pre-training data update

README.md

@@ -208,19 +208,7 @@ model-index:
 # 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, including natural language, math, code, and safety. 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.
-
-
-<!-- **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). The particular characteristics of this model, includig a dense architecture, small size, and open-source nature, make it an ideal baseline for finetuning other models requiring fast and/or real-time inference while keeping the need of deployment resources low. **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, including natural language, math, code, and safety. 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. -->
-
-<!-- Use Cases: 
-Dense LLMs: Suitable for scenarios where fast inference with a smaller model size is prioritized, such as real-time applications or deployment on resource-constrained devices. 
-MoE LLMs: Ideal for situations where large model capacity is needed while maintaining computational efficiency, like handling complex tasks or large datasets with high computational demands -->
-
-<!-- businesses seeking to implement advanced AI solutions. -->
-
-<!-- It is built with a similar technology used to create the Granite Code Models.  -->
-<!-- (e.g., dialog, reasoning, math, safety, code, tools) -->
+**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.
 
 - **Developers:** IBM Research
 - **GitHub Repository:** [ibm-granite/granite-language-models](https://github.com/ibm-granite/granite-language-models)
@@ -295,12 +283,10 @@ print(output)
 
 <!-- TO DO: To be completed once the paper is ready -->
 ## Training Data
-This model is trained on a mix of open-source and proprietary datasets.
-<!-- - **Data Collection and Filtering:** Pretraining knowledge data is sourced from the following publicly available sources [LIST OF SOURCES]. 
-- **Exact and Fuzzy Deduplication:** We adopt an aggressive deduplication strategy that includes both exact and fuzzy deduplication to remove documents having (near) identical code content.
-- **HAP, PII[, Malware Filtering]:** We apply a HAP content filter that reduces models' likelihood of generating hateful, abusive, or profane language. We also make sure to redact Personally Identifiable Information (PII) by replacing PII content (e.g., names, email addresses, keys, passwords) with corresponding tokens (e.g., ⟨NAME⟩, ⟨EMAIL⟩, ⟨KEY⟩, ⟨PASSWORD⟩). [Particularly for code data, we scan all datasets using [ClamAV](https://www.clamav.net/) to identify and remove instances of malware in the source code.]
-- **Natural Language Datasets:** In addition to collecting data from multiple sources for model training, we use several publicly available high-quality natural language datasets to improve the model's proficiency in critical tasks (e.g., language understanding, mathematical reasoning). Unlike the knowledge data, we do not deduplicate these datasets. -->
-
+This model is trained on a mix of open-source and proprietary datasets. 
+* Phase 1: The data for phase 1 is sourced from diverse domains, such as: web, code, academic sources, books, and math data.
+* Phase 2: 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.