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---
license: apache-2.0
base_model: pszemraj/mega-ar-small-4096-NC-simplewiki-v1
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
datasets:
- JeanKaddour/minipile
- pszemraj/simple_wikipedia_LM
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mega-ar-small-4096-NC-minipile-v1
65M parameter MEGA autoregressive model initialized from scratch and trained on:
1. `pszemraj/simple_wikipedia_LM`
2. `JeanKaddour/minipile`
It achieves the following results on the evaluation set:
- Loss: 3.7502
- Accuracy: 0.3650
## eval
initial 'get the feet wet':
`hf-causal-experimental (pretrained=pszemraj/mega-ar-small-4096-sw_minipile,revision=main,trust_remote_code=True,dtype='float'), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16`
| Task |Version| Metric | Value | | Stderr |
|--------------|------:|--------|--------:|---|-------:|
|arc_easy | 0|acc | 0.3173|± | 0.0096|
| | |acc_norm| 0.3022|± | 0.0094|
|boolq | 1|acc | 0.4107|± | 0.0086|
|lambada_openai| 0|ppl |6843.1824|± |295.0792|
| | |acc | 0.0155|± | 0.0017|
|openbookqa | 0|acc | 0.1220|± | 0.0147|
| | |acc_norm| 0.2480|± | 0.0193|
|piqa | 0|acc | 0.5609|± | 0.0116|
| | |acc_norm| 0.5566|± | 0.0116|
|winogrande | 0|acc | 0.5059|± | 0.0141|
still some ways to go.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 80085
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0
### Framework versions
- Transformers 4.33.1
- Pytorch 2.2.0.dev20230907+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3 |