|
--- |
|
datasets: |
|
- nicholasKluge/instruct-aira-dataset |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
library_name: transformers |
|
tags: |
|
- alignment |
|
- instruction tuned |
|
- text generation |
|
- conversation |
|
- assistant |
|
pipeline_tag: text-generation |
|
widget: |
|
- text: <|startofinstruction|>What is your name?<|endofinstruction|> |
|
example_title: Greetings |
|
- text: >- |
|
<|startofinstruction|>Can you explain what is Machine |
|
Learning?<|endofinstruction|> |
|
example_title: Machine Learning |
|
- text: >- |
|
<|startofinstruction|>Do you know anything about virtue |
|
ethics?<|endofinstruction|> |
|
example_title: Ethics |
|
- text: >- |
|
<|startofinstruction|>How can I make my girlfriend |
|
happy?<|endofinstruction|> |
|
example_title: Advise |
|
inference: |
|
parameters: |
|
repetition_penalty: 1.2 |
|
temperature: 0.2 |
|
top_k: 30 |
|
top_p: 0.3 |
|
max_length: 200 |
|
length_penalty: 0.3 |
|
early_stopping: true |
|
co2_eq_emissions: |
|
emissions: 0.29 |
|
source: CodeCarbon |
|
training_type: fine-tuning |
|
geographical_location: United States of America |
|
hardware_used: NVIDIA A100-SXM4-40GB |
|
license: apache-2.0 |
|
--- |
|
# Aira-2-355M |
|
|
|
`Aira-2` is the second version of the Aira instruction-tuned series. `Aira-2-355M` is an instruction-tuned GPT-style model based on [GPT-2](https://huggingface.co/gpt2-medium). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc). |
|
|
|
Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo). |
|
|
|
## Details |
|
|
|
- **Size:** 354,825,216 parameters |
|
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset) |
|
- **Language:** English |
|
- **Number of Epochs:** 3 |
|
- **Batch size:** 16 |
|
- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8) |
|
- **GPU:** 1 NVIDIA A100-SXM4-40GB |
|
- **Emissions:** 0.29 KgCO2 (United States of America) |
|
- **Total Energy Consumption:** 0.83 kWh |
|
|
|
This repository has the [notebook](AIRA_FineTuning.ipynb) used to train this model. |
|
|
|
## Usage |
|
|
|
Three special tokens are used to mark the user side of the interaction and the model's response: |
|
|
|
`<|startofinstruction|>`What is a language model?`<|endofinstruction|>`A language model is a probability distribution over a vocabulary.`<|endofcompletion|>` |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import torch |
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-355M') |
|
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-355M') |
|
|
|
aira.eval() |
|
aira.to(device) |
|
|
|
question = input("Enter your question: ") |
|
|
|
inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token, return_tensors="pt").to(device) |
|
|
|
responses = aira.generate(**inputs, |
|
bos_token_id=tokenizer.bos_token_id, |
|
pad_token_id=tokenizer.pad_token_id, |
|
eos_token_id=tokenizer.eos_token_id, |
|
do_sample=True, |
|
top_k=50, |
|
max_length=500, |
|
top_p=0.95, |
|
temperature=0.7, |
|
num_return_sequences=2) |
|
|
|
print(f"Question: 👤 {question}\n") |
|
|
|
for i, response in enumerate(responses): |
|
print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}') |
|
``` |
|
|
|
The model will output something like: |
|
|
|
```markdown |
|
>>>Question: 👤 What is the capital of Brazil? |
|
|
|
>>>Response 1: 🤖 The capital of Brazil is Brasília. |
|
>>>Response 2: 🤖 The capital of Brazil is Brasília. |
|
``` |
|
|
|
## Limitations |
|
|
|
🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful. |
|
|
|
🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes. |
|
|
|
## Evaluation |
|
|
|
| Model (GPT-2) | Average | [ARC](https://arxiv.org/abs/1803.05457) | [TruthfulQA](https://arxiv.org/abs/2109.07958) | [ToxiGen](https://arxiv.org/abs/2203.09509) | | | |
|
|-----------------------------------------------------------------|-----------|-----------------------------------------|------------------------------------------------|---------------------------------------------|---|---| |
|
| [Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-2-124M) | **38.07** | **24.57** | **41.02** | **48.62** | | | |
|
| GPT-2 | 35.37 | 21.84 | 40.67 | 43.62 | | | |
|
| [Aira-2-355M](https://huggingface.co/nicholasKluge/Aira-2-355M) | **39.68** | **27.56** | 38.53 | **53.19** | | | |
|
| GPT-2-medium | 36.43 | 27.05 | **40.76** | 41.49 | | | |
|
| [Aira-2-774M](https://huggingface.co/nicholasKluge/Aira-2-774M) | **42.26** | **28.75** | **41.33** | **56.70** | | | |
|
| GPT-2-large | 35.16 | 25.94 | 38.71 | 40.85 | | | |
|
| [Aira-2-1B5](https://huggingface.co/nicholasKluge/Aira-2-1B5) | **42.22** | 28.92 | **41.16** | **56.60** | | | |
|
| GPT-2-xl | 36.84 | **30.29** | 38.54 | 41.70 | | | |
|
|
|
* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)). The notebook used to make these evaluations is available in the [this repo](lm_evaluation_harness.ipynb). |
|
|
|
## Cite as 🤗 |
|
|
|
```latex |
|
|
|
@misc{nicholas22aira, |
|
doi = {10.5281/zenodo.6989727}, |
|
url = {https://huggingface.co/nicholasKluge/Aira-2-355M}, |
|
author = {Nicholas Kluge Corrêa}, |
|
title = {Aira}, |
|
year = {2023}, |
|
publisher = {HuggingFace}, |
|
journal = {HuggingFace repository}, |
|
} |
|
|
|
``` |
|
|
|
## License |
|
|
|
The `Aira-2-355M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details. |
|
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
|
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-2-355M) |
|
|
|
| Metric | Value | |
|
|-----------------------|---------------------------| |
|
| Avg. | 27.0 | |
|
| ARC (25-shot) | 27.56 | |
|
| HellaSwag (10-shot) | 38.92 | |
|
| MMLU (5-shot) | 27.26 | |
|
| TruthfulQA (0-shot) | 38.53 | |
|
| Winogrande (5-shot) | 53.75 | |
|
| GSM8K (5-shot) | 0.0 | |
|
| DROP (3-shot) | 2.99 | |
|
|
