Update README.md

README.md

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 ### Training Data
 
-Rago v2 13B is trained using the [Neural Bridge's RAG Full 20000](https://huggingface.co/datasets/neural-bridge/rag-full-20000) dataset, which is a dataset that is mixture of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), [gms8k](https://huggingface.co/datasets/gsm8k), and [RAG Hallucination Dataset 1000](https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000).
+Rago v2 13B has been trained using the [Neural Bridge's RAG Full 20000](https://huggingface.co/datasets/neural-bridge/rag-full-20000) dataset, which comprises a blend of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), [gms8k](https://huggingface.co/datasets/gsm8k), and [RAG Hallucination Dataset 1000](https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000).
 
 ### Training Details
 
-Rago v2 13B is built upon [Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf) using [LoRA](https://arxiv.org/abs/2106.09685). It is designed to improve the model's ability to produce relevant, accurate, and context-specific output across specialized domains or internal data and to address key challenges of LLMs by taking advantage of the power of RAG, such as unpredictability, reliance on potentially outdated data, the propagation of incorrect information, etc. The models has the same architecture as [Llama 2 13B's](https://huggingface.co/meta-llama/Llama-2-13b-hf) in addition to the LoRA adapters. The model is trained with an NVIDIA A100 for around 2 days with 1e-5 learning rate (with cosine schedular) as well as the following LoRA parameters:
+In terms of training specifics, Rago v2 13B is built upon [Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf) employing [LoRA](https://arxiv.org/abs/2106.09685) to enhance the model's capability to deliver relevant, precise, and context-specific output across specialized domains or internal datasets. This approach aims to tackle significant challenges faced by LLMs, such as unpredictability, reliance on potentially outdated information, and the spread of incorrect data. The architecture of Rago v2 13B mirrors that of [Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf), augmented with [LoRA](https://arxiv.org/abs/2106.09685) adapters. The model is trained on a single NVIDIA A100 GPU for approximately two days, utilizing a learning rate of *1e-5* with cosine scheduler, alongside the following LoRA parameters:
+
 * LoRA Rank (R): 64
 * LoRA Alpha: 16
 * LoRA Dropout: 0.1
 * Target Modules: *q_proj, k_proj, v_proj, o_proj*
 
-Rago v2 13B is boosted a custom data collator to enhance the model performace. It is trained embracing the masked language modeling (MLM) approach. Thereby, it is pushed to generate more accurate responses by only masking the answer part of the training data. Thanks to the custom data collator, it is observed improvement in the factuality performance of the model.
+Rago v2 13B benefits from a custom data collator designed to boost model performance significantly. Employing a masked language modeling (MLM) strategy, the model is fine-tuned to generate more accurate responses by exclusively masking the answer portion of the training data. This custom data collator has led to noticeable improvements in the model's factuality performance.
+
+## **Neural Bridge AI Rago Models Index**
+
+| Model       |  Link                                                     | Base Model
+| -----       | ------                                                    | ----------
+| Rago v1 1B  |  [link](https://huggingface.co/neural-bridge/Rago-v1-1b)  | [Falcon-RW-1B](https://huggingface.co/tiiuae/falcon-rw-1b)
+| Rago v1 7B  |  [link](https://huggingface.co/neural-bridge/Rago-v1-7b)  | [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b)
+| Rago v1 40B |  [link](https://huggingface.co/neural-bridge/Rago-v1-40b) |[Falcon-40B](https://huggingface.co/tiiuae/falcon-40b)
+| Rago v2 7B  |  [link](https://huggingface.co/neural-bridge/Rago-v2-7b)  | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf)
+| Rago v2 13B |  [link](https://huggingface.co/neural-bridge/Rago-v2-13b) | [Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf)
+
+## **Licence**
+
+This public extract is made available under [Apache licence 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). Users should also abide to the [Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf) ToU.