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
@@ -204,29 +201,28 @@ model-index:
       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-3.0-8B-Base
 
-## Model Summary
-**Granite-3.0-8B-Base** is an open-source decoder-only language model from IBM Research that supports a variety of text-to-text generation tasks (e.g., question-answering, text-completion). **Granite-3.0-8B-Base** is trained from scratch and follows a two-phase training strategy. In the first phase, it is trained on 10 trillion tokens sourced from diverse domains. During the second phase, it is further trained on 2 trillion tokens using a carefully curated mix of high-quality data, aiming to enhance its performance on specific tasks.
+**Model Summary:** 
+Granite-3.0-8B-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It is trained from scratch following a two-stage training strategy. In the first stage, it is trained on 10 trillion tokens sourced from diverse domains. During the second stage, it is further trained on 2 trillion tokens using a carefully curated mix of high-quality data, aiming to enhance its performance on specific tasks.
 
 - **Developers:** IBM Research
 - **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 3.0 Language Models]()
+- **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)
 
-<!-- de/es/fr/ja/pt/ar/cs/it/ko/nl/zh -->
-## Supported Languages
-English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese 
+**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:** 
 Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. 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-3.0-8B-Base** model.
+**Generation:** 
+This is a simple example of how to use Granite-3.0-8B-Base model.
 
 Install the following libraries:
 
@@ -258,8 +254,8 @@ output = tokenizer.batch_decode(output)
 print(output)
 ```
 
-## Model Architeture
-**Granite-3.0-8B-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 embbeddings.
+**Model Architeture:** 
+Granite-3.0-8B-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 embbeddings.
 
 | Model                     | 2B Dense | 8B Dense     | 1B MoE | 3B MoE |
 | :--------                 | :--------| :--------    | :------| :------|
@@ -279,19 +275,18 @@ 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 -->
-## Training Data
-This model is trained on a mix of open-source and proprietary data following a two-phase training strategy.
-* Phase 1 data: The data for phase 1 is sourced from diverse domains, such as: web, code, academic sources, books, and math data.
-* Phase 2 data: The data for phase 2 comprises a curated mix of high-quality data from the same domains, plus multilingual and instruction data. The goal of this second training phase is to enhance the model’s performance on specific tasks.
+**Training Data:** 
+This model is trained on a mix of open source and proprietary data following a two-phase training strategy.
+* Stage 1 data: The data for phase 1 is sourced from diverse domains, such as: web, code, academic sources, books, and math data.
+* Stage 2 data: The data for phase 2 comprises a curated mix of high-quality data from the same domains, plus multilingual and instruction data. The goal of this second training phase is to enhance the model’s performance on specific tasks.
   
-## 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.
+**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.
 
-## Ethical Considerations and Limitations
+**Ethical Considerations and Limitations:** 
 The use of Large Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. **Granite-3.0-8B-Base** model is not the exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying text verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use **Granite-3.0-8B-Base** model with ethical intentions and in a responsible way. 
 
-## Citation
+<!-- ## Citation
 ```
 @misc{granite-models,
   author = {author 1, author2, ...},
@@ -301,4 +296,4 @@ The use of Large Language Models involves risks and ethical considerations peopl
   year = {2024},
   url = {https://arxiv.org/abs/0000.00000},
 }
-```
+``` -->