docs: update the figure style

docs/docs/blog/00-surpassing-chatgpt-with-open-source-alternatives.mdx

@@ -35,7 +35,7 @@ Problems still arise with catastrophic forgetting in general tasks, commonly obs
 
 ![Mistral vs LLama vs Gemma](assets/mistral-comparasion.png)
 
-_Figure 1. Mistral 7B excels in benchmarks, ranking among the top foundational models._
+_Figure 1._ Mistral 7B excels in benchmarks, ranking among the top foundational models.
 
 _Note: we are not sponsored by the Mistral team. Though many folks in their community do like to run Mistral locally using our desktop client - [Jan](https://jan.ai/)._
 
@@ -45,7 +45,7 @@ Mistral alone has known, poor math capabilities, which we needed for our highly
 
 ![Merged model vs finetuned models](assets/stealth-comparasion.png)
 
-_Figure 2: The merged model, Stealth, doubles the mathematical capabilities of its foundational model while retaining the performance in other tasks._
+_Figure 2._ The merged model, Stealth, doubles the mathematical capabilities of its foundational model while retaining the performance in other tasks.
 
 We found merging models is quick and cost-effective, enabling fast adjustments based on the result of each iteration.
 
@@ -95,13 +95,13 @@ This final model is publicly available at https://huggingface.co/jan-hq/nitro-v1
 
 ![Using LLM locally](assets/nitro-on-jan.png)
 
-_Figure 3. Using the new finetuned model in [Jan](https://jan.ai/)_
+_Figure 3._ Using the new finetuned model in [Jan](https://jan.ai/)
 
 ## Improving Results With Rag
 
 As an additional step, we also added [Retrieval Augmented Generation (RAG)](https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/) as an experiment parameter.
 
-A simple RAG setup was done using **[Llamaindex](https://www.llamaindex.ai/)** and the **[bge-en-base-v1.5 embedding](https://huggingface.co/BAAI/bge-base-en-v1.5)** model for efficient documentation retrieval and question-answering. You can find the RAG implementation [here](https://github.com/janhq/open-foundry/blob/main/rag-is-not-enough/rag/nitro_rag.ipynb).
+A simple RAG setup was done using **[Llamaindex](https://www.llamaindex.ai/)** and the **[bge-en-base-v1.5 embedding](https://huggingface.co/BAAI/bge-base-en-v1.5)** model for efficient documentation retrieval and question-answering. The RAG implementation is publicly available at at https://github.com/janhq/open-foundry/blob/main/rag-is-not-enough/rag/nitro_rag.ipynb
 
 ## Benchmarking the Results
 
@@ -109,10 +109,9 @@ We curated a new set of [50 multiple-choice questions](https://github.com/janhq/
 
 ![Opensource model outperforms GPT](assets/rag-comparasion.png)
 
-_Figure 4. Comparison between fine-tuned model and OpenAI's GPT._
+_Figure 4._ Comparison between fine-tuned model and OpenAI's GPT.
-
-**Results**
 
+_Table 1._ Result of the Benchmarking.
 | Approach | Performance |
 | ----------------------------------------------------------------------------------- | ----------- |
 | GPT-3.5 with RAG | 56.7% |