Quantization made by Richard Erkhov.
Aira-2-1B5 - GGUF
- Model creator: https://huggingface.co/nicholasKluge/
- Original model: https://huggingface.co/nicholasKluge/Aira-2-1B5/
| Name | Quant method | Size |
|---|---|---|
| Aira-2-1B5.Q2_K.gguf | Q2_K | 0.84GB |
| Aira-2-1B5.IQ3_XS.gguf | IQ3_XS | 0.84GB |
| Aira-2-1B5.IQ3_S.gguf | IQ3_S | 0.84GB |
| Aira-2-1B5.Q3_K_S.gguf | Q3_K_S | 0.84GB |
| Aira-2-1B5.IQ3_M.gguf | IQ3_M | 0.91GB |
| Aira-2-1B5.Q3_K.gguf | Q3_K | 0.97GB |
| Aira-2-1B5.Q3_K_M.gguf | Q3_K_M | 0.97GB |
| Aira-2-1B5.Q3_K_L.gguf | Q3_K_L | 1.03GB |
| Aira-2-1B5.IQ4_XS.gguf | IQ4_XS | 0.9GB |
| Aira-2-1B5.Q4_0.gguf | Q4_0 | 0.91GB |
| Aira-2-1B5.IQ4_NL.gguf | IQ4_NL | 0.91GB |
| Aira-2-1B5.Q4_K_S.gguf | Q4_K_S | 1.04GB |
| Aira-2-1B5.Q4_K.gguf | Q4_K | 1.11GB |
| Aira-2-1B5.Q4_K_M.gguf | Q4_K_M | 1.11GB |
| Aira-2-1B5.Q4_1.gguf | Q4_1 | 1.0GB |
| Aira-2-1B5.Q5_0.gguf | Q5_0 | 1.09GB |
| Aira-2-1B5.Q5_K_S.gguf | Q5_K_S | 1.15GB |
| Aira-2-1B5.Q5_K.gguf | Q5_K | 1.29GB |
| Aira-2-1B5.Q5_K_M.gguf | Q5_K_M | 1.29GB |
| Aira-2-1B5.Q5_1.gguf | Q5_1 | 1.18GB |
| Aira-2-1B5.Q6_K.gguf | Q6_K | 1.52GB |
| Aira-2-1B5.Q8_0.gguf | Q8_0 | 1.63GB |
Original model description:
license: apache-2.0 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: 1690 source: CodeCarbon training_type: fine-tuning geographical_location: United States of America hardware_used: NVIDIA A100-SXM4-40GB
Aira-2-1B5
Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-1B5 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: 1,557,614,400 parameters
- Dataset: 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: 1.69 KgCO2 (Singapore)
- Total Energy Consumption: 3.47 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:
<|startofinstruction|>What is a language model?<|endofinstruction|>A language model is a probability distribution over a vocabulary.<|endofcompletion|>
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-1B5')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-1B5')
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-2-1B5 is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.