update after review

README.md

@@ -2,9 +2,6 @@
 pipeline_tag: text-generation
 inference: false
 license: apache-2.0
-# datasets:
-# metrics:
-# - code_eval
 library_name: transformers
 tags:
 - language
@@ -203,40 +200,40 @@ model-index:
       value: 
       veriefied: false 
 ---
+
 <!-- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62cd5057674cdb524450093d/1hzxoPwqkBJXshKVVe6_9.png) -->
+![image/png](granite-3_0-language-models_Group_1.png)
 
 # Granite-3.0-2B-Instruct
 
-## Model Summary
-**Granite-3.0-2B-Instruct** is a lightweight and open-source 2B parameter model fine tuned from *Granite-3.0-2B-Base* on a combination of open-source and proprietary instruction data with a **permissively licensed**. This language model is designed to excel in instruction following tasks such as summarization, problem-solving, text translation, reasoning, code tasks, funcion-calling, and more. 
-<!-- The lightweight and open-source nature of this model makes it an excellent choice to serve as backbone of real-time applications such as chatbots and conversational agents. -->
+**Model Summary:**
+Granite-3.0-2B-Instruct is a 2B parameter model finetuned from *Granite-3.0-2B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. 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:** IBM Research
-- **GitHub Repository:** [ibm-granite/granite-language-models](https://github.com/ibm-granite/granite-language-models)
+- **GitHub Repository:** [ibm-granite/granite-3.0-language-models](https://github.com/ibm-granite/granite-3.0-language-models)
 - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
-- **Paper:** [Granite Language Models](https://) <!--     TO DO: Update github repo link when it is ready -->
+- **Paper:** [Granite 3.0 Language Models](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/granite-3-language-models.pdf)
 - **Release Date**: October 21st, 2024
-- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
-## Supported Languages
-English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese (Simplified) 
+**Supported Languages:** 
+English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may fintune Granite 3.0 models for languages beyond these 12 languages.
 
-## Usage
-### Intended use
+**Intended use:** 
 The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including bussiness applications.
 
-### Capabilities
+*Capabilities*
 * Summarization
 * Text classification
 * Text extraction
 * Question-answering
 * Retrieval Augmented Generation (RAG)
-* Code related 
-* Function-calling
+* Code related tasks
+* Function-calling tasks
 * Multilingual dialog use cases
 
-### Generation
-This is a simple example of how to use **Granite-3.0-2B-Instruct** model.
+**Generation:** 
+This is a simple example of how to use Granite-3.0-2B-Instruct model.
 
 Install the following libraries:
 
@@ -273,11 +270,8 @@ output = tokenizer.batch_decode(output)
 print(output)
 ```
 
-<!-- TO DO: function-calling-example
- -->
-
-## Model Architeture
-**Granite-3.0-2B-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 embbeddings.
+**Model Architeture:** 
+Granite-3.0-2B-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 embbeddings.
 
 | Model                        | 2B Dense     | 8B Dense | 1B MoE   | 3B MoE   |
 | :--------                    | :--------    | :--------| :--------| :--------|
@@ -297,21 +291,23 @@ print(output)
 | # Active Parameters          | **2.5B**     | 8.1B     | 400M     | 800M     |
 | # Training tokens            | **12T**      | 12T      | 10T      | 10T      |
 
-<!-- TO DO: To be completed once the paper is ready, we may changed title to Supervised Finetuning -->
-## Training Data
-Granite Language Instruct models are trained on a collection of publicly available datasets with non-restrictive license, as well as an IBM collection of synthetic datasets. We annotated and filtered these datasets to only include high-quality instances from each of them in our final mixture. This dataset selection is representative of the following domains:
+**Training Data:** 
+Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) very small amounts of human-curated data. Please refer to [Granite 3.0 Language Models technical report](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/granite-3-language-models.pdf) for more details on the individual categories and datasets.
 
-* English datasets: [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), [WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub), [OASST-OctoPack](https://huggingface.co/datasets/bigcode/oasst-octopack), [Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater), [SoftAge-Multiturn](https://huggingface.co/datasets/SoftAge-AI/multi-turn_dataset), [Glaive-RAG-v1 ](https://huggingface.co/datasets/glaiveai/RAG-v1 ), [EvolKit-20k](https://huggingface.co/datasets/arcee-ai/EvolKit-20k ), [Magpie-Phi3-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Phi3-Pro-300K-Filtered). 
-* Multilingual datasets: [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) and IBM Synthetic datasets (e.g., Blue Multilingual, Daring Anteater Translated).
-* Code datasets: [Glaive Code Assistant V3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [SQL Create Context Instruction](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction), and [Self-OSS-Instruct-SC2](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k). Single and multi-turn IBM synthetic datasets, including a set of datasets generated via the evol-instruct method.
-* Math: [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), [StackMathQA](https://huggingface.co/datasets/math-ai/StackMathQA ), and [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
-* Tools: [xlam-function-calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k), [Glaive Function Calling V2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [Hermes Function Calling V1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1), and IBM Synthetic API data.
-* Safety: [SimpleSafetyTests](https://huggingface.co/datasets/Bertievidgen/SimpleSafetyTests), [HarmBench Behaviors](https://github.com/centerforaisafety/HarmBench/blob/main/data/behavior_datasets/harmbench_behaviors_text_all.csv), [Strong Reject](https://github.com/alexandrasouly/strongreject/blob/main/strongreject_dataset/strongreject_dataset.csv), [AdvBench](https://huggingface.co/datasets/walledai/AdvBench), [MistralGuard](https://huggingface.co/datasets/natolambert/xstest-v2-copy), [Do-Not-Answer](https://huggingface.co/datasets/LibrAI/do-not-answer), and IBM Synthetic data for safety.
+**Infrastructure:**
+We train Granite 3.0 Language Models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
 
-<!-- CHECK: removed Vela, only talk about blue-vela-->
-## Infrastructure
-We train the Granite Language models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
+**Ethical Considerations and Limitations:** 
+Granite 3.0 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.
 
-<!-- TO DO: Check multilingual statement once the paper is ready -->
-## Ethical Considerations and Limitations
-Granite instruct models are primarily finetuned using instruction-response pairs mostly in English, but also in German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese (Simplified). As this model has been exposed to multilingual data, it can handle multilingual dialog use cases with a limited performance in non-English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. The model also inherits ethical considerations and limitations from its base model. For more information, please refer to *[Granite-3.0-2B-Base](https://huggingface.co/ibm-granite/granite-3.0-2b-base)* model card.
+<!-- ## Citation
+```
+@misc{granite-models,
+  author = {author 1, author2, ...},
+  title = {},
+  journal = {},
+  volume = {},
+  year = {2024},
+  url = {https://arxiv.org/abs/0000.00000},
+}
+``` -->