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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 a few reasons, not in the least of which is that it's technically not 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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Use it as you would any small text generation model. Given the small size and architecture of the model, it's probably best to take advantage of the longer context length by providing the model with additional text/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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--- |