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README.md ADDED
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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|>How should I call you?<|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_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: 1.78
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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-2-1B1
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+
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+ `Aira-2` is the second version of the Aira instruction-tuned series. `Aira-2-1B1` is an instruction-tuned GPT-style model based on [TinyLlama-1.1B](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-480k-1T). 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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+
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+ Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).
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+
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+ ## Details
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+
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+ - **Size:** 1,261,545,472 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:** 1.78 KgCO2 (Singapore)
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+ - **Total Energy Consumption:** 3.64 kWh
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+
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+ This repository has the [notebook](AIRA_FineTuning.ipynb) used to train this model.
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+
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+ ## Usage
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+
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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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+
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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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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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+ tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-1B1')
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+ aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-1B1')
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+
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+ aira.eval()
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+ aira.to(device)
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+
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+ question = input("Enter your question: ")
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+
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+ inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token, return_tensors="pt").to(device)
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+
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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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+
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+ print(f"Question: 👤 {question}\n")
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+
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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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+
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+ The model will output something like:
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+
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+ ```markdown
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+ >>>Question: 👤 What is the capital of Brazil?
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+
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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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+
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+ ## Limitations
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+
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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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+
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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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+
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+ ## Cite as 🤗
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+
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+ ```latex
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+
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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-2-1B1},
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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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+ ```
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+
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+ ## License
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+
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+ The `Aira-2-1B1` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.