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--- |
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base_model: BEE-spoke-data/smol_llama-101M-GQA |
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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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inference: false |
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language: |
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- en |
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license: apache-2.0 |
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model_creator: BEE-spoke-data |
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model_name: smol_llama-101M-GQA |
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pipeline_tag: text-generation |
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quantized_by: afrideva |
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tags: |
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- smol_llama |
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- llama2 |
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- gguf |
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- ggml |
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- quantized |
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- q2_k |
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- q3_k_m |
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- q4_k_m |
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- q5_k_m |
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- q6_k |
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- q8_0 |
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thumbnail: https://i.ibb.co/TvyMrRc/rsz-smol-llama-banner.png |
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widget: |
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- example_title: El Microondas |
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text: My name is El Microondas the Wise and |
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- example_title: Kennesaw State University |
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text: Kennesaw State University is a public |
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- example_title: Bungie |
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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: Mona Lisa |
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text: The Mona Lisa is a world-renowned painting created by |
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- example_title: Harry Potter Series |
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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: Riddle |
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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: Photosynthesis |
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text: The process of photosynthesis involves the conversion of |
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- example_title: Story Continuation |
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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: Math Problem |
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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: Algorithm Definition |
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text: In the context of computer programming, an algorithm is |
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--- |
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# BEE-spoke-data/smol_llama-101M-GQA-GGUF |
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Quantized GGUF model files for [smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data) |
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| Name | Quant method | Size | |
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| ---- | ---- | ---- | |
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| [smol_llama-101m-gqa.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.fp16.gguf) | fp16 | 203.28 MB | |
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| [smol_llama-101m-gqa.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q2_k.gguf) | q2_k | 50.93 MB | |
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| [smol_llama-101m-gqa.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q3_k_m.gguf) | q3_k_m | 57.06 MB | |
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| [smol_llama-101m-gqa.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q4_k_m.gguf) | q4_k_m | 65.40 MB | |
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| [smol_llama-101m-gqa.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q5_k_m.gguf) | q5_k_m | 74.34 MB | |
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| [smol_llama-101m-gqa.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q6_k.gguf) | q6_k | 83.83 MB | |
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| [smol_llama-101m-gqa.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q8_0.gguf) | q8_0 | 108.35 MB | |
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## Original Model Card: |
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# smol_llama-101M-GQA |
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<img src="smol-llama-banner.png" alt="banner" style="max-width:95%; height:auto;"> |
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A small 101M param (total) decoder model. This is the first version of the model. |
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- 768 hidden size, 6 layers |
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- GQA (24 heads, 8 key-value), context length 1024 |
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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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### Checkpoints & Links |
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- _smol_-er 81M parameter checkpoint with in/out embeddings tied: [here](https://huggingface.co/BEE-spoke-data/smol_llama-81M-tied) |
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- Fine-tuned on `pypi` to generate Python code - [link](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python) |
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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-101M-GQA) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 25.32 | |
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| ARC (25-shot) | 23.55 | |
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| HellaSwag (10-shot) | 28.77 | |
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| MMLU (5-shot) | 24.24 | |
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| TruthfulQA (0-shot) | 45.76 | |
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| Winogrande (5-shot) | 50.67 | |
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| GSM8K (5-shot) | 0.83 | |
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| DROP (3-shot) | 3.39 | |
