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+ BigScience RAIL License v1.0
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+ dated May 19, 2022
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+ This is a license (the “License”) between you (“You”) and the participants of BigScience (“Licensor”). Whereas the Apache 2.0 license was applicable to resources used to develop the Model, the licensing conditions have been modified for the access and distribution of the Model. This has been done to further BigScience’s aims of promoting not just open-access to its artifacts, but also a responsible use of these artifacts. Therefore, this Responsible AI License (RAIL)[1] aims at having an open and permissive character while striving for responsible use of the Model.
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+ BigScience is a collaborative open innovation project aimed at the responsible development and use of large multilingual datasets and Large Language Models (“LLM”), as well as, the documentation of best practices and tools stemming from this collaborative effort. Further, BigScience participants wish to promote collaboration and sharing of research artifacts - including the Model - for the benefit of society, pursuant to this License.
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+ 5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
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+ END OF TERMS AND CONDITIONS
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+ Attachment A
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+ Use Restrictions
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+ You agree not to use the Model or Derivatives of the Model:
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+ 1. In any way that violates any applicable national, federal, state, local or international law or regulation;
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+ 2. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
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+ 3. To generate or disseminate verifiably false information with the purpose of harming others;
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+ 4. To generate or disseminate personal identifiable information that can be used to harm an individual;
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+ 5. To generate or disseminate information or content, in any context (e.g. posts, articles, tweets, chatbots or other kinds of automated bots) without expressly and intelligibly disclaiming that the text is machine generated;
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+ 6. To defame, disparage or otherwise harass others;
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+ 7. To impersonate or attempt to impersonate others;
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+ 8. For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
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+ 9. For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics
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+ 10. To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
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+ 11. For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
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+ 12. To provide medical advice and medical results interpretation;
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+ 13. To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
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+
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+ ________________
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+ [1] https://arxiv.org/pdf/2011.03116.pdf
README.md CHANGED
@@ -1,3 +1,138 @@
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  ---
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  license: bigscience-bloom-rail-1.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: bigscience-bloom-rail-1.0
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+ datasets:
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+ - nicholasKluge/fine-tuning-instruct-aira
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+ - Dahoas/synthetic-instruct-gptj-pairwise
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+ - databricks/databricks-dolly-15k
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+ - HuggingFaceH4/instruction-dataset
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+ language:
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+ - pt
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+ metrics:
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+ - bleu
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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: <|startoftext|>Olá! Qual o seu nome?<|endoftext|>
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+ example_title: Olá
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+ - text: >-
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+ <|startoftext|>Você pode me explicar o que é aprendizagem de
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+ máquina?<|endoftext|>
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+ example_title: Aprendizagem de máquina
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+ - text: <|startoftext|>Você sabe alguma coisa sobre ética das virtudes<|endoftext|>
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+ example_title: Ética das virtudes
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+ - text: <|startoftext|>O que posso fazer para alegrar minha namorada?<|endoftext|>
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+ example_title: Conselho
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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: 100
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+ length_penalty: 0.3
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+ early_stopping: True
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  ---
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+ # Aira-Instruct-PT-1B7 (Portuguese)
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+
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+ `Aira-Instruct-PT-1B7` is a instruction-tuned GPT-style model based on [BLOOM](https://huggingface.co/bigscience/bloom-1b7). 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-PT-1B7` instruction-tuning was achieved via conditional text generation.
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+
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+ 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.
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+
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+ Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo-Portuguese).
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+
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+ ## Details
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+
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+ - **Size:** 559,012,864 total parameters
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+ - **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/fine-tuning-instruct-aira)
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+ - **Language:** Portuguese
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+ - **Number of Epochs:** 2
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+ - **Batch size:** 6
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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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+
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+ | Epoch|Training Loss|Validation Loss|
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+ |---|---|---|
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+ | 1 |0.888486|0.744728|
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+ | 2 |0.749612|0.673719|
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+
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+ This repository has the notebook used to train this model.
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+
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+ ## Usage
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+
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+ Two 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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+ `<|startoftext|>`What is a language model?`<|endoftext|>`A language model is a probability distribution over a vocabulary.`<|endoftext|>`
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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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-Instruct-PT-1B7')
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+ aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-PT-1B7')
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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.eos_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=200,
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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: 👤 Olá! Como você se chama?
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+
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+ >>>Response 1: 🤖 Olá! Meu nome é Aira e sou um chatbot projetado para conversar sobre Ética e Segurança da IA. Se você precisar de ajuda com um assunto diferente, por favor, peça "ajuda".
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+ >>>Response 2: 🤖 Olá! Meu nome é Aira e sou um chatbot treinado para responder perguntas sobre Ética e Segurança da IA. Se você precisar de ajuda para navegar em nossa conversa, não hesite em pedir ajuda.
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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-Instruct-PT-560M},
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+ author = {Nicholas Kluge Corrêa and Carolina Del Pino},
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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-Instruct-PT-1B7` is licensed under the RAIL License since it is a model derived from BLOOM. See the [LICENSE](LICENSE) file for more details.
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