Merge branch 'main' into fsdp_lmm

.github/scripts/spellcheck_conf/wordlist.txt

@@ -1483,3 +1483,4 @@ ttft
 uv
 8xL40S
 xL
+EDA

docs/multi_gpu.md

@@ -4,7 +4,7 @@ To run fine-tuning on multi-GPUs, we will  make use of two packages:
 
 1. [PEFT](https://huggingface.co/blog/peft) methods and in particular using the Hugging Face [PEFT](https://github.com/huggingface/peft)library.
 
-2. [FSDP](https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html) which helps us parallelize the training over multiple GPUs. [More details](LLM_finetuning.md/#2-full-partial-parameter-finetuning).
+2. [FSDP](https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html) which helps us parallelize the training over multiple GPUs. [More details](./LLM_finetuning.md).
 
 Given the combination of PEFT and FSDP, we would be able to fine tune a Meta Llama 8B model on multiple GPUs in one node.
 For big models like 405B we will need to fine-tune in a multi-node setup even if 4bit quantization is enabled.

recipes/experimental/long_context/H2O/README.md

@@ -36,7 +36,7 @@ Expected results on XSUM (Rouge-2 score, the higher the better) from the above s
 
 ### One Demo on Streaming to "Infinite" Context Length
 
-The following example demonstrates the generation process of "infinite" sequence length. We use MT-Bench data and generate the context sample-by-sample. The KV Cache will keep the KV pairs from the previous samples while maintain a fixed size. Results can be found on [Demo](https://allenz.work/?p=11) (Video 1).
+The following example demonstrates the generation process of "infinite" sequence length. We use MT-Bench data and generate the context sample-by-sample. The KV Cache will keep the KV pairs from the previous samples while maintain a fixed size.
 
 ```
 # run with full cache

recipes/quickstart/agents/DeepLearningai_Course_Notebooks/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/dlai/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {