Card: link Runpod's serverless deploy guide
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README.md
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@@ -36,6 +36,8 @@ vllm serve openjev/openjev-FP8 --served-model-name qwen --port 8000 --enable-pre
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Then start the helper from the main repository (`helper/shim.py`) exactly as its card describes; the calibration settings are the same as for the 16-bit model. The conversion receipt (`FP8_CONVERSION_RECEIPT.json`) records the source checkpoint and the conversion tool.
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## Licence
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Weights: CC BY-NC 4.0 (research and non-commercial use), the same as the main repository. For a commercial licence, email [support@loopai.com](mailto:support@loopai.com). Helper and serving files: Apache 2.0. The licence texts and the base model's attribution are in `LICENSE`, `LICENSE-APACHE-2.0` and `NOTICE`.
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Then start the helper from the main repository (`helper/shim.py`) exactly as its card describes; the calibration settings are the same as for the 16-bit model. The conversion receipt (`FP8_CONVERSION_RECEIPT.json`) records the source checkpoint and the conversion tool.
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Serverless: Runpod has a step-by-step guide for serving this build on Runpod Serverless with one 80 GB H100: [How to deploy OpenJev on Runpod Serverless](https://www.runpod.io/blog/how-to-deploy-openjev-on-runpod-serverless).
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## Licence
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Weights: CC BY-NC 4.0 (research and non-commercial use), the same as the main repository. For a commercial licence, email [support@loopai.com](mailto:support@loopai.com). Helper and serving files: Apache 2.0. The licence texts and the base model's attribution are in `LICENSE`, `LICENSE-APACHE-2.0` and `NOTICE`.
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