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1qh/chandra-ocr-2-oq8-mlx
chandra-ocr-2-oq8-mlx is a image-text-to-text model from 1qh. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as apache-2.0.
datalab-to/chandra-ocr-2 quantized with oQ (oMLX v0.5.3) at level 8.0, data-driven mixed precision: oQ measures each layer's quantization error through calibration and allocates bits where the measurement says they ma…
Downloads · 30 days
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24% of all-time downloads
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4.5B
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.safetensors5.1 GB · 100%
How the weights are stored.
U324.2B · 93%
From the Hugging Face model README
datalab-to/chandra-ocr-2 quantized with oQ (oMLX v0.5.3) at level 8.0,
data-driven mixed precision: oQ measures each layer's quantization error through calibration and
allocates bits where the measurement says they matter, rather than by a fixed per-tensor rule.
| Base | datalab-to/chandra-ocr-2 |
| oQ level | 8.0 (effective ~8.0 bits/weight) |
| Size | 5.1 GB (from 9.1 GB bf16) |
| Sensitivity entries | 248 |
| oMLX | v0.5.3 |
Table-column fidelity: PRESERVED. On the OCR fidelity check this build reads each config-table column into its own field.
Vision weights are kept fp16; only the language tower is quantized.
from mlx_vlm import load, generate
model, processor = load("1qh/chandra-ocr-2-oq8-mlx")