Fix broken links

README.md

@@ -37,7 +37,7 @@ pip install -e .
 ```
 ⚠️ **Note** ⚠️  Some features (especially fine-tuning with FSDP + PEFT) currently require PyTorch nightlies to be installed. Please make sure to install the nightlies if you're using these features following [this guide](https://pytorch.org/get-started/locally/).
 
-**Note** All the setting defined in [config files](./configs/) can be passed as args through CLI when running the script, there is no need to change from config files directly.
+**Note** All the setting defined in [config files](src/llama_recipes/configs/) can be passed as args through CLI when running the script, there is no need to change from config files directly.
 
 **Note** In case need to run PEFT model with FSDP, please make sure to use the PyTorch Nightlies.
 
@@ -84,7 +84,7 @@ All the parameters in the examples and recipes below need to be further tuned to
 
 * Default dataset and other LORA config has been set to `samsum_dataset`.
 
-* Make sure to set the right path to the model in the [training config](./configs/training.py).
+* Make sure to set the right path to the model in the [training config](src/llama_recipes/configs/training.py).
 
 ### Single GPU:
 
@@ -100,7 +100,7 @@ Here we make use of Parameter Efficient Methods (PEFT) as described in the next
 
 **Note** if you are running on a machine with multiple GPUs please make sure to only make one of them visible using `export CUDA_VISIBLE_DEVICES=GPU:id`
 
-**Make sure you set [save_model](configs/training.py) in [training.py](configs/training.py) to save the model. Be sure to check the other training settings in [train config](configs/training.py) as well as others in the config folder as needed or they can be passed as args to the training script as well.**
+**Make sure you set `save_model` parameter to save the model. Be sure to check the other training parameter in [train config](src/llama_recipes/configs/training.py) as well as others in the config folder as needed. All parameter can be passed as args to the training script. No need to alter the config files.**
 
 
 ### Multiple GPUs One Node:
@@ -157,20 +157,20 @@ You can read more about our fine-tuning strategies [here](./docs/LLM_finetuning.
 # Repository Organization
 This repository is organized in the following way:
 
-[configs](configs/): Contains the configuration files for PEFT methods, FSDP, Datasets.
+[configs](src/llama_recipes/configs/): Contains the configuration files for PEFT methods, FSDP, Datasets.
 
 [docs](docs/): Example recipes for single and multi-gpu fine-tuning recipes.
 
-[ft_datasets](ft_datasets/): Contains individual scripts for each dataset to download and process. Note: Use of any of the datasets should be in compliance with the dataset's underlying licenses (including but not limited to non-commercial uses)
+[datasets](src/llama_recipes/datasets/): Contains individual scripts for each dataset to download and process. Note: Use of any of the datasets should be in compliance with the dataset's underlying licenses (including but not limited to non-commercial uses)
 
 
-[inference](inference/): Includes examples for inference for the fine-tuned models and how to use them safely.
+[inference](src/llama_recipes/inference/): Includes examples for inference for the fine-tuned models and how to use them safely.
 
-[model_checkpointing](model_checkpointing/): Contains FSDP checkpoint handlers.
+[model_checkpointing](src/llama_recipes/model_checkpointing/): Contains FSDP checkpoint handlers.
 
-[policies](policies/): Contains FSDP scripts to provide different policies, such as mixed precision, transformer wrapping policy and activation checkpointing along with any precision optimizer (used for running FSDP with pure bf16 mode).
+[policies](src/llama_recipes/policies/): Contains FSDP scripts to provide different policies, such as mixed precision, transformer wrapping policy and activation checkpointing along with any precision optimizer (used for running FSDP with pure bf16 mode).
 
-[utils](utils/): Utility files for:
+[utils](src/llama_recipes/utils/): Utility files for:
 
 - `train_utils.py` provides training/eval loop and more train utils.
 

docs/inference.md

@@ -52,7 +52,7 @@ python -m llama_recipes.inference.chat_completion --model_name "PATH/TO/MODEL/7B
 
 Code llama was recently released with three flavors, base-model that support multiple programming languages, Python fine-tuned model and an instruction fine-tuned and aligned variation of Code Llama, please read more [here](https://ai.meta.com/blog/code-llama-large-language-model-coding/). Also note that the Python fine-tuned model and 34B models are not trained on infilling objective, hence can not be used for infilling use-case.
 
-Find the scripts to run Code Llama [here](../src/llama_recipes/inference/code-llama/), where there are two examples of running code completion and infilling.
+Find the scripts to run Code Llama [here](../src/llama_recipes/inference/code_llama/), where there are two examples of running code completion and infilling.
 
 **Note** Please find the right model on HF side [here](https://huggingface.co/codellama). 
 
@@ -131,4 +131,4 @@ Below is an example of how to run the vLLM_inference.py script found within the
 python -m llama_recipes.inference.vLLM_inference --model_name <PATH/TO/MODEL/7B>
 ```
 
-[**TGI**](https://github.com/huggingface/text-generation-inference): Text Generation Inference (TGI) is another inference option available to you. For more information on how to set up and use TGI see [here](../inference/hf-text-generation-inference/README.md).
+[**TGI**](https://github.com/huggingface/text-generation-inference): Text Generation Inference (TGI) is another inference option available to you. For more information on how to set up and use TGI see [here](../src/llama_recipes/inference/hf_text_generation_inference/README.md).

src/llama_recipes/inference/README.md

@@ -6,6 +6,6 @@ This folder contains inference examples for Llama 2. So far, we have provided su
 
 2. [vLLM_inference.py](vLLM_inference.py) script takes advantage of vLLM's paged attention concept for low latency.
 
-3. The [hf-text-generation-inference](hf-text-generation-inference/README.md) folder contains information on Hugging Face Text Generation Inference (TGI).
+3. The [hf_text_generation_inference](hf_text_generation_inference/README.md) folder contains information on Hugging Face Text Generation Inference (TGI).
 
-For more in depth information on inference including inference safety checks and examples, see the inference documentation [here](../docs/inference.md).
+For more in depth information on inference including inference safety checks and examples, see the inference documentation [here](../../../docs/inference.md).

src/llama_recipes/inference/hf_text_generation_inference/README.md

@@ -9,7 +9,7 @@ In case the model was fine tuned with LoRA method we need to merge the weights o
 The script takes the base model, the peft weight folder as well as an output as arguments:
 
 ```
-python inference/hf-text-generation-inference/merge_lora_weights.py --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output
+python -m llama_recipes.inference.hf_text_generation_inference.merge_lora_weights --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output
 ```
 
 ## Step 1: Serving the model