Update README.md

README.md

@@ -15,6 +15,101 @@ base_model: ibm-granite/granite-3.1-2b-instruct
 This model was converted to GGUF format from [`ibm-granite/granite-3.1-2b-instruct`](https://huggingface.co/ibm-granite/granite-3.1-2b-instruct) 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-instruct) for more details on the model.
 
+---
+Model details:
+-
+Granite-3.1-2B-Instruct is a 2B parameter long-context instruct model
+ finetuned from Granite-3.1-2B-Base using a combination of open source 
+instruction datasets with permissive license and internally collected 
+synthetic datasets tailored for solving long context problems. This 
+model is developed using a diverse set of techniques with a structured 
+chat format, including supervised finetuning, model alignment using 
+reinforcement learning, and model merging.
+
+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: 
+The model is designed to respond to general instructions and can be used
+ to build AI assistants for multiple domains, including business 
+applications.
+
+
+Capabilities
+
+
+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-3.1-2B-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.
+
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "auto"
+model_path = "ibm-granite/granite-3.1-2b-instruct"
+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
+chat = [
+    { "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." },
+]
+chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+# tokenize the text
+input_tokens = tokenizer(chat, return_tensors="pt").to(device)
+# generate output tokens
+output = model.generate(**input_tokens, 
+                        max_new_tokens=100)
+# decode output tokens into text
+output = tokenizer.batch_decode(output)
+# print output
+print(output)
+
+
+
+Model Architecture:
+
+Granite-3.1-2B-Instruct 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)