Update README.md

README.md

@@ -13,6 +13,82 @@ base_model: ibm-granite/granite-3.1-2b-base
 This model was converted to GGUF format from [`ibm-granite/granite-3.1-2b-base`](https://huggingface.co/ibm-granite/granite-3.1-2b-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/ibm-granite/granite-3.1-2b-base) for more details on the model.
 
+---
+Model details:
+-
+Granite-3.1-2B-Base extends the context length of Granite-3.0-2B-Base
+ from 4K to 128K using a progressive training strategy by increasing the
+ supported context length in increments while adjusting RoPE theta until
+ the model has successfully adapted to desired length of 128K. This 
+long-context pre-training stage was performed using approximately 500B 
+tokens.
+
+Developers: Granite Team, IBM
+GitHub Repository: ibm-granite/granite-3.1-language-models
+Website: Granite Docs
+Paper: Granite 3.1 Language Models (coming soon) 
+Release Date: December 18th, 2024
+License: Apache 2.0
+
+
+Supported Languages: 
+English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, 
+Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 
+models for languages beyond these 12 languages.
+
+
+Intended Use:
+Prominent use cases of LLMs in text-to-text generation include 
+summarization, text classification, extraction, question-answering, and 
+other long-context tasks. All Granite Base models are able to handle 
+these tasks as they were trained on a large amount of data from various 
+domains. Moreover, they can serve as baseline to create specialized 
+models for specific application scenarios.
+
+
+Generation: 
+This is a simple example of how to use Granite-3.1-2B-Base model.
+
+
+Install the following libraries:
+
+
+pip install torch torchvision torchaudio
+pip install accelerate
+pip install transformers
+
+
+
+Then, copy the code snippet below to run the example.
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "auto"
+model_path = "ibm-granite/granite-3.1-2b-base"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+# drop device_map if running on CPU
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
+model.eval()
+# change input text as desired
+input_text = "Where is the Thomas J. Watson Research Center located?"
+# tokenize the text
+input_tokens = tokenizer(input_text, return_tensors="pt").to(device)
+# generate output tokens
+output = model.generate(**input_tokens,
+                        max_length=4000)
+# decode output tokens into text
+output = tokenizer.batch_decode(output)
+# print output
+print(output)
+
+
+
+Model Architecture: 
+Granite-3.1-2B-Base is based on a decoder-only dense transformer 
+architecture. Core components of this architecture are: GQA and RoPE, 
+MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)