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Update README.md

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@@ -31,6 +31,8 @@ MolmoAct is trained on a subset of OXE and MolmoAct Dataset, a dataset with 10k
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  This checkpoint is a **preview** of the MolmoAct release. All artifacts used in creating MolmoAct (data, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
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  Quick links:
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  - πŸ“‚ [All Models](https://huggingface.co/collections/allenai/molmoact-689697591a3936fba38174d7)
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  - πŸ“‚ [All Data](https://huggingface.co/collections/allenai/molmoact-data-mixture-6897e583e13b6c2cf3ea2b80)
@@ -64,7 +66,7 @@ ckpt = "allenai/MolmoAct-7B-D-Pretrain-RT-1-0812"
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  processor = AutoProcessor.from_pretrained(
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  ckpt,
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  trust_remote_code=True,
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- torch_dtype="auto",
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  device_map="auto",
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  padding_side="left",
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  )
@@ -73,7 +75,7 @@ processor = AutoProcessor.from_pretrained(
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  model = AutoModelForImageTextToText.from_pretrained(
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  ckpt,
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  trust_remote_code=True,
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- torch_dtype="auto",
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  device_map="auto",
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  )
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  This checkpoint is a **preview** of the MolmoAct release. All artifacts used in creating MolmoAct (data, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
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+ **Update:** Checkpoints are now stored in FP32 (previously BF16). The model was trained in FP32, so publishing FP32 weights aligns with training and enables fine-tuning or continued training directly from this repo. For inference, you can still run BF16 by casting at load, which is what we did for evaluations. See more in the [instructions](#quick-start) below.
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+
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  Quick links:
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  - πŸ“‚ [All Models](https://huggingface.co/collections/allenai/molmoact-689697591a3936fba38174d7)
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  - πŸ“‚ [All Data](https://huggingface.co/collections/allenai/molmoact-data-mixture-6897e583e13b6c2cf3ea2b80)
 
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  processor = AutoProcessor.from_pretrained(
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  ckpt,
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  trust_remote_code=True,
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+ torch_dtype="bfloat16",
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  device_map="auto",
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  padding_side="left",
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  )
 
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  model = AutoModelForImageTextToText.from_pretrained(
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  ckpt,
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  trust_remote_code=True,
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+ torch_dtype="bfloat16",
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  device_map="auto",
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  )
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