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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- meta-llama/Meta-Llama-3.1-8B |
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--- |
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# Empathetic teacher model |
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## Overview |
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This is a LLM fine-tuned with real-life, ideally-empathetic teacher-student conversations. |
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This model processes the recent conversation history and provides guidance on how a teacher might respond to the student's utterance. |
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To fine-tune an open-weighted LLM to act as this generic teacher, we have used the following datasets: |
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the Teacher-Student Chatroom Corpus, TSCCv2 [Caines et al., 2022](https://aclanthology.org/2022.nlp4call-1.3), |
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CIMA [Stasaski et al., 2020](https://aclanthology.org/2020.bea-1.5), |
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the Multicultural Classroom Discourse Dataset [Rapanta et al., 2021](https://www.sciencedirect.com/science/article/pii/S2352340921007940), |
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MathDial [Macina et al., 2023](https://aclanthology.org/2023.findings-emnlp.372), and |
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Conversational Uptake [Demszky et al., 2021]. |
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We are evaluating LLaMa-3 for this task. |
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Instead of using programmable fine-tuning libraries such as Axolotl ([link](https://github.com/OpenAccess-AI-Collective/axolotl)) |
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or Huggingface TRL ([link](https://github.com/huggingface/trl)), |
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we are employing the more general command-line LLaMA-Factory ([link](https://github.com/hiyouga/LLaMA-Factory)) toolkit |
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that facilitates the fine-tuning of various well-known LLMs on custom data. |
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Parameter-efficient fine-tuning is achieved via the QLoRA method [Dettmers et al., 2023](https://proceedings.neurips.cc/paper_files/paper/2023/file/1feb87871436031bdc0f2beaa62a049b-Paper-Conference.pdf). |
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Number of conversation turns and words in the original datasets and after splitting long conversations: |
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| **Dataset** | **Turns (Original)** | **Words (Original)** | **Turns (Split turns)** | **Words (Split turns)** | |
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|------------------|:--------------------:|:--------------------:|:-----------------------:|:-----------------------:| |
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| TSCC v2 | 570 | 788k | 1074 | 786k | |
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| CIMA | 1135 | 44k | 1135 | 38k | |
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| MathDial | 2861 | 923k | 2876 | 879k | |
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| Multicultural | 5 | 614k | 643 | 614k | |
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| Uptake | 774 | 35k | 775 | 34k | |
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| **Total** | **5345** | **2404k** | **6503** | **2351k** | |
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## Usage Guide |
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This project was executed on an Ubuntu 22.04.3 system running Linux kernel 6.8.0-40-generic. |
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### Installation |
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To get started, you first need to set up the environment using the **LLaMA-Factory** project. Please refer to the official [LLaMA-Factory repository](https://github.com/hiyouga/LLaMA-Factory) for more details. |
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You can install the project by running the following commands: |
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```bash |
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git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git |
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cd LLaMA-Factory |
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pip install -e ".[torch,metrics]" |
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``` |
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### Execution |
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In the DeMINT project, the model was utilized to create a REST API. Below is an example of how to configure and run it. |
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**Setting Server Configuration** |
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To specify the port and server address, use the following environment variables: |
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To set the port and the address of the server: |
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```bash |
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# Default 8000 |
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export KIND_TEACHER_PORT=8000 |
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# Default localhost |
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export KIND_TEACHER_HOST="localhost" |
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``` |
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**Running the Program** |
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Once the environment is configured, you can execute the program by running the following command: |
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```bash |
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llamafactory-cli api run_api_inference_1.yaml |
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``` |
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**API Call from Client** |
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```python |
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address="localhost" |
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port=8000 |
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type_message = {"GET": "/models", "POST": "/chat/completions"} |
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url = f'http://{address}:{port}/v1{type_message["POST"]}' |
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headers = { |
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'accept': 'application/json', |
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'Content-Type': 'application/json' |
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} |
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messages = [ |
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{ |
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"role": "system", # "user", "assistant" or "system" |
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"content": "You are a kind teacher that help students with their problems.", |
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}, |
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{ |
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"role": "user", # "user", "assistant" or "system" |
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"content": "Hello teacher", |
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"tool_calls": [] |
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}, |
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{ |
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"role": "assistant", # "user", "assistant" or "system" |
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"content": "Hello student!", |
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}, |
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{ |
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"role": "user", # "user", "assistant" or "system" |
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"content": "Can you help me to understand the past perfect of english?", |
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"tool_calls": [] |
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}, |
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] |
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data = { |
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"model": "Transducens/kind_teacher", |
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"messages": messages, # messages must be formatted in the required format |
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"tools": [], |
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"do_sample": True, |
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"temperature": 1.0, |
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"top_p": 0.7, |
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"n": 1, # number of completions (responses) to generate |
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"max_tokens": 150, |
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"stream": False |
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} |
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response = requests.post(url, headers=headers, data=json.dumps(data)) |
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``` |
