ibleducation/ibl-neural-edu-content-7B

ibleducation/ibl-neural-edu-content-7B is a model finetuned on top of mistralai/Mistral-7B-v0.1

The model is finetuned to generate appropriate subtitles that can further be used with video generators to create tutorial videos. The content of generated response includes full timestamps as well as content. The content provides a full tutorial to a topic passed as input.

Example Conversations

  1. Question: Information Theory
    Answer:
     WEBVTT
     Kind: captions
     Language: en
     
     00:00:00.320 --> 00:00:01.153
     - [Instructor] In this video,
     
     00:00:01.153 --> 00:00:03.270
     we're going to talk about information.
     
     00:00:03.270 --> 00:00:05.100
     And I know what you're thinking.
     
     00:00:05.100 --> 00:00:07.030
     You're thinking, I know what information is.
     
     00:00:07.030 --> 00:00:08.860
     I read the newspaper every day.
     
     00:00:08.860 --> 00:00:10.860
     I watch TV shows.
     
     00:00:10.860 --> 00:00:12.690
     I talk to my friends.
     
     00:00:12.690 --> 00:00:14.520
     I know what information is.
     
     00:00:14.520 --> 00:00:16.450
     But what we're going to
     talk about in this video
     
     00:00:16.450 --> 00:00:18.280
     is a very specific definition
     
     00:00:18.280 --> 00:00:20.150
     of what information is.
     
     00:00:20.150 --> 00:00:22.150
     And it's a very mathematical definition.
     
     00:00:22.150 --> 00:00:24.150
     And it's a very specific definition
    [.... content shortened for brevity ...]
    

Model Details

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-neural-edu-content-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]Information Theory[/INST] "

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

Important - Use the prompt template below:

<s>[INST]{prompt}[/INST] 
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Dataset used to train iblai/ibl-neural-edu-content-7B