Aira-OPT-350M / README.md

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	---
	license: other
	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: "Can you explain what is Machine Learning?<\|endofinstruction\|>"
	example_title: Machine Learning
	- text: "Do you know anything about virtue ethics?<\|endofinstruction\|>"
	example_title: Ethics
	- text: "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_new_tokens: 200
	length_penalty: 0.3
	early_stopping: true
	co2_eq_emissions:
	emissions: 330
	source: CodeCarbon
	training_type: fine-tuning
	geographical_location: United States of America
	hardware_used: NVIDIA A100-SXM4-40GB
	---
	# Aira-OPT-350M

	Aira-2 is the second version of the Aira instruction-tuned series. Aira-OPT-350M is an instruction-tuned model based on [OPT](https://huggingface.co/facebook/opt-350m). 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: 331,195,392 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 = 1e-4, epsilon = 1e-8)
	- GPU: 1 NVIDIA A100-SXM4-40GB
	- Emissions: 0.33 KgCO2 (Netherlands)
	- Total Energy Consumption: 0.85 kWh

	This repository has the [source code](https://github.com/Nkluge-correa/Aira) 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-OPT-350M')
	aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-OPT-350M')

	aira.eval()
	aira.to(device)

	question = input("Enter your question: ")

	inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token,
	add_special_tokens=False,
	return_tensors="pt").to(device)

	responses = aira.generate(**inputs, 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

	- Hallucinations: This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.

	- Biases and Toxicity: This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.

	- Repetition and Verbosity: The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.

	## Evaluation

	\| Model \| Average \| [ARC](https://arxiv.org/abs/1803.05457) \| [TruthfulQA](https://arxiv.org/abs/2109.07958) \| [ToxiGen](https://arxiv.org/abs/2203.09509) \|
	\|---------------------------------------------------------------------\|-----------\|-----------------------------------------\|------------------------------------------------\|---------------------------------------------\|
	\| [Aira-OPT-125M](https://huggingface.co/nicholasKluge/Aira-OPT-125M) \| 43.34 \| 24.65 \| 49.11 \| 56.27 \|
	\| OPT-125M \| 40.29 \| 22.78 \| 42.88 \| 55.21 \|
	\| [Aira-OPT-350M](https://huggingface.co/nicholasKluge/Aira-OPT-350M) \| 41.56 \| 25.00 \| 42.13 \| 57.55 \|
	\| OPT-350M \| 40.62 \| 23.97 \| 41.00 \| 56.91 \|
	\| [Aira-OPT-1B3](https://huggingface.co/nicholasKluge/Aira-OPT-1B3) \| 43.90 \| 28.41 \| 46.59 \| 56.70 \|
	\| OPT-1.3b \| 40.91 \| 29.69 \| 38.68 \| 54.36 \|

	* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)).

	## Cite as 🤗

	```latex

	@misc{nicholas22aira,
	doi = {10.5281/zenodo.6989727},
	url = {https://huggingface.co/nicholasKluge/Aira-2-350M},
	author = {Nicholas Kluge Corrêa},
	title = {Aira},
	year = {2023},
	publisher = {HuggingFace},
	journal = {HuggingFace repository},
	}

	```

	## License

	Aira-OPT-350M is licensed under the OPT-175B License Agreement, Copyright (c) Meta Platforms, Inc. All Rights Reserved. See the [LICENSE](LICENSE.md) file for more details.