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README.md

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----
-license: apache-2.0
-language:
-- en
-pipeline_tag: text-generation
-library_name: transformers
-base_model: ibm-granite/granite-4.0-micro
-tags:
-- granite
-- uncertainty
-- context-attribution
-- requirement-checking
-- lora
-- peft
----
-
-# Granite Core Library
-
-The Granite Core Library includes three intrinsics implemented as LoRA adapters for [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro). Each intrinsic has been developed for a specific task that enhances the base model's capabilities for explainability, calibration, and constraint verification. We give a brief overview of the functionality of each intrinsic, as the details can be found in each individual intrinsic readme.
-
-
-## Intrinsics implemented as LoRA adapters
-
-The three intrinsics that have been implemented as LoRA adapters for [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro) and made available in this HF repository are:
-
-**Context Attribution:** A LoRA adapter that predicts which sentences in the context (including documents and previous conversation turns) were most important for the base model to generate each sentence in its response. The adapter provides contributive attribution, identifying the context sentences that actually influenced the model's response, and ranks them by importance. This is a post-generation intrinsic designed to explain the base model's behavior. Details can be found in the [context-attribution readme](context-attribution/granite-4.0-micro/README.md).
-
-**Requirement Check:** A LoRA adapter that judges whether an assistant's generation satisfies a set of user-specified requirements, outputting a binary yes/no assessment. It is designed for evaluating constraint satisfaction in instruction-following scenarios, including formatting, content, structural requirements, and quality assurance pipelines. Details can be found in the [requirement-check readme](requirement-check/granite-4.0-micro/README.md).
-
-**Uncertainty:** A LoRA adapter that provides calibrated certainty scores for the base model's responses. Given a conversation ending with an assistant response, it calculates a certainty percentage reflecting how confident the model is about its answer. Certainty scores are calibrated such that among answers assigned a score of X%, approximately X% are correct. Details can be found in the [uncertainty readme](uncertainty/granite-4.0-micro/README.md).
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+base_model: ibm-granite/granite-4.0-micro
+tags:
+- granite
+- uncertainty
+- context-attribution
+- requirement-checking
+- lora
+- peft
+---
+
+# Granite Core Library
+
+The Granite Core Library includes three intrinsics implemented as LoRA adapters for [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro). Each adapter has been developed for a specific task that enhances the base model's capabilities for explainability, calibration, and constraint verification. We give a brief overview of the functionality of each adapter, as the details can be found in each individual adapter readme.
+
+
+## Capabilities implemented as LoRA adapters
+
+The three capabilities that have been implemented as LoRA adapters for [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro) and made available in this HF repository are:
+
+**Context Attribution:** A LoRA adapter that predicts which sentences in the context (including documents and previous conversation turns) were most important for the base model to generate each sentence in its response. The adapter provides contributive attribution, identifying the context sentences that actually influenced the model's response, and ranks them by importance. This is a post-generation intrinsic designed to explain the base model's behavior. Details can be found in the [context-attribution readme](context-attribution/granite-4.0-micro/README.md).
+
+**Requirement Check:** A LoRA adapter that judges whether an assistant's generation satisfies a set of user-specified requirements, outputting a binary yes/no assessment. It is designed for evaluating constraint satisfaction in instruction-following scenarios, including formatting, content, structural requirements, and quality assurance pipelines. Details can be found in the [requirement-check readme](requirement-check/granite-4.0-micro/README.md).
+
+**Uncertainty:** A LoRA adapter that provides calibrated certainty scores for the base model's responses. Given a conversation ending with an assistant response, it calculates a certainty percentage reflecting how confident the model is about its answer. Certainty scores are calibrated such that among answers assigned a score of X%, approximately X% are correct. Details can be found in the [uncertainty readme](uncertainty/granite-4.0-micro/README.md).