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---
license: mit
datasets:
- ibleducation/ibl-khanacademy-transcripts
language:
- en
library_name: transformers
---
# 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:
```code
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
- **Developed by:** [IBL Education](https://ibl.ai)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Base Model:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Language:** English
- **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Finetuned on data:**
- [ibleducation/ibl-khanacademy-transcripts](https://huggingface.co/datasets/ibleducation/ibl-khanacademy-transcripts)
- **Model License:** MIT
## How to Get Started with the Model
### Install the necessary packages
Requires: [transformers](https://pypi.org/project/transformers/) > 4.35.0
```shell
pip install transformers
pip install accelerate
```
### You can then try the following example code
```python
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]
``` |