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--- |
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language: |
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- en |
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license: apache-2.0 |
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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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- mattymchen/refinedweb-3m |
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- BEE-spoke-data/knowledge-inoc-concat-v1 |
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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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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: 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 Destiny. |
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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: The Harry Potter series, written by J.K. Rowling, begins with the book titled |
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example_title: Harry Potter Series |
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- text: '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: 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: '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 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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model-index: |
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- name: smol_llama-220M-GQA |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 24.83 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 29.76 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 25.85 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 44.55 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 50.99 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 0.68 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA |
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name: Open LLM Leaderboard |
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--- |
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# smol_llama: 220M GQA |
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> model card WIP, more details to come |
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A small 220M param (total) decoder model. This is the first version of the model. |
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- 1024 hidden size, 10 layers |
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- GQA (32 heads, 8 key-value), context length 2048 |
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- train-from-scratch on one GPU :) |
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## Links |
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[Here](https://huggingface.co/collections/BEE-spoke-data/finetuned-smol-220m-65998b080ae723e79c830f83) are some fine-tunes we did, but there are many more possibilities out there! |
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- instruct |
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- openhermes - [link](https://huggingface.co/BEE-spoke-data/smol_llama-220M-openhermes) |
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- open-instruct - [link](https://huggingface.co/BEE-spoke-data/smol_llama-220M-open_instruct) |
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- code |
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- python (pypi) - [link](https://huggingface.co/BEE-spoke-data/beecoder-220M-python) |
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- zephyr DPO tune |
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- SFT - [link](https://huggingface.co/BEE-spoke-data/zephyr-220m-sft-full) |
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- full DPO - [link](https://huggingface.co/BEE-spoke-data/zephyr-220m-dpo-full) |
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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-220M-GQA) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |29.44| |
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|AI2 Reasoning Challenge (25-Shot)|24.83| |
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|HellaSwag (10-Shot) |29.76| |
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|MMLU (5-Shot) |25.85| |
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|TruthfulQA (0-shot) |44.55| |
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|Winogrande (5-shot) |50.99| |
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|GSM8k (5-shot) | 0.68| |
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