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LICENSE

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+BigScience RAIL License v1.0
+dated May 19, 2022
+
+
+
+
+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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+                                                                    Section  I:  PREAMBLE
+
+
+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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+The development and use of LLMs, and broadly artificial intelligence (“AI”), does not come without concerns. The world has witnessed how just a few companies/institutions are able to develop LLMs, and moreover, how Natural Language Processing techniques might, in some instances, become a risk for the public in general. Concerns might come in many forms, from racial discrimination to the treatment of sensitive information. 
+
+
+BigScience believes in the intersection between open and responsible AI development, thus, this License aims to strike a balance between both in order to enable responsible open-science for large language models and future NLP techniques. 
+This License governs the use of the BigScience BLOOM models (and their derivatives) and is informed by both the BigScience Ethical Charter and the model cards associated with the BigScience BLOOM models. BigScience has set forth its Ethical Charter representing the values of  its community. Although the BigScience community does not aim to impose its values on potential users of this Model, it is determined to take tangible steps towards protecting the community from inappropriate uses of the work being developed by BigScience.
+Furthermore, the model cards for the BigScience BLOOM models will inform the user about the limitations of the Model, and thus serves as the basis of some of the use-based restrictions in this License (See Part II).
+
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+NOW THEREFORE, You and Licensor agree as follows:
+
+
+1. Definitions 
+
+
+1. "License" shall mean the terms and conditions for use, reproduction, and Distribution as defined in this document.
+2. “Data” means a collection of texts extracted from the BigScience Corpus used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License. The BigScience Corpus is a collection of existing sources of language data documented on the BigScience website.
+3. “Output” means the results of operating a Model as embodied in informational content resulting therefrom.
+4. “Model” means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the BigScience BLOOM model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part, on the Data using the Complementary Material. 
+5. “Derivatives of the Model” means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
+6. “Complementary Material” shall mean the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation. This includes any accompanying documentation, tutorials, examples etc.
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+8. “Licensor” means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
+9. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator.
+10. “Third Parties” means individuals or legal entities that are not under common control with Licensor or You.
+11. "Contribution" shall mean any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." 
+12. "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+
+
+                                              Section II:   INTELLECTUAL PROPERTY RIGHTS
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is filed.
+
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+1. Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+2. You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License; 
+3. You must cause any modified files to carry prominent notices stating that You changed the files; 
+4. You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+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). 
+6.  The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License. 
+
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+Section IV: OTHER PROVISIONS
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or modify the Output of the Model based on updates. You shall undertake reasonable efforts to use the latest version of the Model.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
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+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+END OF TERMS AND CONDITIONS
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+Attachment A
+Use Restrictions
+You agree not to use the Model or Derivatives of the Model:
+1. In any way that violates any applicable national, federal, state, local or international law or regulation;
+2. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+3. To generate or disseminate verifiably false information with the purpose of harming others;
+4. To generate or disseminate personal identifiable information that can be used to harm an individual;
+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; 
+6. To defame, disparage or otherwise harass others;
+7. To impersonate or attempt to impersonate others;
+8. For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
+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
+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;
+11. For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
+12. To provide medical advice and medical results interpretation; 
+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).
+
+
+________________
+[1] https://arxiv.org/pdf/2011.03116.pdf

README.md

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 ---
 license: bigscience-bloom-rail-1.0
+datasets:
+- nicholasKluge/fine-tuning-instruct-aira
+- Dahoas/synthetic-instruct-gptj-pairwise
+- databricks/databricks-dolly-15k
+- HuggingFaceH4/instruction-dataset
+language:
+- pt
+metrics:
+- bleu
+library_name: transformers
+tags:
+- alignment
+- instruction tuned
+- text generation
+- conversation
+- assistant
+pipeline_tag: text-generation
+widget:
+- text: <|startoftext|>Olá! Qual o seu nome?<|endoftext|>
+  example_title: Olá
+- text: >-
+    <|startoftext|>Você pode me explicar o que é aprendizagem de
+    máquina?<|endoftext|>
+  example_title: Aprendizagem de máquina
+- text: <|startoftext|>Você sabe alguma coisa sobre ética das virtudes<|endoftext|>
+  example_title: Ética das virtudes
+- text: <|startoftext|>O que posso fazer para alegrar minha namorada?<|endoftext|>
+  example_title: Conselho
+inference:
+  parameters:
+    repetition_penalty: 1.2
+    temperature: 0.2
+    top_k: 30
+    top_p: 0.3
+    max_length: 100
+    length_penalty: 0.3
+    early_stopping: True
 ---
+# Aira-Instruct-PT-1B7 (Portuguese)
+
+`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.
+
+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-Portuguese).
+
+## Details
+
+- **Size:** 559,012,864 total parameters
+- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/fine-tuning-instruct-aira)
+- **Language:** Portuguese
+- **Number of Epochs:** 2
+- **Batch size:** 6
+- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
+- **GPU:** 1 NVIDIA A100-SXM4-40GB
+
+| Epoch|Training Loss|Validation Loss|
+|---|---|---|
+| 1 |0.888486|0.744728|
+| 2 |0.749612|0.673719|
+
+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 AutoModelForCausalLM, AutoTokenizer
+import torch
+
+device = torch.device("cuda"  if torch.cuda.is_available() else  "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-Instruct-PT-1B7')
+aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-PT-1B7')
+
+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: 👤 Olá! Como você se chama?
+
+>>>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".
+>>>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.
+```
+
+## 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-PT-560M},
+  author = {Nicholas Kluge Corrêa and Carolina Del Pino},
+  title = {Aira},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+}
+
+```
+
+## License
+
+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.

config.json

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+{
+  "_name_or_path": "bigscience/bloom-1b7",
+  "apply_residual_connection_post_layernorm": false,
+  "architectures": [
+    "BloomForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "attention_softmax_in_fp32": true,
+  "bias_dropout_fusion": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_dropout": 0.0,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "masked_softmax_fusion": true,
+  "model_type": "bloom",
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "offset_alibi": 100,
+  "pad_token_id": 3,
+  "pretraining_tp": 2,
+  "seq_length": 4096,
+  "skip_bias_add": true,
+  "skip_bias_add_qkv": false,
+  "slow_but_exact": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "unk_token_id": 0,
+  "use_cache": true,
+  "vocab_size": 250683
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "transformers_version": "4.30.2"
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "bos_token": "<|startoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|pad|>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "padding_side": "left",
+  "tokenizer_class": "BloomTokenizer",
+  "unk_token": "<unk>"
+}

training_stats.parquet

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