license: apache-2.0
language:
- en
base_model:
- meta-llama/Meta-Llama-3.1-8B
Empathetic teacher model
Overview
This is a LLM fine-tuned with real-life, ideally-empathetic teacher-student conversations. This model processes the recent conversation history and provides guidance on how a teacher might respond to the student's utterance.
To fine-tune an open-weighted LLM to act as this generic teacher, we have used the following datasets: the Teacher-Student Chatroom Corpus, TSCCv2 Caines et al., 2022, CIMA Stasaski et al., 2020, the Multicultural Classroom Discourse Dataset Rapanta et al., 2021, MathDial Macina et al., 2023, and Conversational Uptake [Demszky et al., 2021].
We are evaluating LLaMa-3 for this task. Instead of using programmable fine-tuning libraries such as Axolotl (link) or Huggingface TRL (link), we are employing the more general command-line LLaMA-Factory (link) toolkit that facilitates the fine-tuning of various well-known LLMs on custom data. Parameter-efficient fine-tuning is achieved via the QLoRA method Dettmers et al., 2023.
Number of conversation turns and words in the original datasets and after splitting long conversations:
| Dataset | Turns (Original) | Words (Original) | Turns (Split turns) | Words (Split turns) |
|---|---|---|---|---|
| TSCC v2 | 570 | 788k | 1074 | 786k |
| CIMA | 1135 | 44k | 1135 | 38k |
| MathDial | 2861 | 923k | 2876 | 879k |
| Multicultural | 5 | 614k | 643 | 614k |
| Uptake | 774 | 35k | 775 | 34k |
| Total | 5345 | 2404k | 6503 | 2351k |
Usage Guide
This project was executed on an Ubuntu 22.04.3 system running Linux kernel 6.8.0-40-generic.
Installation
To get started, you first need to set up the environment using the LLaMA-Factory project. Please refer to the official LLaMA-Factory repository for more details.
You can install the project by running the following commands:
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]"
Execution
In the DeMINT project, the model was utilized to create a REST API. Below is an example of how to configure and run it.
Setting Server Configuration
To specify the port and server address, use the following environment variables:
To set the port and the address of the server:
# Default 8000
export KIND_TEACHER_PORT=8000
# Default localhost
export KIND_TEACHER_HOST="localhost"
Running the Program
Once the environment is configured, you can execute the program by running the following command:
llamafactory-cli api run_api_inference_1.yaml
API Call from Client
address="localhost"
port=8000
type_message = {"GET": "/models", "POST": "/chat/completions"}
url = f'http://{address}:{port}/v1{type_message["POST"]}'
headers = {
'accept': 'application/json',
'Content-Type': 'application/json'
}
messages = [
{
"role": "system", # "user", "assistant" or "system"
"content": "You are a kind teacher that help students with their problems.",
},
{
"role": "user", # "user", "assistant" or "system"
"content": "Hello teacher",
"tool_calls": []
},
{
"role": "assistant", # "user", "assistant" or "system"
"content": "Hello student!",
},
{
"role": "user", # "user", "assistant" or "system"
"content": "Can you help me to understand the past perfect of english?",
"tool_calls": []
},
]
data = {
"model": "Transducens/kind_teacher",
"messages": messages, # messages must be formatted in the required format
"tools": [],
"do_sample": True,
"temperature": 1.0,
"top_p": 0.7,
"n": 1, # number of completions (responses) to generate
"max_tokens": 150,
"stream": False
}
response = requests.post(url, headers=headers, data=json.dumps(data))