granite docling 258M mlx

[!NOTE] This repository contains the FP8 version of 4.1-30B. Please refer to the the original instruct model's model card for additional details: https://huggingface.co/ibm-granite/granite-4.1-30b

[!NOTE] This repository contains the FP8 version of 4.1-8B. Please refer to the the original instruct model's model card for additional details: https://huggingface.co/ibm-granite/granite-4.1-8b

Granite Guardian 4.1 8B introduces improved Bring Your Own Criteria (BYOC) support, enabling users to define arbitrary judging criteria beyond the pre-baked safety and hallucination detectors. The model can now faithfully evaluate complex, multi-part requirements such as formatting rules, length constraints, and domain-specific instructions.

Model Summary: Granite Vision 4.1 4B is a vision-language model (VLM) that delivers frontier-level performance on structured document extraction tasks — chart extraction, table extraction, and semantic key-value pair extraction — in a compact 4B parameter footprint, providing a lightweight alternative to much larger frontier models for these tasks:

Model Summary: Granite‑4.1‑30B‑Base is a decoder‑only language model with long‑context capabilities, designed to support a broad range of text‑to‑text generation tasks. In addition to standard generation, it supports Fill‑in‑the‑Middle (FIM) code completion through specialized prefix and suffix tokens. The model is trained from scratch on approximately 15 trillion tokens using a five‑phase training strategy: 10 trillion tokens in phase one, 2 trillion tokens each in phases two and three, and 0.5 trillion tokens in phase four. In the final phase, long‑context extension is applied to expand the

Model Summary: Granite-4.1-30B is a 30B parameter long-context instruct model finetuned from Granite-4.1-30B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities.