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--- |
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license: apache-2.0 |
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datasets: |
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- nicholasKluge/instruct-aira-dataset |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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tags: |
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- alignment |
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- instruction tuned |
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- text generation |
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- conversation |
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- assistant |
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pipeline_tag: text-generation |
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widget: |
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- text: "<|startofinstruction|>What is your name?<|endofinstruction|>" |
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example_title: Greetings |
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- text: "<|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>" |
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example_title: Machine Learning |
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- text: "<|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>" |
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example_title: Ethics |
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- text: "<|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>" |
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example_title: Advise |
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inference: |
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parameters: |
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repetition_penalty: 1.2 |
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temperature: 0.2 |
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top_k: 30 |
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top_p: 0.3 |
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max_new_tokens: 200 |
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length_penalty: 0.3 |
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early_stopping: true |
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co2_eq_emissions: |
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emissions: 1.69 |
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source: CodeCarbon |
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training_type: fine-tuning |
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geographical_location: United States of America |
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hardware_used: NVIDIA A100-SXM4-40GB |
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--- |
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# Aira-2-1B5 |
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`Aira-2` is the second version of the Aira instruction-tuned series. `Aira-2-1B5` is an instruction-tuned GPT-style model based on [GPT-2](https://huggingface.co/gpt2-xl). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc). |
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Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo). |
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## Details |
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- **Size:** 1,557,614,400 parameters |
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- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset) |
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- **Language:** English |
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- **Number of Epochs:** 3 |
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- **Batch size:** 4 |
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- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8) |
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- **GPU:** 1 NVIDIA A100-SXM4-40GB |
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- **Emissions:** 1.69 KgCO2 (Singapore) |
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- **Total Energy Consumption:** 3.47 kWh |
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This repository has the [source code](https://github.com/Nkluge-correa/Aira) used to train this model. |
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## Usage |
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Three special tokens are used to mark the user side of the interaction and the model's response: |
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`<|startofinstruction|>`What is a language model?`<|endofinstruction|>`A language model is a probability distribution over a vocabulary.`<|endofcompletion|>` |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-1B5') |
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aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-1B5') |
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aira.eval() |
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aira.to(device) |
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question = input("Enter your question: ") |
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inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token, return_tensors="pt").to(device) |
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responses = aira.generate(**inputs, |
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do_sample=True, |
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top_k=50, |
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top_p=0.95, |
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temperature=0.7, |
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num_return_sequences=2) |
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print(f"Question: 👤 {question}\n") |
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for i, response in enumerate(responses): |
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print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}') |
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``` |
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The model will output something like: |
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```markdown |
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>>>Question: 👤 What is the capital of Brazil? |
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>>>Response 1: 🤖 The capital of Brazil is Brasília. |
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>>>Response 2: 🤖 The capital of Brazil is Brasília. |
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``` |
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## Limitations |
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🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful. |
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🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes. |
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## Evaluation |
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| 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) | | | |
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|-----------------------------------------------------------------|-----------|-----------------------------------------|------------------------------------------------|---------------------------------------------|---|---| |
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| [Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-2-124M) | **38.07** | **24.57** | **41.02** | **48.62** | | | |
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| GPT-2 | 35.37 | 21.84 | 40.67 | 43.62 | | | |
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| [Aira-2-355M](https://huggingface.co/nicholasKluge/Aira-2-355M) | **39.68** | **27.56** | 38.53 | **53.19** | | | |
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| GPT-2-medium | 36.43 | 27.05 | **40.76** | 41.49 | | | |
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| [Aira-2-774M](https://huggingface.co/nicholasKluge/Aira-2-774M) | **42.26** | **28.75** | **41.33** | **56.70** | | | |
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| GPT-2-large | 35.16 | 25.94 | 38.71 | 40.85 | | | |
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| [Aira-2-1B5](https://huggingface.co/nicholasKluge/Aira-2-1B5) | **42.22** | 28.92 | **41.16** | **56.60** | | | |
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| GPT-2-xl | 36.84 | **30.29** | 38.54 | 41.70 | | | |
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* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)). |
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## Cite as 🤗 |
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```latex |
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@misc{nicholas22aira, |
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doi = {10.5281/zenodo.6989727}, |
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url = {https://huggingface.co/nicholasKluge/Aira-2-1B5}, |
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author = {Nicholas Kluge Corrêa}, |
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title = {Aira}, |
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year = {2023}, |
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publisher = {HuggingFace}, |
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journal = {HuggingFace repository}, |
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} |
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``` |
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## License |
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The `Aira-2-1B5` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details. |
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