nsfwthrowitaway69
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Venus-103b-v1.1

Text Generation

Not-For-All-Audiences

text-generation-inference

Inference Endpoints

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nsfwthrowitaway69 commited on Dec 6, 2023

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 ## Model Details
-- A result of interleaving layers of [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B), [migtissera/SynthIA-70B-v1.5](https://huggingface.co/migtissera/SynthIA-70B-v1.5), and [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) using [mergekit](https://github.com/cg123/mergekit).
 - The resulting model has 120 layers and 103 billion parameters.
 - See mergekit-config.yml for details on the merge method used.
 - See the `exl2-*` branches for exllama2 quantizations. The 5.65 bpw quant should fit in 80GB VRAM, and the 3.35 bpw quant should fit in 48GB VRAM.

 ## Model Details
+- A result of interleaving layers of [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B), [migtissera/SynthIA-70B-v1.2b](https://huggingface.co/migtissera/SynthIA-70B-v1.2b), and [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) using [mergekit](https://github.com/cg123/mergekit).
 - The resulting model has 120 layers and 103 billion parameters.
 - See mergekit-config.yml for details on the merge method used.
 - See the `exl2-*` branches for exllama2 quantizations. The 5.65 bpw quant should fit in 80GB VRAM, and the 3.35 bpw quant should fit in 48GB VRAM.