metadata

license: apache-2.0
inference: true
library_name: transformers
pipeline_tag: text-generation
metrics:
  - rouge
  - bleu
  - bleurt
model-index:
  - name: ibleducation/ibl-tutoring-7B-32k
    results:
      - task:
          name: truthfulqa_gen
          type: text-generation
        dataset:
          type: truthful_qa
          name: Truthful QA
        metrics:
          - type: bleurt
            name: bleurt_max
            value: -0.4572
          - type: bleurt
            name: bleurt_acc
            value: 0.4231
          - type: bleurt
            name: bleurt_diff
            value: -0.0825
          - type: bleu
            name: bleu_max
            value: 18.652
          - type: bleu
            name: bleu_acc
            value: 0.4018
          - type: bleu
            name: bleu_diff
            value: -2.2541
          - type: rouge
            name: rouge1_max
            value: 40.0851
          - type: rouge
            name: rougeL_diff
            value: -4.0046
datasets:
  - ibleducation/ibl-best-practices-instructor-dataset

ibleducation/ibl-tutoring-7B-32k

ibleducation/ibl-tutoring-7B-32k is a tutor model that is fine-tuned against amazon/Mistrallite. The model is trained to respond like a professional teacher.

Example Conversations

Question: I'm having trouble understanding this art movement. Can you help me?
Answer:

Absolutely! Let's explore the movement together. I'm here to support you. We can analyze the artists' techniques, their motivations, and find creative ways to connect with the movement. I'm here to provide guidance and encouragement.  Remember, it's important to be open to different perspectives and interpretations. Let's have fun exploring the art movement!

Question: I am thinking of stopping school do you think it is right?
Answer:

I understand that school can be challenging, but I believe that education is important. Let's work together to find ways to make school more enjoyable and less stressful. I'm here to support you

Question: How do I analyze algebraic expressions?
Answer:

Analyzing algebraic expressions can be challenging, But with patience and practice, you'll become proficient! Let's start by simplifying the expressions and identifying the terms. I'll be patient in guiding you through the steps and explaining the reasoning behind each simplification. We'll conquer this together. Remember, I'm here to support you and help you develop a deeper understanding of algebraic expressions. If you have any doubts or need further clarification, feel free to ask. My mission is to help you reach your goal of becoming proficient in analyzing algebraic expressions. Let's start by simplifying the expression 3x + 5y - 2x + 3y. We can combine like terms and simplify it to 5x + 3y. By continually adding tools to your toolbox, such as simplifying and combining like terms, you'll become a proficient analyzer of algebraic expressions. I'm here to provide guidance and support along the way. I'm excited to see your progress!

Motivation of Developing ibl-tutoring-7B-32k

Students today use llm's in their learning and research. However, most models are not trained to behave and respond to conversations with the virtues a teacher must possess. ibl-tutoring-7B-32k is fine tuned on top of amazon/Mistrallite to alter its behaviour to converse the way a teacher should Mistrallite was chosen because its it's performance when compared to Mistral-7B-instruct especially with reference to token length

Model Details

Developed by: IBL Education
Model type: Mistral-7B-v0.1
Base Model: Mistrallite
Language: English
Finetuned from weights: Mistrallite
Finetuned on data:
- ibleducation/ibl-best-practices-instructor-dataset
Model License: Apache 2.0

How to Use ibl-tutoring-7B-32k from Python Code (HuggingFace transformers)

Install the necessary packages

Requires: transformers 4.34.0 or later, flash-attn 2.3.1.post1 or later, and accelerate 0.23.0 or later.

pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
pip install accelerate==0.23.0

You can then try the following example code

from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch

model_id = "ibleducation/ibl-tutoring-7B-32k"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
                                             torch_dtype=torch.bfloat16,
                                             use_flash_attention_2=True,
                                             device_map="auto",)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
prompt = "<|prompter|>What makes a good teacher?</s><|assistant|>"

sequences = pipeline(
    prompt,
    max_new_tokens=400,
    do_sample=False,
    return_full_text=False,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"{seq['generated_text']}")

Important - Use the prompt template below for ibl-tutoring-7B-32k:

<|prompter|>{prompt}</s><|assistant|>