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--- |
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license: apache-2.0 |
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thumbnail: https://i.ibb.co/TvyMrRc/rsz-smol-llama-banner.png |
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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: 1.15 |
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no_repeat_ngram_size: 4 |
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eta_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 for |
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developing the award winning Halo series of video games. They also made |
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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, oranges, |
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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, and |
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another train leaves Station B at 10:00 AM and travels at 80 mph, when will |
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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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tags: |
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- smol_llama |
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- llama2 |
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datasets: |
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- JeanKaddour/minipile |
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- pszemraj/simple_wikipedia_LM |
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- BEE-spoke-data/wikipedia-20230901.en-deduped |
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- mattymchen/refinedweb-3m |
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--- |
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# smol_llama-81M-tied |
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<img src="smol-llama-banner.png" alt="banner" style="max-width:80%; height:auto;"> |
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A small 81M param (total) decoder model, enabled through tying the input/output embeddings. This is the first version of the model. |
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- 768 hidden size, 6 layers |
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- standard multi-head attention (24 heads), context length 1024 |
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- input/output embeddings **are tied** |
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- train-from-scratch |
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## Notes |
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**This checkpoint** is the 'raw' pre-trained model and has not been tuned to a more specific task. **It should be fine-tuned** before use in most cases. |
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- slightly larger 101M param GQA pretrained version: [here](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) |
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- For the chat version of this model, please [see here](https://youtu.be/dQw4w9WgXcQ?si=3ePIqrY1dw94KMu4) |
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--- |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-81M-tied) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 24.52 | |
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| ARC (25-shot) | 22.18 | |
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| HellaSwag (10-shot) | 29.33 | |
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| MMLU (5-shot) | 24.06 | |
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| TruthfulQA (0-shot) | 43.97 | |
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| Winogrande (5-shot) | 49.25 | |
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| GSM8K (5-shot) | 0.23 | |
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| DROP (3-shot) | 2.64 | |
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