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RichardErkhov/xzyao_-_openllama-3b-chat-gguf
xzyao_-_openllama-3b-chat-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
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0% of all-time downloads
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.gguf52.6 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
openllama-3b-chat - GGUF
| Name | Quant method | Size |
|---|---|---|
| openllama-3b-chat.Q2_K.gguf | Q2_K | 1.84GB |
| openllama-3b-chat.IQ3_XS.gguf | IQ3_XS | 1.84GB |
| openllama-3b-chat.IQ3_S.gguf | IQ3_S | 1.84GB |
| openllama-3b-chat.Q3_K_S.gguf | Q3_K_S | 1.84GB |
| openllama-3b-chat.IQ3_M.gguf | IQ3_M | 1.92GB |
| openllama-3b-chat.Q3_K.gguf | Q3_K | 1.99GB |
| openllama-3b-chat.Q3_K_M.gguf | Q3_K_M | 1.99GB |
| openllama-3b-chat.Q3_K_L.gguf | Q3_K_L | 2.06GB |
| openllama-3b-chat.IQ4_XS.gguf | IQ4_XS | 1.86GB |
| openllama-3b-chat.Q4_0.gguf | Q4_0 | 1.84GB |
| openllama-3b-chat.IQ4_NL.gguf | IQ4_NL | 1.86GB |
| openllama-3b-chat.Q4_K_S.gguf | Q4_K_S | 2.24GB |
| openllama-3b-chat.Q4_K.gguf | Q4_K | 2.4GB |
| openllama-3b-chat.Q4_K_M.gguf | Q4_K_M | 2.4GB |
| openllama-3b-chat.Q4_1.gguf | Q4_1 | 2.04GB |
| openllama-3b-chat.Q5_0.gguf | Q5_0 | 2.23GB |
| openllama-3b-chat.Q5_K_S.gguf | Q5_K_S | 2.42GB |
| openllama-3b-chat.Q5_K.gguf | Q5_K | 2.57GB |
| openllama-3b-chat.Q5_K_M.gguf | Q5_K_M | 2.57GB |
| openllama-3b-chat.Q5_1.gguf | Q5_1 | 2.42GB |
| openllama-3b-chat.Q6_K.gguf | Q6_K | 3.39GB |
| openllama-3b-chat.Q8_0.gguf | Q8_0 | 3.39GB |
license: apache-2.0 language:
<human>: Who is Alan Turing?<|endoftext|><assistant>:
If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
@software{Yao_FMEngine_Library_for_2023,
author = {Yao, Xiaozhe},
doi = {10.5281/zenodo.8314779},
month = sep,
title = {{FMEngine: Library for Training Foundation Models}},
url = {https://github.com/eth-easl/fmengine},
version = {0.0.1},
year = {2023}
}
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
As with all language models, openllama-3b-chat may generate incorrect or biased content. It's important to keep this in mind when using the model.