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README.md
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@@ -77,19 +77,23 @@ Quantized GGUF model files for [smol_llama-101M-GQA-python](https://huggingface.
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| [smol_llama-101m-gqa-python.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.fp16.gguf) | fp16 |
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| [smol_llama-101m-gqa-python.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q2_k.gguf) | q2_k |
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| [smol_llama-101m-gqa-python.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q3_k_m.gguf) | q3_k_m |
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| [smol_llama-101m-gqa-python.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q4_k_m.gguf) | q4_k_m |
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| [smol_llama-101m-gqa-python.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q5_k_m.gguf) | q5_k_m |
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| [smol_llama-101m-gqa-python.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q6_k.gguf) | q6_k |
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| [smol_llama-101m-gqa-python.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q8_0.gguf) | q8_0 |
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## Original Model Card:
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# smol_llama-101M-GQA: python
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> 400MB of buzz: pure Python programming nectar! 🍯
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This model is the general pre-trained checkpoint `BEE-spoke-data/smol_llama-101M-GQA` trained on a deduped version of `pypi` for +1 epoch. Play with the model in [this demo space](https://huggingface.co/spaces/BEE-spoke-data/beecoder-playground).
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [smol_llama-101m-gqa-python.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.fp16.gguf) | fp16 | 203.28 MB |
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| [smol_llama-101m-gqa-python.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q2_k.gguf) | q2_k | 50.93 MB |
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| [smol_llama-101m-gqa-python.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q3_k_m.gguf) | q3_k_m | 57.06 MB |
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| [smol_llama-101m-gqa-python.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q4_k_m.gguf) | q4_k_m | 65.41 MB |
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| [smol_llama-101m-gqa-python.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q5_k_m.gguf) | q5_k_m | 74.34 MB |
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| [smol_llama-101m-gqa-python.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q6_k.gguf) | q6_k | 83.83 MB |
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| [smol_llama-101m-gqa-python.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q8_0.gguf) | q8_0 | 108.35 MB |
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## Original Model Card:
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# smol_llama-101M-GQA: python
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<a href="https://colab.research.google.com/gist/pszemraj/91b5a267df95461b46922e6c0212e8f7/beecoder-basic-test-notebook.ipynb">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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</a>
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> 400MB of buzz: pure Python programming nectar! 🍯
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This model is the general pre-trained checkpoint `BEE-spoke-data/smol_llama-101M-GQA` trained on a deduped version of `pypi` for +1 epoch. Play with the model in [this demo space](https://huggingface.co/spaces/BEE-spoke-data/beecoder-playground).
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