fix: a broken link, an image, and punctuation (#7795)

* introduction-to-llms.md - editing a broken link and an image, and punctuation

- Editing the URL of the image, it was broken.
- Editing the URL of BERT, it was broken.
- Some punctuation corrections

* Reverted the URL update 

Because the URL of BERT was broken, I had updated it. But the previous URL works now.

src/data/guides/introduction-to-llms.md

@@ -23,20 +23,20 @@ Everyone these days is talking about LLMs, ChatGPT and what not. GitHub's [trend
 
 LLM stands for "Large Language Model." These are advanced AI systems designed to understand and generate human-like text based on the input they receive. These models have been trained on vast amounts of text data and can perform a wide range of language-related tasks, such as answering questions, carrying out conversations, summarizing text, translating languages, and much more.
 
-[![LLMS Visualized](/guides/llms.png)](https://twitter.com/kamrify/status/1658271217189634049)
+[![LLMS Visualized](https://roadmap.sh/guides/llms.png)](https://x.com/kamrify/status/1658271217189634049)
 
 
-OpenAI has been a major contributor to this space in the past few years with their models and research. However, there are other players in the market as well e.g. Meta with their [OPT](https://huggingface.co/facebook/opt-66b), [OPT-IML](https://huggingface.co/facebook/opt-iml-30b) and [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) models, Google released [FLAN-T5](https://huggingface.co/google/flan-t5-xxl) and [BERT](https://huggingface.co/bert-base-uncased), [StableLM](https://github.com/stability-AI/stableLM/) by Stability AI, [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) at Stanford and there are many [other opensource models as well](https://github.com/Hannibal046/Awesome-LLM).
+OpenAI has been a major contributor to this space in the past few years with their models and research. However, there are other players in the market as well e.g. Meta with their [OPT](https://huggingface.co/facebook/opt-66b), [OPT-IML](https://huggingface.co/facebook/opt-iml-30b) and [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) models, Google released [FLAN-T5](https://huggingface.co/google/flan-t5-xxl) and [BERT](https://huggingface.co/google-bert/bert-base-uncased), [StableLM](https://github.com/stability-AI/stableLM/) by Stability AI, [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) at Stanford and there are many [other opensource models as well](https://github.com/Hannibal046/Awesome-LLM).
 
 ## Training an LLM Model
 
 On a high level, training an LLM model involves three steps i.e. data collection, training and evaluation.
 
-- **Data Collection** The first step is to collect the data that will be used to train the model. The data can be collected from various sources such as Wikipedia, news articles, books, websites etc.
+- **Data Collection**: The first step is to collect the data that will be used to train the model. The data can be collected from various sources, such as Wikipedia, news articles, books, websites, etc.
 
 - **Training**: The data then goes through a training pipeline where it is cleaned and preprocessed before being fed into the model for training. The training process usually takes a long time and requires a lot of computational power.
 
-- **Evaluation**: The final step is to evaluate the performance of the model to see how well it performs on various tasks such as question answering, summarization, translation etc.
+- **Evaluation**: The final step is to evaluate the performance of the model to see how well it performs on various tasks, such as question answering, summarization, translation, etc.
 
 The output from the training Pipeline is an LLM model which is simply the parameters or weights which capture the knowledge learned during the training process. These parameters or weights are typically serialized and stored in a file, which can then be loaded into any application that requires language processing capabilities e.g. text generation, question answering, language processing etc.
 
@@ -62,6 +62,6 @@ To build an Instruction Tuned LLM, a Base LLM is taken and is further trained us
 
 ## Conclusion
 
-LLMs are a powerful tool that can be used to solve a wide range of language-related tasks. They are being used in various industries such as healthcare, finance, education etc. to automate processes and improve efficiency. LLMs have the potential to revolutionize the way we interact with computers and make our lives easier.
+LLMs are a powerful tool that can be used to solve a wide range of language-related tasks. They are being used in various industries, such as healthcare, finance, education, etc., to automate processes and improve efficiency. LLMs have the potential to revolutionize the way we interact with computers and make our lives easier.
 
 And that's it for this guide. We have some exciting AI related content coming up in the next few weeks. Stay tuned!