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Copyright Nicholas Kluge Corrêa
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See the License for the specific language governing permissions and
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README.md
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@@ -1,83 +1,142 @@
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---
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license: apache-2.0
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datasets:
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-
- nicholasKluge/
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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-
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---
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-
# Aira-2-124M-DPO-checkpoint-200
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-
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## Hyperparameters
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-
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```yaml
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model_args:
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base_model: "nicholasKluge/Aira-2-124M"
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model_ref: "nicholasKluge/Aira-2-124M"
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cache_dir: null
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data_args:
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dataset_name: "nicholasKluge/reward-aira-dataset"
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dataset_split: "english"
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validation_split_percentage: null
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streaming: false
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max_prompt_length: 150
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max_length: 600
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sanity_check: false
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training_args:
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output_dir: "checkpoints"
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do_eval: false
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evaluation_strategy: "no"
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-
save_strategy: "steps"
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logging_strategy: "steps"
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logging_steps: 200
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max_steps: 2400
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save_steps: 200
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-
per_device_train_batch_size: 8
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-
per_device_eval_batch_size: 8
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gradient_accumulation_steps: 1
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gradient_checkpointing: false
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optim: "adamw_torch"
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learning_rate: 0.00005
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lr_scheduler_type: "cosine"
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warmup_steps: 100
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hub_token: null
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push_to_hub: false
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hub_model_id: null
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extra_args:
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project_name: "Aira-2"
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wandb_token: null
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beta: 0.9
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```
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-
##
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-
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-
| Key | Value |
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-
|-----------------------|---------------------------------|
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| loss | 0.2274 |
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-
| learning_rate | 4.976714865090827e-05 |
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| rewards/chosen | -33.849693298339844 |
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| rewards/rejected | -114.72045135498047 |
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| rewards/accuracies | 0.9768750071525574 |
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| rewards/margins | 80.87075805664062 |
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| logps/rejected | -404.8834228515625 |
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| logps/chosen | -383.7469482421875 |
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| logits/rejected | -67.6454086303711 |
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| logits/chosen | -30.543472290039062 |
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| epoch | 0.05 |
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-
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-
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## Eval
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-
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| Task |Version| Metric |Value | |Stderr|
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-
|-------------|------:|--------|-----:|---|-----:|
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-
|arc_challenge| 0|acc |0.2125|± |0.0120|
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| | |acc_norm|0.2466|± |0.0126|
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|toxigen | 0|acc |0.5479|± |0.0162|
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| | |acc_norm|0.4309|± |0.0162|
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|truthfulqa_mc| 1|mc1 |0.2546|± |0.0153|
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-
| | |mc2 |0.4261|± |0.0154|
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83 |
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|
1 |
---
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2 |
license: apache-2.0
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3 |
datasets:
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4 |
+
- nicholasKluge/instruct-aira-dataset
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5 |
language:
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6 |
- en
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7 |
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metrics:
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8 |
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- accuracy
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9 |
library_name: transformers
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|
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10 |
tags:
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11 |
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- alignment
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12 |
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- instruction tuned
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13 |
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- text generation
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14 |
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- conversation
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15 |
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- assistant
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pipeline_tag: text-generation
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17 |
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widget:
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- text: "<|startofinstruction|>Hello! What is your name?<|endofinstruction|>"
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19 |
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example_title: Greetings
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20 |
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- text: "<|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>"
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21 |
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example_title: Machine Learning
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- text: "<|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>"
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example_title: Ethics
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- text: "<|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>"
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example_title: Advise
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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_new_tokens: 200
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length_penalty: 0.3
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early_stopping: true
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co2_eq_emissions:
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emissions: 0.25
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source: CodeCarbon
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training_type: fine-tuning
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geographical_location: United States of America
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hardware_used: NVIDIA A100-SXM4-40GB
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---
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# Aira-2-124M
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`Aira-2` is the second version of the Aira instruction-tuned series. `Aira-2-124M` is an instruction-tuned model based on [GPT-2](https://huggingface.co/gpt2). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).
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Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).
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## Details
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- **Size:** 124,441,344 parameters
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- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
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- **Language:** English
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- **Number of Epochs:** 5
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- **Batch size:** 32
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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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- **Emissions:** 0.25 KgCO2 (Singapore)
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- **Total Energy Consumption:** 0.52 kWh
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This repository has the [source code](https://github.com/Nkluge-correa/Aira) used to train this model.
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## Usage
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Three special tokens are used to mark the user side of the interaction and the model's response:
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`<|startofinstruction|>`What is a language model?`<|endofinstruction|>`A language model is a probability distribution over a vocabulary.`<|endofcompletion|>`
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-124M')
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aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-124M')
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aira.eval()
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aira.to(device)
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question = input("Enter your question: ")
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inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token,
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add_special_tokens=False,
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return_tensors="pt").to(device)
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responses = aira.generate(**inputs, num_return_sequences=2)
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print(f"Question: 👤 {question}\n")
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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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The model will output something like:
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```markdown
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>>>Question: 👤 What is the capital of Brazil?
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>>>Response 1: 🤖 The capital of Brazil is Brasília.
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>>>Response 2: 🤖 The capital of Brazil is Brasília.
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```
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## Limitations
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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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🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes.
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## Evaluation
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| Model (GPT-2) | Average | [ARC](https://arxiv.org/abs/1803.05457) | [TruthfulQA](https://arxiv.org/abs/2109.07958) | [ToxiGen](https://arxiv.org/abs/2203.09509) | | |
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|-----------------------------------------------------------------|-----------|-----------------------------------------|------------------------------------------------|---------------------------------------------|---|---|
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| [Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-2-124M) | **38.07** | **24.57** | **41.02** | **48.62** | | |
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| GPT-2 | 35.37 | 21.84 | 40.67 | 43.62 | | |
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| [Aira-2-355M](https://huggingface.co/nicholasKluge/Aira-2-355M) | **39.68** | **27.56** | 38.53 | **53.19** | | |
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| GPT-2-medium | 36.43 | 27.05 | **40.76** | 41.49 | | |
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| [Aira-2-774M](https://huggingface.co/nicholasKluge/Aira-2-774M) | **42.26** | **28.75** | **41.33** | **56.70** | | |
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| GPT-2-large | 35.16 | 25.94 | 38.71 | 40.85 | | |
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| [Aira-2-1B5](https://huggingface.co/nicholasKluge/Aira-2-1B5) | **42.22** | 28.92 | **41.16** | **56.60** | | |
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| GPT-2-xl | 36.84 | **30.29** | 38.54 | 41.70 | | |
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* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)).
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## Cite as 🤗
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```latex
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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-2-124M},
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author = {Nicholas Kluge Corrêa},
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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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## License
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The `Aira-2-124M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
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