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--- |
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license: other |
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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: >- |
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<|startofinstruction|>Can you explain what is Machine |
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Learning?<|endofinstruction|> |
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example_title: Machine Learning |
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- text: >- |
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<|startofinstruction|>Do you know anything about virtue |
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ethics?<|endofinstruction|> |
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example_title: Ethics |
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- text: >- |
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<|startofinstruction|>How can I make my girlfriend |
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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_length: 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: 0.25 |
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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-OPT-125M |
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`Aira-2` is the second version of the Aira instruction-tuned series. `Aira-OPT-125M` is an instruction-tuned OPT-style model based on [OPT](https://huggingface.co/facebook/opt-125m). 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:** 125,237,760 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:** 5 |
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- **Batch size:** 32 |
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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:** 0.25 KgCO2 (Singapore) |
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- **Total Energy Consumption:** 0.52 kWh |
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This repository has the [notebook](AIRA_FineTuning.ipynb) 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-OPT-125M') |
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aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-OPT-125M') |
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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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# OPT tokenizer already adds the BOS token, so we do not need to add it manually |
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inputs = tokenizer(question + tokenizer.sep_token, return_tensors="pt").to(device) |
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responses = aira.generate(**inputs, |
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bos_token_id=tokenizer.bos_token_id, |
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pad_token_id=tokenizer.pad_token_id, |
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eos_token_id=tokenizer.eos_token_id, |
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do_sample=True, |
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top_k=50, |
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max_length=500, |
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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 (OPT) | 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-OPT-125M](https://huggingface.co/nicholasKluge/Aira-OPT-125M) | **43.34** | **24.65** | **49.11** | **56.27** | | | |
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| OPT-125M | 40.29 | 22.78 | 42.88 | 55.21 | | | |
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| [Aira-OPT-350M](https://huggingface.co/nicholasKluge/Aira-OPT-350M) | 24.95 | **25.00** | **42.32** | 45.53 | | | |
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| OPT-350M | **40.62** | 23.97 | 41.00 | **56.91** | | | |
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| [Aira-OPT-1B3](https://huggingface.co/nicholasKluge/Aira-OPT-1B3) | **43.90** | 28.41 | **46.59** | **56.70** | | | |
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| OPT-1.3b | 40.91 | **29.69** | 38.68 | 54.36 | | | |
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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/)). The notebook used to make these evaluations is available in the [this repo](lm_evaluation_harness.ipynb). |
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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-OPT-125M}, |
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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-OPT-125M` 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. |
