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README.md

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+---
+base_model: nicholasKluge/Aira-2-1B1
+co2_eq_emissions:
+  emissions: 1.78
+  geographical_location: United States of America
+  hardware_used: NVIDIA A100-SXM4-40GB
+  source: CodeCarbon
+  training_type: fine-tuning
+datasets:
+- nicholasKluge/instruct-aira-dataset
+inference: false
+language:
+- en
+library_name: transformers
+license: apache-2.0
+metrics:
+- accuracy
+model_creator: nicholasKluge
+model_name: Aira-2-1B1
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- alignment
+- instruction tuned
+- text generation
+- conversation
+- assistant
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+widget:
+- example_title: Greetings
+  text: <|startofinstruction|>How should I call you?<|endofinstruction|>
+- example_title: Machine Learning
+  text: <|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>
+- example_title: Ethics
+  text: <|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>
+- example_title: Advise
+  text: <|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>
+---
+# nicholasKluge/Aira-2-1B1-GGUF
+
+Quantized GGUF model files for [Aira-2-1B1](https://huggingface.co/nicholasKluge/Aira-2-1B1) from [nicholasKluge](https://huggingface.co/nicholasKluge)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [aira-2-1b1.fp16.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.fp16.gguf) | fp16 | 2.20 GB  |
+| [aira-2-1b1.q2_k.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.q2_k.gguf) | q2_k | 482.15 MB  |
+| [aira-2-1b1.q3_k_m.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.q3_k_m.gguf) | q3_k_m | 549.86 MB  |
+| [aira-2-1b1.q4_k_m.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.q4_k_m.gguf) | q4_k_m | 667.83 MB  |
+| [aira-2-1b1.q5_k_m.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.q5_k_m.gguf) | q5_k_m | 782.06 MB  |
+| [aira-2-1b1.q6_k.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.q6_k.gguf) | q6_k | 903.43 MB  |
+| [aira-2-1b1.q8_0.gguf](https://huggingface.co/afrideva/Aira-2-1B1-GGUF/resolve/main/aira-2-1b1.q8_0.gguf) | q8_0 | 1.17 GB  |
+
+
+
+## Original Model Card:
+# Aira-2-1B1
+
+`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).
+
+Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).
+
+## Details
+
+- **Size:** 1,261,545,472 parameters
+- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
+- **Language:** English
+- **Number of Epochs:** 3
+- **Batch size:** 4
+- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
+- **GPU:** 1 NVIDIA A100-SXM4-40GB
+- **Emissions:** 1.78 KgCO2 (Singapore)
+- **Total Energy Consumption:** 3.64 kWh
+
+This repository has the [source code](https://github.com/Nkluge-correa/Aira) used to train this model.
+
+## Usage
+
+Three special tokens are used to mark the user side of the interaction and the model's response:
+
+`<|startofinstruction|>`What is a language model?`<|endofinstruction|>`A language model is a probability distribution over a vocabulary.`<|endofcompletion|>`
+
+```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-2-1B1')
+aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-1B1')
+
+aira.eval()
+aira.to(device)
+
+question =  input("Enter your question: ")
+
+inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_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=500,
+	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: 👤 What is the capital of Brazil?
+
+>>>Response 1: 🤖 The capital of Brazil is Brasília.
+>>>Response 2: 🤖 The capital of Brazil is Brasília.
+```
+
+## 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.
+
+## Evaluation
+
+| Model (TinyLlama)                                             | Average   | [ARC](https://arxiv.org/abs/1803.05457) | [TruthfulQA](https://arxiv.org/abs/2109.07958) | [ToxiGen](https://arxiv.org/abs/2203.09509) |
+|---------------------------------------------------------------|-----------|-----------------------------------------|------------------------------------------------|---------------------------------------------|
+| [Aira-2-1B1](https://huggingface.co/nicholasKluge/Aira-2-1B1) | **42.55** | 25.26                                   | **50.81**                                      | **51.59**                                   |
+| TinyLlama-1.1B-intermediate-step-480k-1T                      | 37.52     | **30.89**                               | 39.55                                          | 42.13                                       |
+
+
+* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)).
+
+## Cite as 🤗
+
+```latex
+
+@misc{nicholas22aira,
+  doi = {10.5281/zenodo.6989727},
+  url = {https://huggingface.co/nicholasKluge/Aira-2-1B1},
+  author = {Nicholas Kluge Corrêa},
+  title = {Aira},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+}
+
+```
+
+## License
+
+The `Aira-2-1B1` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-2-1B1)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 25.19   |
+| ARC (25-shot)         | 23.21          |
+| HellaSwag (10-shot)   | 26.97    |
+| MMLU (5-shot)         | 24.86         |
+| TruthfulQA (0-shot)   | 50.63   |
+| Winogrande (5-shot)   | 50.28   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 0.39         |