', 'content': "Hello! How's it going?"},
    {'role': 'assistant', 'content': 'Hello. How can I assist you today?'},
    {'role': 'user', 'content': "I'm feeling a bit down today."},
]

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_name = "navidmadani/esconv_sra_llama3_8b"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = model.to(device)
model.eval()

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
input_t = torch.LongTensor([input_ids]).to(device)

output = model.generate(input_t, max_new_tokens=512)[:, input_t.shape[1]:]
resp = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
print(resp)

```