ibl-multiple-choice-7B

ibleducation/ibl-multiple-choice-7B

ibleducation/ibl-multiple-choice-7B is a model finetuned on top of mistralai/Mistral-7B-Instruct-v0.1

The model is finetuned to generate a multiple choice questions. The output of the model is a json object with the following entries

1. category: The topic area of the question
2. qtext: The question text
3. ra: The aid of the correct answer
4. answers: a list of possible answer choices each with an aid (answer id) and atext (answer text.)

Example Conversations

1. Question: Photosynthesis
   Answer:

     {
      "category": "Photosynthesis",
      "qtext": "The chlorophyll fluorescence measurement technique is based on the emission of fluorescence by the chlorophylls present in the photosynthetic pigmentation:",
      "ra": 4,
      "answers": [
        {"aid": 1, "atext": "It is used to determine the light absorption characteristics of the pigments."},
        {"aid": 2, "atext": "It is used to determine the light emission characteristics of the pigments."},
        {"aid": 3, "atext": "It is used to determine the kinetics of light absorption by the pigments."},
        {"aid": 4, "atext": "It is used to determine the kinetics of light emission by the pigments."},
        {"aid": 5, "atext": "It is used to determine the energy that the pigments emit when they absorb light."}
      ]
    }
   

Model Details

• Developed by: IBL Education
• Model type: Mistral-7B-v0.1
• Base Model: Mistral-7B-Instruct-v0.1
• Language: English
• Finetuned from weights: Mistral-7B-Instruct-v0.1
• Finetuned on data:
  • Head_qa
• Model License: MIT

How to Get Started with the Model

Install the necessary packages

Requires: transformers > 4.35.0

pip install transformers
pip install accelerate

You can then try the following example code

from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch

model_id = "ibleducation/ibl-multiple-choice-7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
  model_id,
  device_map="auto",
)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
prompt = "<s>[INST] Algebra [/INST] "

response = pipeline(prompt)
print(response['generated_text'])

Important - Use the prompt template below:

<s>[INST] {prompt} [/INST]