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RichardErkhov/Nondzu_-_zephyr-7b-beta-pl-gguf
Nondzu_-_zephyr-7b-beta-pl-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
54
4% of all-time downloads
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.gguf95.2 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
zephyr-7b-beta-pl - GGUF
| Name | Quant method | Size |
|---|---|---|
| zephyr-7b-beta-pl.Q2_K.gguf | Q2_K | 2.53GB |
| zephyr-7b-beta-pl.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| zephyr-7b-beta-pl.IQ3_S.gguf | IQ3_S | 2.96GB |
| zephyr-7b-beta-pl.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| zephyr-7b-beta-pl.IQ3_M.gguf | IQ3_M | 3.06GB |
| zephyr-7b-beta-pl.Q3_K.gguf | Q3_K | 3.28GB |
| zephyr-7b-beta-pl.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| zephyr-7b-beta-pl.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| zephyr-7b-beta-pl.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| zephyr-7b-beta-pl.Q4_0.gguf | Q4_0 | 3.83GB |
| zephyr-7b-beta-pl.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| zephyr-7b-beta-pl.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| zephyr-7b-beta-pl.Q4_K.gguf | Q4_K | 4.07GB |
| zephyr-7b-beta-pl.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| zephyr-7b-beta-pl.Q4_1.gguf | Q4_1 | 4.24GB |
| zephyr-7b-beta-pl.Q5_0.gguf | Q5_0 | 4.65GB |
| zephyr-7b-beta-pl.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| zephyr-7b-beta-pl.Q5_K.gguf | Q5_K | 4.78GB |
| zephyr-7b-beta-pl.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| zephyr-7b-beta-pl.Q5_1.gguf | Q5_1 | 5.07GB |
| zephyr-7b-beta-pl.Q6_K.gguf | Q6_K | 5.53GB |
| zephyr-7b-beta-pl.Q8_0.gguf | Q8_0 | 7.17GB |
license: mit language:
The model is a result of advanced fine-tuning methods applied to a base model, focusing on enhancing its capabilities for specific Polish language datasets. It incorporates cutting-edge techniques and is built upon the Zephyr Beta model framework.
I trained the model using 3xRTX 3090 for 163 hours.

As this is an experimental model, users should be aware of the following:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:

User feedback is crucial during this testing phase. We encourage users to provide feedback on model performance, issues encountered, and any suggestions for improvements. Contributions in terms of shared test results, datasets, or code improvements are also welcome.
Disclaimer: This experimental model is provided 'as is', without warranty of any kind. Users should use the model at their own risk. The creators or maintainers of the model are not responsible for any consequences arising from its use.
