diff --git a/README.md b/README.md
index 2d0b3ff170b4777918486855f23da79cc9e54993..bbf32d6c9120c3f2182c7e8f562e1aa42cc85a3f 100644
--- a/README.md
+++ b/README.md
@@ -82,32 +82,6 @@ Since all these metrics range from [0, 1], we calculate the average metric for a
Please refer to [local_evaluation.py](local_evaluation.py) for more details on how we will evaluate your submissions.
-
-# ðŸ—ƒï¸ Submission
-
-The challenge would be evaluated as a code competition. Participants must submit their code and essential resources, such as fine-tuned model weights and indices for Retrieval-Augmented Generation (RAG), which will be run on our servers to generate results and then for evaluation.
-
-## Hardware and System Configuration
-We apply a limit on the hardware available to each participant to run their solutions. Specifically,
-
-- All solutions will be run on [AWS g4dn.12xlarge](https://aws.amazon.com/ec2/instance-types/g4/) instances equipped with [NVIDIA T4 GPUs](https://www.nvidia.com/en-us/data-center/tesla-t4/).
-- Solutions for Phase 1 will have access to `2` x [NVIDIA T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/s).
-- Solutions for Phase 2 will have access to `4` x [NVIDIA T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/s).
-Please note that NVIDIA T4 uses somewhat outdated architectures and is thus not compatible with certain acceleration toolkits (e.g. Flash Attention), so please be careful about compatibility.
-
-Besides, the following restrictions will also be imposed.
-
-- Network connection will be disabled (except for HuggingFace to download open-source checkpoints).
-- Each submission will be assigned a certain amount of time to run. Submissions that exceed the time limits will be killed and will not be evaluated. The tentative time limit is set as follows **[TO BE TESTED WITH AICROWD SUBMISSION SYSTEM]**.
-
-| Phase | Track 1 | Track 2 | Track 3 | Track 4 | Track 5 |
-| ------ | ------- | ------- | ------- | ------- | ------- |
-| **Phase 1**| 140 minutes | 40 minutes | 60 minutes | 60 minutes | 5 hours |
-
-- Each team will be able to make up to **4 submissions per week**, with a maximum of **2 Track 5 all-around submissions** **[TO BE TESTED WITH AICROWD SUBMISSION SYSTEM]**.
-
-Based on the hardware and system configuration, we recommend participants to begin with 7B models. According to our experiments, 7B models like Vicuna-7B and Mistral can perform inference smoothly on 2 NVIDIA T4 GPUs, while 13B models will result in OOM.
-
# Getting Started
1. **Sign up** to join the competition [on the AIcrowd website](https://www.aicrowd.com/challenges/amazon-kdd-cup-2024-multi-task-online-shopping-challenge-for-llms).
2. **Fork** this starter kit repository. You can use [this link](https://gitlab.aicrowd.com/aicrowd/challenges/amazon-kdd-cup-2024/amazon-kdd-cup-2024-starter-kit/-/forks/new) to create a fork.
@@ -150,105 +124,17 @@ You can add your SSH Keys to your GitLab account by going to your profile settin
6. Make a submission as described in [How to make a submission](#how-to-make-a-submission) section.
-## How do I specify my software runtime / dependencies?
-
-We accept submissions with custom runtime, so you don't need to worry about which libraries or framework to pick from.
-
-The configuration files typically include `requirements.txt` (pypi packages), `apt.txt` (apt packages) or even your own `Dockerfile`.
-
-An example Dockerfile is provided in [utilities/_Dockerfile](utilities/_Dockerfile) which you can use as a starting point.
-
-You can check detailed information about setting up runtime dependencies in the 👉 [docs/runtime.md](docs/runtime.md) file.
-
-## What should my code structure be like?
-
-Please follow the example structure as it is in the starter kit for the code structure.
-The different files and directories have following meaning:
-
-```
-.
-├── README.md # Project documentation and setup instructions
-├── aicrowd.json # Submission meta information - like your username, track name
-├── data
-│ └── development.json # Development dataset local testing
-├── docs
-│ └── runtime.md # Documentation on the runtime environment setup, dependency confifgs
-├── local_evaluation.py # Use this to check your model evaluation flow locally
-├── metrics.py # Scripts to calculate evaluation metrics for your model's performance
-├── models
-│ ├── README.md # Documentation specific to the implementation of model interfaces
-│ ├── base_model.py # Base model class
-│ ├── dummy_model.py # A simple or placeholder model for demonstration or testing
-│ └── user_config.py # IMPORTANT: Configuration file to specify your model
-├── parsers.py # Model output parser
-├── requirements.txt # Python packages to be installed for model development
-├── requirements_eval.txt # Additional Python packages to be installed for local evaluation
-└── utilities
- └── _Dockerfile # Example Dockerfile for specifying runtime via Docker
-```
-
-Finally, **you must specify your submission specific metadata JSON in `aicrowd.json`**
-
-The `aicrowd.json` of each submission should contain the following content:
-
-```json
-{
- "challenge_id": "amazon-kdd-cup-24-understanding-shopping-concepts",
- "authors": [
- "aicrowd-bot"
- ],
- "gpu": false,
- "description": "(optional) description about your awesome agent"
-}
-```
-**IMPORTANT: To use GPUs** - Set the GPU flag to `true`.
-
-This JSON is used to map your submission to the challenge - so please remember to use the correct `challenge_id` as specified above. You can modify the `authors` and `description` keys. Please DO NOT add any additional keys to `aicrowd.json` unless otherwise communicated during the course of the challenge.
-
-## How do I submit to different tracks ?
-
-You can submit to different tracks by specifying task specific the `challenge_id` in [aicrowd.json](aicrowd.json). Here are the challenge ids for the different tracks:
-
-Sure, let's create a markdown table based on the information provided:
-
-| Track Name | Challenge ID |
-|-------------------------------------------|-----------------------------------------|
-| Understanding Shopping Concepts | amazon-kdd-cup-24-understanding-shopping-concepts |
-| Shopping Knowledge Reasoning | amazon-kdd-cup-24-shopping-knowledge-reasoning |
-| User Behavior Alignment | amazon-kdd-cup-24-user-behavior-alignment |
-| Multi-Lingual Abilities | amazon-kdd-cup-24-multi-lingual-abilities |
-| All-Around | amazon-kdd-cup-24-all-around |
-
-
## How to make a submission?
Please follow the instructions in [docs/submission.md](ocs/submission.md) to make your first submission.
+This also includes instructions on **specifying your software runtime**, **code structure**, **submitting to different tracks**.
**Note**: **Remember to accept the Challenge Rules** on the challenge page, and task page before making your first submission.
-
-## Hardware and System Configuration
-We apply a limit on the hardware available to each participant to run their solutions. Specifically,
-
-- All solutions will be run on [AWS g4dn.12xlarge](https://aws.amazon.com/ec2/instance-types/g4/) instances equipped with [NVIDIA T4 GPUs](https://www.nvidia.com/en-us/data-center/tesla-t4/).
-- Solutions for Phase 1 will have access to `2` x [NVIDIA T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/s).
-- Solutions for Phase 2 will have access to `4` x [NVIDIA T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/s).
-Please note that NVIDIA T4 uses somewhat outdated architectures and is thus not compatible with certain acceleration toolkits (e.g. Flash Attention), so please be careful about compatibility.
-
-Besides, the following restrictions will also be imposed.
-
-- Network connection will be disabled (except for HuggingFace to download open-source checkpoints).
-- Each submission will be assigned a certain amount of time to run. Submissions that exceed the time limits will be killed and will not be evaluated. The tentative time limit is set as follows **[TO BE TESTED WITH AICROWD SUBMISSION SYSTEM]**.
-
-| Phase | Track 1 | Track 2 | Track 3 | Track 4 | Track 5 |
-| ------ | ------- | ------- | ------- | ------- | ------- |
-| **Phase 1**| 140 minutes | 40 minutes | 60 minutes | 60 minutes | 5 hours |
-
-- Each team will be able to make up to **4 submissions per week**, with a maximum of **2 Track 5 all-around submissions** **[TO BE TESTED WITH AICROWD SUBMISSION SYSTEM]**.
-
-Based on the hardware and system configuration, we recommend participants to begin with 7B models. According to our experiments, 7B models like Vicuna-7B and Mistral can perform inference smoothly on 2 NVIDIA T4 GPUs, while 13B models will result in OOM.
-
+## What hardware does my code run on ?
+You can find more details about the hardware and system configuration in [docs/hardware-and-system-config.md](docs/hardware-and-system-config.md).
+In summary, we provide you `2` x [[NVIDIA T4 GPUs](https://www.nvidia.com/en-us/data-center/tesla-t4/)] in Phase 1; and `4` x [[NVIDIA T4 GPUs](https://www.nvidia.com/en-us/data-center/tesla-t4/)] in Phase 2.
**Best of Luck** :tada: :tada:
diff --git a/docs/hardware-and-system-config.md b/docs/hardware-and-system-config.md
new file mode 100644
index 0000000000000000000000000000000000000000..eefd3dc50cbc3f4c8be1cfa4a5415d9e11f0e86b
--- /dev/null
+++ b/docs/hardware-and-system-config.md
@@ -0,0 +1,28 @@
+## Hardware and System Configuration
+We apply a limit on the hardware available to each participant to run their solutions. Specifically,
+
+- All solutions will be run on [AWS g4dn.12xlarge](https://aws.amazon.com/ec2/instance-types/g4/) instances equipped with [NVIDIA T4 GPUs](https://www.nvidia.com/en-us/data-center/tesla-t4/).
+- Solutions for Phase 1 will have access to :
+ - `2` x [NVIDIA T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/s).
+ - `20` x vCPU (`10` physical CPU cores)
+ - `90GB` RAM
+- Solutions for Phase 2 will have access to:
+ - `4` x [NVIDIA T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/s).
+ - `40` x vCPU (`20` physical CPU cores)
+ - `180GB` RAM
+
+
+Please note that NVIDIA T4 uses a somewhat outdated architectures and is thus not compatible with certain acceleration toolkits (e.g. Flash Attention), so please be careful about compatibility.
+
+Besides, the following restrictions will also be imposed:
+
+- Network connection will be disabled (except for HuggingFace to download open-source checkpoints).
+- Each submission will be assigned a certain amount of time to run. Submissions that exceed the time limits will be killed and will not be evaluated. The tentative time limit is set as follows **[TO BE TESTED WITH AICROWD SUBMISSION SYSTEM]**.
+
+| Phase | Track 1 | Track 2 | Track 3 | Track 4 | Track 5 |
+| ------ | ------- | ------- | ------- | ------- | ------- |
+| **Phase 1**| 140 minutes | 40 minutes | 60 minutes | 60 minutes | 5 hours |
+
+- Each team will be able to make up to **4 submissions per week**, with a maximum of **2 Track 5 all-around submissions** **[TO BE TESTED WITH AICROWD SUBMISSION SYSTEM]**.
+
+Based on the hardware and system configuration, we recommend participants to begin with 7B models. According to our experiments, 7B models like Vicuna-7B and Mistral can perform inference smoothly on 2 NVIDIA T4 GPUs, while 13B models will result in OOM.
diff --git a/docs/submission.md b/docs/submission.md
index a4995086f566287d1a269146a48b31ee7b9b59e3..a84c86d52a563ead6a723a811152bc6de189a54b 100644
--- a/docs/submission.md
+++ b/docs/submission.md
@@ -2,6 +2,76 @@
This file will help you in making your first submission.
+## How do I specify my software runtime / dependencies?
+
+We accept submissions with custom runtime, so you don't need to worry about which libraries or framework to pick from.
+
+The configuration files typically include `requirements.txt` (pypi packages), `apt.txt` (apt packages) or even your own `Dockerfile`.
+
+An example Dockerfile is provided in [utilities/_Dockerfile](utilities/_Dockerfile) which you can use as a starting point.
+
+You can check detailed information about setting up runtime dependencies in the 👉 [docs/runtime.md](docs/runtime.md) file.
+
+## What should my code structure be like?
+
+Please follow the example structure as it is in the starter kit for the code structure.
+The different files and directories have following meaning:
+
+```
+.
+├── README.md # Project documentation and setup instructions
+├── aicrowd.json # Submission meta information - like your username, track name
+├── data
+│ └── development.json # Development dataset local testing
+├── docs
+│ └── runtime.md # Documentation on the runtime environment setup, dependency confifgs
+├── local_evaluation.py # Use this to check your model evaluation flow locally
+├── metrics.py # Scripts to calculate evaluation metrics for your model's performance
+├── models
+│ ├── README.md # Documentation specific to the implementation of model interfaces
+│ ├── base_model.py # Base model class
+│ ├── dummy_model.py # A simple or placeholder model for demonstration or testing
+│ └── user_config.py # IMPORTANT: Configuration file to specify your model
+├── parsers.py # Model output parser
+├── requirements.txt # Python packages to be installed for model development
+├── requirements_eval.txt # Additional Python packages to be installed for local evaluation
+└── utilities
+ └── _Dockerfile # Example Dockerfile for specifying runtime via Docker
+```
+
+Finally, **you must specify your submission specific metadata JSON in `aicrowd.json`**
+
+The `aicrowd.json` of each submission should contain the following content:
+
+```json
+{
+ "challenge_id": "amazon-kdd-cup-24-understanding-shopping-concepts",
+ "authors": [
+ "aicrowd-bot"
+ ],
+ "gpu": false,
+ "description": "(optional) description about your awesome agent"
+}
+```
+**IMPORTANT: To use GPUs** - Set the GPU flag to `true`.
+
+This JSON is used to map your submission to the challenge - so please remember to use the correct `challenge_id` as specified above. You can modify the `authors` and `description` keys. Please DO NOT add any additional keys to `aicrowd.json` unless otherwise communicated during the course of the challenge.
+
+## How do I submit to different tracks ?
+
+You can submit to different tracks by specifying task specific the `challenge_id` in [aicrowd.json](aicrowd.json). Here are the challenge ids for the different tracks:
+
+Sure, let's create a markdown table based on the information provided:
+
+| Track Name | Challenge ID |
+|-------------------------------------------|-----------------------------------------|
+| Understanding Shopping Concepts | amazon-kdd-cup-24-understanding-shopping-concepts |
+| Shopping Knowledge Reasoning | amazon-kdd-cup-24-shopping-knowledge-reasoning |
+| User Behavior Alignment | amazon-kdd-cup-24-user-behavior-alignment |
+| Multi-Lingual Abilities | amazon-kdd-cup-24-multi-lingual-abilities |
+| All-Around | amazon-kdd-cup-24-all-around |
+
+
## Submission Entrypoint