Update README.md

README.md

@@ -16,7 +16,69 @@ This model was converted to GGUF format from [`ibm-granite/granite-3.2-2b-instru
 Refer to the [original model card](https://huggingface.co/ibm-granite/granite-3.2-2b-instruct) for more details on the model.
 
 ---
+Model Summary:
+-
+Granite-3.2-2B-Instruct is an 2-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of Granite-3.1-2B-Instruct,
+ it has been trained using a mix of permissively licensed open-source 
+datasets and internally generated synthetic data designed for reasoning 
+tasks. The model allows controllability of its thinking capability, 
+ensuring it is applied only when required.
 
+
+Developers: Granite Team, IBM
+Website: Granite Docs
+Release Date: February 26th, 2025
+License: Apache 2.0
+
+
+Supported Languages:
+-
+English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, 
+Italian, Korean, Dutch, and Chinese. However, users may finetune this 
+Granite model for languages beyond these 12 languages.
+
+
+Intended Use:
+-
+This model is designed to handle general instruction-following tasks and
+ can be integrated into AI assistants across various domains, including 
+business applications.
+
+
+Capabilities
+-
+Thinking
+Summarization
+Text classification
+Text extraction
+Question-answering
+Retrieval Augmented Generation (RAG)
+Code related tasks
+Function-calling tasks
+Multilingual dialog use cases
+Long-context tasks including long document/meeting summarization, long document QA, etc.
+
+
+Generation:
+-
+This is a simple example of how to use Granite-3.2-2B-Instruct model.
+
+
+Install the following libraries:
+-
+
+pip install torch torchvision torchaudio
+pip install accelerate
+pip install transformers
+
+Then, copy the snippet from the section that is relevant for your use case.
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+import torch
+
+model_path="ibm-granite/granite-3.2-2b-instruct"
+device="cuda"
 model = AutoModelForCausalLM.from_pretrained(
         model_path,
         device_map=device,