--- license: apache-2.0 datasets: - Dahoas/synthetic-instruct-gptj-pairwise - databricks/databricks-dolly-15k - HuggingFaceH4/instruction-dataset - nicholasKluge/instruct-aira-dataset language: - en metrics: - bleu library_name: transformers tags: - alignment - instruction tuned - text generation - conversation - assistant pipeline_tag: text-generation widget: - text: <|startoftext|>Hello! What is your name?<|endoftext|> example_title: Greetings - text: <|startoftext|>Can you explain what is Machine Learning?<|endoftext|> example_title: Machine Learning - text: <|startoftext|>Do you know anything about virtue ethics?<|endoftext|> example_title: Ethics - text: <|startoftext|>How can I make my girlfried happy?<|endoftext|> 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 --- # Aira-Instruct-124M `Aira-Instruct-124M` is a instruction-tuned GPT-style model based on [GPT-2](https://huggingface.co/gpt2). The model was trained with a dataset composed of `prompt`, `completions`, generated via the [Self-Instruct](https://github.com/yizhongw/self-instruct) framework. `Aira-Instruct-124M` instruction-tuning was achieved via conditional text generation. The dataset used to train this model combines the following sources of data: the [`synthetic-instruct-gptj-pairwise`](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) dataset, the [`databricks_dolly_15k`](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) dataset, the [`instruction-dataset`](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) dataset, and a subset of [Aira's](https://github.com/Nkluge-correa/Aira-EXPERT) fine-tuning dataset, focused on Q&A related to Ethics, AI, AI safety, and other related topics. The dataset is available in both Portuguese and English. Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo). ## Details - **Size:** 124,441,344 parameters - **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset) - **Language:** English - **Number of Epochs:** 5 - **Batch size:** 32 - **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8) - **GPU:** 1 NVIDIA A100-SXM4-40GB - **Emissions:** 0.19 KgCO2 (United States of America) - **Total Energy Consumption:** 0.42 kWh | Epoch|Training Loss|Validation Loss| |---|---|---| | 1 |1.076073|0.692127| | 2 |0.680394|0.662053| | 3 |0.622054|0.651161| | 4 |0.577170|0.644864| | 5 |0.541509|0.644677| This repository has the notebook used to train this model. ## Usage Two special tokens are used to mark the user side of the interaction and the model's response: `<|startoftext|>`What is a language model?`<|endoftext|>`A language model is a probability distribution over a vocabulary.`<|endoftext|>` ```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-Instruct-124M') aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-124M') aira.eval() aira.to(device) question = input("Enter your question: ") inputs = tokenizer(tokenizer.bos_token + question + tokenizer.eos_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=200, 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: 👤 Hello! What is your name? >>>Response 1: 🤖 Hi there! I am Aira, a chatbot designed to answer questions about AI ethics and AI safety. If you need assistance navigating our conversation, please feel free to ask! >>>Response 2: 🤖 Hi there! My name is Aira, and I'm a chatbot designed to answer questions related to AI ethics and AI Safety. If you need assistance, feel free to ask, and I'll be happy to help you out. ``` ## 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. ## Cite as 🤗 ```latex @misc{nicholas22aira, doi = {10.5281/zenodo.6989727}, url = {https://huggingface.co/nicholasKluge/Aira-Instruct-124M}, author = {Nicholas Kluge Corrêa and Carolina Del Pino}, title = {Aira}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, } ``` ## License The `Aira-Instruct-124M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.