Update README.md

README.md

@@ -43,7 +43,7 @@ inference:
     top_p: 0.5
     max_new_tokens: 200
 co2_eq_emissions:
-  emissions: 15
+  emissions: 5.6
   source: CodeCarbon
   training_type: pre-training
   geographical_location: Germany
@@ -75,8 +75,8 @@ Teeny-tiny-llama has been trained by leveraging scaling laws to determine the op
 - **Optimizer:** `torch.optim.AdamW` (warmup_ratio = 0.01, learning_rate = 6e-4, epsilon = 1e-8)
 - **GPU:** 1 NVIDIA A100-SXM4-40GB
 - **Training time**: ~ 36 hours
-- **Emissions:** 15 KgCO2 (Germany)
-- **Total Energy Consumption:** 42 kWh
+- **Emissions:** 5.6 KgCO2 (Germany)
+- **Total Energy Consumption:** 15.5 kWh
 
 This repository has the [source code](https://github.com/Nkluge-correa/Aira) used to train this model.
 
@@ -138,18 +138,20 @@ model = AutoModelForCausalLM.from_pretrained("nicholasKluge/Teeny-tiny-llama-162
 
 ## Evaluations
 
+| Steps   | Evaluation Loss | Perplexity | Total Energy Consumption | Emissions  |
+|---------|-----------------|------------|--------------------------|------------|
+| 100.000 | 3.19            | 24.52      | 3.75 kWh                 | 1.28 CO2eq |
+| 200.000 | 3.02            | 20.58      | 7.51 kWh                 | 2.56 CO2eq |
+| 300.000 | 2.83            | 16.98      | 11.25 kWh                | 3.84 CO2eq |
+| 400.000 | 2.79            | 16.41      | 14.52 kWh                | 5.11 CO2eq |
+
+## Benchmarks
+
 | Models                                                                              | Average | [ARC](https://arxiv.org/abs/1803.05457) | [Hellaswag](https://arxiv.org/abs/1905.07830) | [MMLU](https://arxiv.org/abs/2009.03300) | [TruthfulQA](https://arxiv.org/abs/2109.07958) |
 |-------------------------------------------------------------------------------------|---------|-----------------------------------------|-----------------------------------------------|------------------------------------------|------------------------------------------------|
 | [Gpt2-portuguese-small](https://huggingface.co/pierreguillou/gpt2-small-portuguese) | 30.22   | 22.48                        | 29.62                              | 27.36                          | 41.44                            |
 
-* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)). Thanks to [Laiviet](https://github.com/laiviet/lm-evaluation-harness) for translating some of the tasks in the LM-Evaluation-Harness.
-
-| Steps   | Evaluation Loss | Perplexity | Total Energy Consumption |
-|---------|-----------------|------------|--------------------------|
-| 100.000 | 3.19            | 24.52      | 3.75 kWh                 |
-| 200.000 | 3.02            | 20.58      | 7.51 kWh                 |
-| 300.000 | 2.83            | 16.98      | 11.25 kWh                |
-| 400.000 | 2.79            | 16.41      | 30.20 kWh                |
+* Evaluations on benchmarks were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)). Thanks to [Laiviet](https://github.com/laiviet/lm-evaluation-harness) for translating some of the tasks in the LM-Evaluation-Harness.
 
 ## Cite as 🤗