fix some typos

README.md

@@ -286,7 +286,7 @@ Here are notable topics that didn't fit into other categories. Some are establis
 * **Model merging**: Merging trained models has become a popular way of creating performant models without any fine-tuning. The popular [mergekit](https://github.com/cg123/mergekit) library implements the most popular merging methods, like SLERP, [DARE](https://arxiv.org/abs/2311.03099), and [TIES](https://arxiv.org/abs/2311.03099).
 * **Multimodal models**: These models (like [CLIP](https://openai.com/research/clip), [Stable Diffusion](https://stability.ai/stable-image), or [LLaVA](https://llava-vl.github.io/)) process multiple types of inputs (text, images, audio, etc.) with a unified embedding space, which unlocks powerful applications like text-to-image.
 * **Interpretability**: Mechanistic interpretability techniques like Sparse Autoencoders (SAEs) made remarkable progress to provide insights about the inner workings of LLMs. This has also been applied with techniques such as abliteration, which allow you to modify the behavior of models without training.
-* **Test-time compute**: Scaling the compute budget during test time requires numerous calls and include specialized models like a Process Reward Model (PRM). Iterative steps with precise scoring significantly improve performance for complex reasoning tasks.
+* **Test-time compute**: Scaling the compute budget during test time requires numerous calls and involves specialized models like a Process Reward Model (PRM). Iterative steps with precise scoring significantly improve performance for complex reasoning tasks.
 
 📚 **References**:
 * [Merge LLMs with mergekit](https://mlabonne.github.io/blog/posts/2024-01-08_Merge_LLMs_with_mergekit.html) by Maxime Labonne: Tutorial about model merging using mergekit.
@@ -321,7 +321,7 @@ Running LLMs can be difficult due to high hardware requirements. Depending on yo
 ---
 ### 2. Building a Vector Storage
 
-Creating a vector storage is the first step to build a Retrieval Augmented Generation (RAG) pipeline. Documents are loaded, split, and relevant chunks are used to produce vector representations (embeddings) that are stored for future use during inference.
+Creating a vector storage is the first step to building a Retrieval Augmented Generation (RAG) pipeline. Documents are loaded, split, and relevant chunks are used to produce vector representations (embeddings) that are stored for future use during inference.
 
 * **Ingesting documents**: Document loaders are convenient wrappers that can handle many formats: PDF, JSON, HTML, Markdown, etc. They can also directly retrieve data from some databases and APIs (GitHub, Reddit, Google Drive, etc.).
 * **Splitting documents**: Text splitters break down documents into smaller, semantically meaningful chunks. Instead of splitting text after *n* characters, it's often better to split by header or recursively, with some additional metadata.
@@ -337,7 +337,7 @@ Creating a vector storage is the first step to build a Retrieval Augmented Gener
 ---
 ### 3. Retrieval Augmented Generation
 
-With RAG, LLMs retrieves contextual documents from a database to improve the accuracy of their answers. RAG is a popular way of augmenting the model's knowledge without any fine-tuning.
+With RAG, LLMs retrieve contextual documents from a database to improve the accuracy of their answers. RAG is a popular way of augmenting the model's knowledge without any fine-tuning.
 
 * **Orchestrators**: Orchestrators (like [LangChain](https://python.langchain.com/docs/get_started/introduction), [LlamaIndex](https://docs.llamaindex.ai/en/stable/), [FastRAG](https://github.com/IntelLabs/fastRAG), etc.) are popular frameworks to connect your LLMs with tools, databases, memories, etc. and augment their abilities.
 * **Retrievers**: User instructions are not optimized for retrieval. Different techniques (e.g., multi-query retriever, [HyDE](https://arxiv.org/abs/2212.10496), etc.) can be applied to rephrase/expand them and improve performance.
@@ -365,7 +365,7 @@ Real-life applications can require complex pipelines, including SQL or graph dat
 * [LangChain - Query Construction](https://blog.langchain.dev/query-construction/): Blog post about different types of query construction.
 * [LangChain - SQL](https://python.langchain.com/docs/use_cases/qa_structured/sql): Tutorial on how to interact with SQL databases with LLMs, involving Text-to-SQL and an optional SQL agent.
 * [Pinecone - LLM agents](https://www.pinecone.io/learn/series/langchain/langchain-agents/): Introduction to agents and tools with different types.
-* [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) by Lilian Weng: More theoretical article about LLM agents.
+* [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) by Lilian Weng: A more theoretical article about LLM agents.
 * [LangChain - OpenAI's RAG](https://blog.langchain.dev/applying-openai-rag/): Overview of the RAG strategies employed by OpenAI, including post-processing.
 * [DSPy in 8 Steps](https://dspy-docs.vercel.app/docs/building-blocks/solving_your_task): General-purpose guide to DSPy introducing modules, signatures, and optimizers.
 
@@ -410,7 +410,7 @@ In addition to traditional security problems associated with software, LLMs have
 * **Defensive measures**: The best way to protect your LLM applications is to test them against these vulnerabilities (e.g., using red teaming and checks like [garak](https://github.com/leondz/garak/)) and observe them in production (with a framework like [langfuse](https://github.com/langfuse/langfuse)).
 
 📚 **References**:
-* [OWASP LLM Top 10](https://owasp.org/www-project-top-10-for-large-language-model-applications/) by HEGO Wiki: List of the 10 most critic vulnerabilities seen in LLM applications.
+* [OWASP LLM Top 10](https://owasp.org/www-project-top-10-for-large-language-model-applications/) by HEGO Wiki: List of the 10 most critical vulnerabilities seen in LLM applications.
 * [Prompt Injection Primer](https://github.com/jthack/PIPE) by Joseph Thacker: Short guide dedicated to prompt injection for engineers.
 * [LLM Security](https://llmsecurity.net/) by [@llm_sec](https://twitter.com/llm_sec): Extensive list of resources related to LLM security.
 * [Red teaming LLMs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming) by Microsoft: Guide on how to perform red teaming with LLMs.