This is a mixed-precision, inference-ready quantization of allenai/MolmoPoint-8B. The 7.6B-parameter language transformer uses bitsandbytes NF4 with BF16 compute and double quantization. The vision encoder, multimodal connector, visual embedding projection, and pointing head remain BF16 to preserve pointing behavior. This is intentionally not an entirely 4-bit model. Quantizing the visual path caused substantial pointing degradation during recipe selection. The figure compares both released quantizations with the BF16 baseline. See Evaluation for coverage, protocol, and metric details. and pointing head The included quantizationprovenance.json records the resolved base revision, complete…
Independent publisher
Erdi Kara
erdi28
Computer Vision, Scientific Machine Learning
Models in Library2
Datasets in Library0
Models on Hugging Face4
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Published tasks: Image and text to text.
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This is an inference-ready bitsandbytes LLM.int8 quantization of allenai/MolmoPoint-8B. All eligible linear modules are quantized except the 7.5M-parameter pointing head, which remains BF16. This avoids a runtime patch because bitsandbytes 0.50.0's INT8 kernel does not natively accept the pointing head's high-rank inputs. The figure compares both released quantizations with the BF16 baseline. See Evaluation for coverage, protocol, and metric details. the model) The included quantizationprovenance.json records the resolved base revision, complete quantization recipe, software versions, and build environment. The checkpoint was built and validated with Python 3.12, Transformers 4.57.1…