metadata
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
ibleducation/ibl-tutoring-neural-chat-7B
ibleducation/ibl-tutoring-neural-chat-7B is a model finetuned on top of Intel/neural-chat-7b-v1-1. This model is finetuned to give responses in a way befitting of a professional teacher. It is finetuned to exhibit characteristics and virtues such as compassion, encouragement, friendliness and more.
Benchmarks
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| hellaswag | 0 | acc | 0.5355 | ± | 0.0050 |
| acc_norm | 0.6977 | ± | 0.0046 | ||
| truthfulqa_mc | 1 | mc1 | 0.2876 | ± | 0.0158 |
| mc2 | 0.4555 | ± | 0.0158 |
Model Details
- Developed by: IBL Education
- Base Model: [Intel/neural-chat-7b-v1-1](https://huggingface.co/ibleducation/ibl-tutoring-neural-chat-7B
- Language: English
- Finetuned from weights: Intel/neural-chat-7b-v1-1
- Finetuned on data:
- ibl-best-practices-instructor-dataset (private)
- Model License: Apache 2.0
How to Use ibl-tutoring-chat-7B Model from Python Code (HuggingFace transformers)
Install the necessary packages
Requires: transformers 4.31.0 , and accelerate 0.23.0 or later.
pip install transformers==4.31.0
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-neural-chat-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True,
)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<s>What makes a good teacher?</s>"
response = pipeline(prompt)
print(response['generated_text'])
Important - Use the prompt template below for ibl-tutoring-chat-7B:
<s>{prompt}</s>