Original model description:

license: apache-2.0 datasets:

JeanKaddour/minipile
BEE-spoke-data/wikipedia-20230901.en-deduped
BEE-spoke-data/knowledge-inoc-concat-v1 language:
en inference: parameters: max_new_tokens: 64 do_sample: true temperature: 0.8 repetition_penalty: 1.05 no_repeat_ngram_size: 4 epsilon_cutoff: 0.0006 renormalize_logits: true 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

BEE-spoke-data/mega-ar-126m-4k

This may not be the best language model, but it is a language model! It's interesting for several reasons, not the least of which is that it's not technically a transformer.

Details:

768 hidden size, 12 layers
no MEGA chunking, 4096 context length
EMA dimension 16, shared dimension 192
tokenizer: GPT NeoX
train-from-scratch

For more info on MEGA (& what some of the params above mean), check out the model docs or the original paper

Usage

Usage is the same as any other small textgen model.

Given the model's small size and architecture, it's probably best to leverage its longer context by adding input context to "see more" rather than "generate more".

evals

Initial data:

hf-causal-experimental (pretrained=BEE-spoke-data/mega-ar-126m-4k,revision=main,trust_remote_code=True,dtype='float'), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4

Task	Version	Metric	Value		Stderr
arc_easy	0	acc	0.4415	±	0.0102
		acc_norm	0.3969	±	0.0100
boolq	1	acc	0.5749	±	0.0086
lambada_openai	0	ppl	94.9912	±	3.9682
		acc	0.2408	±	0.0060
openbookqa	0	acc	0.1660	±	0.0167
		acc_norm	0.2780	±	0.0201
piqa	0	acc	0.5974	±	0.0114
		acc_norm	0.5914	±	0.0115
winogrande	0	acc	0.4830	±	0.0140