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--- |
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license: apache-2.0 |
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datasets: |
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- JeanKaddour/minipile |
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- BEE-spoke-data/wikipedia-20230901.en-deduped |
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- BEE-spoke-data/knowledge-inoc-concat-v1 |
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language: |
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- en |
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inference: |
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parameters: |
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max_new_tokens: 64 |
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do_sample: true |
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temperature: 0.8 |
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repetition_penalty: 1.05 |
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no_repeat_ngram_size: 4 |
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epsilon_cutoff: 0.0006 |
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renormalize_logits: true |
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widget: |
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- text: My name is El Microondas the Wise, and |
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example_title: El Microondas |
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- text: Kennesaw State University is a public |
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example_title: Kennesaw State University |
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- text: >- |
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Bungie Studios is an American video game developer. They are most famous |
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for developing the award winning Halo series of video games. They also |
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made Destiny. The studio was founded |
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example_title: Bungie |
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- text: The Mona Lisa is a world-renowned painting created by |
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example_title: Mona Lisa |
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- text: >- |
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The Harry Potter series, written by J.K. Rowling, begins with the book |
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titled |
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example_title: Harry Potter Series |
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- text: >- |
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Question: I have cities, but no houses. I have mountains, but no trees. I |
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have water, but no fish. What am I? |
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Answer: |
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example_title: Riddle |
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- text: The process of photosynthesis involves the conversion of |
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example_title: Photosynthesis |
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- text: >- |
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Jane went to the store to buy some groceries. She picked up apples, |
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oranges, and a loaf of bread. When she got home, she realized she forgot |
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example_title: Story Continuation |
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- text: >- |
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Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, |
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and another train leaves Station B at 10:00 AM and travels at 80 mph, when |
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will they meet if the distance between the stations is 300 miles? |
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To determine |
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example_title: Math Problem |
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- text: In the context of computer programming, an algorithm is |
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example_title: Algorithm Definition |
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pipeline_tag: text-generation |
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--- |
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# BEE-spoke-data/mega-ar-126m-4k |
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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. |
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Details: |
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- 768 hidden size, 12 layers |
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- no MEGA chunking, 4096 context length |
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- EMA dimension 16, shared dimension 192 |
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- tokenizer: GPT NeoX |
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- train-from-scratch |
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For more info on MEGA (_& what some of the params above mean_), check out the [model docs](https://huggingface.co/docs/transformers/main/en/model_doc/mega#mega) or the [original paper](https://arxiv.org/abs/2209.10655) |
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## Usage |
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Usage is the same as any other small textgen model. |
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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". |
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## evals |
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Initial data: |
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`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` |
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| Task |Version| Metric | Value | |Stderr| |
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|--------------|------:|--------|------:|---|-----:| |
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|arc_easy | 0|acc | 0.4415|± |0.0102| |
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| | |acc_norm| 0.3969|± |0.0100| |
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|boolq | 1|acc | 0.5749|± |0.0086| |
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|lambada_openai| 0|ppl |94.9912|± |3.9682| |
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| | |acc | 0.2408|± |0.0060| |
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|openbookqa | 0|acc | 0.1660|± |0.0167| |
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| | |acc_norm| 0.2780|± |0.0201| |
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|piqa | 0|acc | 0.5974|± |0.0114| |
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| | |acc_norm| 0.5914|± |0.0115| |
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|winogrande | 0|acc | 0.4830|± |0.0140| |
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--- |
