Quantization made by Richard Erkhov.
mega-ar-126m-4k - bnb 8bits
- Model creator: https://huggingface.co/BEE-spoke-data/
- Original model: https://huggingface.co/BEE-spoke-data/mega-ar-126m-4k/
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 |
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