--- base_model: EleutherAI/pythia-31m tags: - generated_from_trainer metrics: - accuracy inference: parameters: max_new_tokens: 64 do_sample: true repetition_penalty: 1.1 no_repeat_ngram_size: 5 eta_cutoff: 0.001 widget: - text: My name is El Microondas the Wise and example_title: El Microondas - text: Kennesaw State University is a public example_title: Kennesaw State University - text: Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded example_title: Bungie - text: The Mona Lisa is a world-renowned painting created by example_title: Mona Lisa - text: >- The Harry Potter series, written by J.K. Rowling, begins with the book titled example_title: Harry Potter Series - text: >- Question: I have cities, but no houses. I have mountains, but no trees. I have water, but no fish. What am I? Answer: example_title: Riddle - text: The process of photosynthesis involves the conversion of example_title: Photosynthesis - text: >- Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot example_title: Story Continuation - text: >- Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and another train leaves Station B at 10:00 AM and travels at 80 mph, when will they meet if the distance between the stations is 300 miles? To determine example_title: Math Problem - text: >- In the context of computer programming, an algorithm is example_title: Algorithm Definition pipeline_tag: text-generation license: apache-2.0 language: - en datasets: - pszemraj/simpleRW-lite --- # BL-pythia-31m-simpleRW-lite-2048-scratch This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7136 - Accuracy: 0.2662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data ``` 2040 ***** eval metrics ***** 2041 epoch = 3.0 2042 eval_accuracy = 0.2668 2043 eval_loss = 4.7076 2044 eval_runtime = 0:00:21.04 2045 eval_samples = 500 2046 eval_samples_per_second = 23.759 2047 eval_steps_per_second = 11.88 2048 perplexity = 110.7897 ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 80085 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07 - lr_scheduler_type: inverse_sqrt - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 7.0159 | 0.13 | 100 | 7.1022 | 0.1180 | | 6.2257 | 0.27 | 200 | 6.3526 | 0.1508 | | 5.8611 | 0.4 | 300 | 5.9888 | 0.1735 | | 5.5514 | 0.54 | 400 | 5.7552 | 0.1855 | | 5.3824 | 0.67 | 500 | 5.5883 | 0.1948 | | 5.344 | 0.81 | 600 | 5.4697 | 0.2017 | | 5.1925 | 0.94 | 700 | 5.3717 | 0.2073 | | 5.0814 | 1.08 | 800 | 5.2932 | 0.2121 | | 5.0865 | 1.21 | 900 | 5.2280 | 0.2162 | | 4.9602 | 1.35 | 1000 | 5.1672 | 0.2207 | | 4.957 | 1.48 | 1100 | 5.1144 | 0.2247 | | 4.8489 | 1.62 | 1200 | 5.0617 | 0.2299 | | 4.79 | 1.75 | 1300 | 5.0122 | 0.2349 | | 4.8005 | 1.89 | 1400 | 4.9637 | 0.2400 | | 4.7409 | 2.02 | 1500 | 4.9216 | 0.2448 | | 4.6674 | 2.16 | 1600 | 4.8815 | 0.2488 | | 4.6729 | 2.29 | 1700 | 4.8475 | 0.2526 | | 4.7071 | 2.43 | 1800 | 4.8156 | 0.2555 | | 4.4937 | 2.56 | 1900 | 4.7841 | 0.2588 | | 4.5153 | 2.7 | 2000 | 4.7573 | 0.2615 | | 4.5512 | 2.83 | 2100 | 4.7345 | 0.2637 | | 4.5153 | 2.96 | 2200 | 4.7136 | 0.2662 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.2.0.dev20230915+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3