Quantization made by Richard Erkhov.

Aira-2-355M - GGUF

Model creator: https://huggingface.co/nicholasKluge/
Original model: https://huggingface.co/nicholasKluge/Aira-2-355M/

Name	Quant method	Size
Aira-2-355M.Q2_K.gguf	Q2_K	0.17GB
Aira-2-355M.IQ3_XS.gguf	IQ3_XS	0.18GB
Aira-2-355M.IQ3_S.gguf	IQ3_S	0.19GB
Aira-2-355M.Q3_K_S.gguf	Q3_K_S	0.19GB
Aira-2-355M.IQ3_M.gguf	IQ3_M	0.2GB
Aira-2-355M.Q3_K.gguf	Q3_K	0.21GB
Aira-2-355M.Q3_K_M.gguf	Q3_K_M	0.21GB
Aira-2-355M.Q3_K_L.gguf	Q3_K_L	0.23GB
Aira-2-355M.IQ4_XS.gguf	IQ4_XS	0.22GB
Aira-2-355M.Q4_0.gguf	Q4_0	0.23GB
Aira-2-355M.IQ4_NL.gguf	IQ4_NL	0.23GB
Aira-2-355M.Q4_K_S.gguf	Q4_K_S	0.23GB
Aira-2-355M.Q4_K.gguf	Q4_K	0.25GB
Aira-2-355M.Q4_K_M.gguf	Q4_K_M	0.25GB
Aira-2-355M.Q4_1.gguf	Q4_1	0.25GB
Aira-2-355M.Q5_0.gguf	Q5_0	0.27GB
Aira-2-355M.Q5_K_S.gguf	Q5_K_S	0.27GB
Aira-2-355M.Q5_K.gguf	Q5_K	0.29GB
Aira-2-355M.Q5_K_M.gguf	Q5_K_M	0.29GB
Aira-2-355M.Q5_1.gguf	Q5_1	0.29GB
Aira-2-355M.Q6_K.gguf	Q6_K	0.32GB
Aira-2-355M.Q8_0.gguf	Q8_0	0.41GB

Original model description:

datasets: - nicholasKluge/instruct-aira-dataset language: - en metrics: - accuracy library_name: transformers tags: - alignment - instruction tuned - text generation - conversation - assistant pipeline_tag: text-generation widget: - text: "<|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>" example_title: Machine Learning - text: "<|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>" example_title: Ethics - text: "<|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>" example_title: Advise inference: parameters: repetition_penalty: 1.2 temperature: 0.2 top_k: 30 top_p: 0.3 max_new_tokens: 200 length_penalty: 0.3 early_stopping: true co2_eq_emissions: emissions: 290 source: CodeCarbon training_type: fine-tuning geographical_location: United States of America hardware_used: NVIDIA A100-SXM4-40GB license: apache-2.0

Aira-2-355M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-355M is an instruction-tuned model based on GPT-2. 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.

Details

Size: 354,825,216 parameters
Dataset: Instruct-Aira Dataset
Language: English
Number of Epochs: 3
Batch size: 16
Optimizer: torch.optim.AdamW (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
GPU: 1 NVIDIA A100-SXM4-40GB
Emissions: 0.29 KgCO2 (United States of America)
Total Energy Consumption: 0.83 kWh

This repository has the source code used to train this model.

Usage

Three special tokens are used to mark the user side of the interaction and the model's response:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-355M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-355M')

aira.eval()
aira.to(device)

question =  input("Enter your question: ")

inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token,
  add_special_tokens=False,
  return_tensors="pt").to(device)

responses = aira.generate(**inputs,	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:

>>>Question: 👤 What is the capital of Brazil?

>>>Response 1: 🤖 The capital of Brazil is Brasília.
>>>Response 2: 🤖 The capital of Brazil is Brasília.

Limitations

Hallucinations: This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.
Biases and Toxicity: This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
Repetition and Verbosity: The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.

Evaluation

Model	Average	ARC	TruthfulQA	ToxiGen
Aira-2-124M-DPO	40.68	24.66	42.61	54.79
Aira-2-124M	38.07	24.57	41.02	48.62
GPT-2	35.37	21.84	40.67	43.62
Aira-2-355M	39.68	27.56	38.53	53.19
GPT-2-medium	36.43	27.05	40.76	41.49
Aira-2-774M	42.26	28.75	41.33	56.70
GPT-2-large	35.16	25.94	38.71	40.85
Aira-2-1B5	42.22	28.92	41.16	56.60
GPT-2-xl	36.84	30.29	38.54	41.70

Evaluations were performed using the Language Model Evaluation Harness (by EleutherAI).

Cite as 🤗

@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://github.com/Nkluge-correa/Aira},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
}

@phdthesis{kluge2024dynamic,
  title={Dynamic Normativity},
  author={Kluge Corr{\^e}a, Nicholas},
  year={2024},
  school={Universit{\"a}ts-und Landesbibliothek Bonn}
}

License

Aira-2-355M is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.