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Parallel_7B

Text Generation
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PyTorch
10 languages
llama
text-generation-inference
Inference Endpoints
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@@ -50,9 +50,9 @@ Our dataset and models are all available at Huggingface.
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  |----|---------------------------------------------------------------|---------------------------------------------------------------------------|
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  | 7B-LLaMA 2 | 🐙 [MathOctopus-Parallel-7B](https://huggingface.co/Mathoctopus/Parallel_7B) | 🐙 [MathOctopus-Cross-7B](https://huggingface.co/Mathoctopus/Cross_7B) |
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  || 🐙[MathOctopus-Parallel-xRFT-7B](https://huggingface.co/Mathoctopus/Parallel_xRFT_7B)|🐙[MathOctopus-Cross-xRFT-7B](https://huggingface.co/Mathoctopus/Cross_xRFT_7B)|
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- | 13B-LLaMA 2 | 🐙 [MathOctopus-Parallel-13B] | 🐙 [MathOctopus-Cross-13B] |
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- || 🐙[MathOctopus-Parallel-xRFT-13B](https://huggingface.co/Mathoctopus/Parallel_xRFT_13B/tree/main)|🐙[MathOctopus-Cross-xRFT-13B]|
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- | 33B-LLaMA 1 | 🐙 [MathOctopus-Parallel-33B] | 🐙 [MathOctopus-Cross-33B] |
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  | 70B-LLaMA 2 | Coming soon! | Coming Soon! |
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  *-Parallel refers to our model trained with the parallel-training strategy.
 
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  |----|---------------------------------------------------------------|---------------------------------------------------------------------------|
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  | 7B-LLaMA 2 | 🐙 [MathOctopus-Parallel-7B](https://huggingface.co/Mathoctopus/Parallel_7B) | 🐙 [MathOctopus-Cross-7B](https://huggingface.co/Mathoctopus/Cross_7B) |
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  || 🐙[MathOctopus-Parallel-xRFT-7B](https://huggingface.co/Mathoctopus/Parallel_xRFT_7B)|🐙[MathOctopus-Cross-xRFT-7B](https://huggingface.co/Mathoctopus/Cross_xRFT_7B)|
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+ | 13B-LLaMA 2 | 🐙 [MathOctopus-Parallel-13B](https://huggingface.co/Mathoctopus/Parallel_13B) | 🐙 [MathOctopus-Cross-13B](https://huggingface.co/Mathoctopus/Cross_13B) |
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+ || 🐙[MathOctopus-Parallel-xRFT-13B](https://huggingface.co/Mathoctopus/Parallel_xRFT_13B)|🐙[MathOctopus-Cross-xRFT-13B]|
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+ | 33B-LLaMA 1 | 🐙 [MathOctopus-Parallel-33B](https://huggingface.co/Mathoctopus/Parallel_33B) | 🐙 [MathOctopus-Cross-33B] |
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  | 70B-LLaMA 2 | Coming soon! | Coming Soon! |
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  *-Parallel refers to our model trained with the parallel-training strategy.