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README.md
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@@ -31,6 +31,7 @@ Read more about how the model is trained and evaluted in our [technical report](
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python -m sglang.launch_server --model-path RyanLi0802/Biomni-R0-Preview --port 30000 --host 0.0.0.0 --mem-fraction-static 0.8 --tp 2 --trust-remote-code --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}, "max_position_embeddings": 131072}'
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```
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This would require two GPUs with 80G VRAM. Alternatively, you may serve with 4 GPUs with 40G VRAM via `--tp 4`.
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Note, `rope_scaling` might degrade performance on tasks with shorter trajectories. Please tune the rope scaling factor according to your usage.
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To run inference with the Biomni-E1 environment, please follow the instructions in our [official repo](https://github.com/snap-stanford/biomni).
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python -m sglang.launch_server --model-path RyanLi0802/Biomni-R0-Preview --port 30000 --host 0.0.0.0 --mem-fraction-static 0.8 --tp 2 --trust-remote-code --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}, "max_position_embeddings": 131072}'
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```
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This would require two GPUs with 80G VRAM. Alternatively, you may serve with 4 GPUs with 40G VRAM via `--tp 4`.
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Note, `rope_scaling` might degrade performance on tasks with shorter trajectories. Please tune the rope scaling factor according to your usage.
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To run inference with the Biomni-E1 environment, please follow the instructions in our [official repo](https://github.com/snap-stanford/biomni).
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