cli: add solutions guides

docs/marketplace/Guides/prepare-comfyui.md → docs/cli/Guides/Solutions/comfyui.md

@@ -1,14 +1,14 @@
 ---
-id: "prepare-comfyui"
-title: "Prepare a ComfyUI Workflow"
-slug: "/guides/prepare-comfyui"
-sidebar_position: 5
+id: "comfyui"
+title: "ComfyUI"
+slug: "/guides/solutions/comfyui"
+sidebar_position: 2
 ---
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-This guide provides step-by-step instructions for preparing a **ComfyUI** workflow with custom nodes before uploading it. For security reasons, you cannot upload custom nodes directly to a deployed ComfyUI.
+This guide provides step-by-step instructions for preparing a **ComfyUI** workflow with custom nodes to run on Super Protocol. For security reasons, you cannot upload custom nodes directly to a deployed ComfyUI.
 
 :::note
 
@@ -28,7 +28,7 @@ You can prepare your model, workflow, and custom node files manually or using Do
 
     1. Clone the [Super-Protocol/solutions](https://github.com/Super-Protocol/solutions/) GitHub repository to the location of your choosing:
 
-    ```
+    ```shell
     git clone https://github.com/Super-Protocol/solutions.git --depth 1
     ```
 
@@ -54,21 +54,21 @@ You can prepare your model, workflow, and custom node files manually or using Do
 
     Access the running container with the following command:
 
-    ```
+    ```shell
     docker exec -it comfyui bash
     ```
 
     Go to the `models` directory inside the container and download the model files to the corresponding subdirectories using the `wget` command. For example:
 
-    ```
+    ```shell
     wget https://huggingface.co/prompthero/openjourney/resolve/main/mdjrny-v4.safetensors
     ```
 
     **Copy from your computer**
 
     If you have the model on your computer, copy its files to the container using the following command:
 
-    ```
+    ```shell
     docker cp <LOCAL_FILE> comfyui:<CONTAINER_FILE>
     ```
 
@@ -77,7 +77,7 @@ You can prepare your model, workflow, and custom node files manually or using Do
 
     For example:
 
-    ```
+    ```shell
     docker cp ~/Downloads/openjourney/mdjrny-v4.safetensors comfyui:/opt/ComfyUI/models/checkpoints/mdjrny-v4.safetensors
     ```
 
@@ -87,7 +87,7 @@ You can prepare your model, workflow, and custom node files manually or using Do
 
     8. Unpack the archive using the following command:
 
-    ```
+    ```shell
     tar -xvzf snapshot.tar.gz -C <MODEL_DIRECTORY>
     ```
 
@@ -159,6 +159,6 @@ You can prepare your model, workflow, and custom node files manually or using Do
   </TabItem>
 </Tabs>
 
-## Contact Super Protocol
+## Support
 
-If you face any issues, contact Super Protocol on [Discord](https://discord.gg/superprotocol) or via the [contact form](https://superprotocol.zendesk.com/hc/en-us/requests/new).
+If you have any issues or questions, contact Super Protocol on [Discord](https://discord.gg/superprotocol) or via the [contact form](https://superprotocol.zendesk.com/hc/en-us/requests/new).

docs/cli/Guides/Solutions/tgwui.md

@@ -0,0 +1,6 @@
+---
+id: "tgwui"
+title: "Text Generation WebUI"
+slug: "/guides/solutions/tgwui"
+sidebar_position: 1
+---

docs/cli/Guides/Solutions/unsloth.md

@@ -0,0 +1,189 @@
+---
+id: "unsloth"
+title: "Unsloth"
+slug: "/guides/solutions/unsloth"
+sidebar_position: 3
+---
+
+This guide provides step-by-step instructions for fine-tuning an AI model using the Super Protocol packaging of [Unsloth](https://unsloth.ai/), an open-source framework for LLM fine-tuning and reinforcement learning.
+
+The <a id="solution"><span className="dashed-underline">solution</span></a> allows you to run fine-tuning within Super Protocol's Trusted Execution Environment (TEE). This provides enhanced security and privacy and enables a range of [confidential collaboration](https://docs.develop.superprotocol.com/cli/guides/fine-tune) scenarios.
+
+## Prerequisites
+
+- [SPCTL](https://docs.develop.superprotocol.com/cli/)
+- Git
+- BNB and SPPI tokens (opBNB) to pay for transactions and orders
+
+## Repository
+
+Clone the repository with Super Protocol solutions:
+
+```shell
+git clone https://github.com/Super-Protocol/solutions.git
+```
+
+The Unsloth solution includes a Dockerfile and a helper script `run-unsloth.sh` that facilitates workflow creation. Note that `run-unsloth.sh` does not build an image and instead uses a pre-existing solution offer.
+
+## run-unsloth.sh
+
+Copy SPCTL’s binary and its `config.json` to the `unsloth/scripts` directory inside the cloned Super-Protocol/solutions repository.
+
+### 1. Prepare training scripts
+
+When preparing your training scripts, keep in mind the special file structure within the TEE:
+
+| **Location**                                                  | **Purpose**                                                               | **Access** |
+| :-                                                            | :-                                                                        | :- |
+| `/sp/inputs/input-0001`<br/>`/sp/inputs/input-0002`<br/>etc.  | Possible data locations<br/> (AI model, dataset, training scripts, etc.)  | Read-only |
+| `/sp/output`                                                  | Output directory for results                                              | Read and write |
+| `/sp/certs`                                                   | Contains the order certificate, private key, and `workloadInfo`           | Read-only |
+
+Your scripts must find the data in `/sp/inputs` and write the results to `/sp/output`.
+
+### 2. Place an order
+
+2.1. Initiate a dialog to construct and place an order:
+
+```shell
+./run-unsloth.sh
+```
+
+2.2. `Enter TEE offer id (number)`: Enter a compute offer ID. This determines the available compute resources and cost of your order. You can find the full list of available compute offers on the [Marketplace](https://marketplace.superprotocol.com/).
+
+2.3. `Choose run mode`: `1) file`.
+
+2.4. `Select the model option`:
+
+- `1) Medgemma 27b (offer 15900)`: Select this option if you need an untuned MedGemma 27B.
+- `2) your model`: Select this option to use another model. Further, when prompted about `Model input`, enter one of the following:
+    - a path to the model's resource JSON file, if it was already uploaded with SPCTL
+    - model offer ID, if the model exists on the Marketplace
+    - a path to the local directory with the model to upload it using SPCTL.
+- `3) no model`: No model will be used.
+
+2.5. `Enter path to a .py/.ipynb file OR a directory`: Enter the path to your training script (file or directory). For a directory, select the file to run (entrypoint) when prompted. Note that you cannot reuse resource files in this step; scripts should be uploaded every time.
+
+2.6. `Provide your dataset as a resource JSON path, numeric offer id, or folder path`: As with the model, enter one of the following:
+
+- a path to the dataset's resource JSON file, if it was already uploaded with SPCTL
+- dataset offer ID, if the dataset exists on the Marketplace
+- a path to the local directory with the dataset to upload it using SPCTL.
+
+2.7. `Upload SPCTL config file as a resource?`: Answer `N` unless you need to use SPCTL from within the TEE during the order execution. In this case, your script should run a `curl` command to download SPCTL and find the uploaded `config.json` in the `/sp/inputs/` subdirectories.
+
+2.8. Wait for the order to be created and find the order ID in the output, for example:
+
+```shell
+Unsloth order id: 259126
+Done.
+```
+
+### 3. Check the order result
+
+3.1. The order will take some time to complete. Check the order status:
+
+```shell
+./spctl orders get <ORDER_ID>
+```
+
+Replace `<ORDER_ID>` with your order ID.
+
+If you lost the order ID, check all your orders to find it:
+
+```shell
+./spctl orders list --my-account --type tee
+```
+
+3.2. When the order status is `Done` or `Error`, download the result:
+
+```shell
+./spctl orders download-result <ORDER_ID>
+```
+
+The downloaded TAR.GZ archive contains the results in the `output` directory and execution logs.
+
+## Dry run
+
+```shell
+./run-unsloth.sh --suggest-only
+```
+
+The option `--suggest-only` allows you to perform a dry run without actually uploading files and creating orders.
+
+Complete the dialog, as usual; only use absolute paths.
+
+In the output, you will see a prepared command for running the script non-interactively, allowing you to easily modify the variables and avoid re-entering the dialog. For example:
+
+```shell
+RUN_MODE=file \
+RUN_DIR=/home/user/Downloads/yma-run \
+RUN_FILE=sft_example.py \
+DATA_RESOURCE=/home/user/unsloth/scripts/yma_data_example-data.json \
+MODEL_RESOURCE=/home/user/unsloth/scripts/medgemma-27b-ft-merged.resource.json \
+/home/user/unsloth/scripts/run-unsloth.sh \
+--tee 8 \
+--config ./config.json
+```
+
+## Jupyter Notebook
+
+You can launch and use Jupyter Notebook instead of uploading training scripts directly.
+
+Initiate a dialog:
+
+```shell
+./run-unsloth.sh
+```
+
+When prompted:
+
+1. `Enter TEE offer id`: Enter a compute offer ID. This determines the available compute resources and cost of your order. You can find the full list of available compute offers on the [Marketplace](https://marketplace.superprotocol.com/). 
+
+2. `Choose run mode`: `2) jupyter-server`.
+
+3. `Select the model option`:
+
+- `1) Medgemma 27b (offer 15900)`: Select this option if you need an untuned MedGemma 27B.
+- `2) your model`: Select this option to use another model. Further, when prompted about `Model input`, enter one of the following:
+    - a path to the model's resource JSON file, if it was already uploaded with SPCTL
+    - model offer ID, if the model exists on the Marketplace
+    - a path to the local directory with the model to upload it using SPCTL.
+- `3) no model`: No model will be used.
+
+4. `Enter Jupyter password` or press Enter to proceed without a password.
+
+5. `Select domain option`:
+
+- `1) Temporary Domain (*.superprotocol.io)` is suitable for testing and quick deployments.
+- `2) Own domain` will require you to provide a domain name, TLS certificate, private key, and a tunnel server auth token.
+
+Wait for the Tunnels Launcher order to be created.
+
+6. `Provide your dataset as a resource JSON path, numeric offer id, or folder path`: As with the model, enter one of the following:
+- a path to the dataset's resource JSON file, if it was already uploaded with SPCTL
+- dataset offer ID, if the dataset exists on the Marketplace
+- a path to the local directory with the dataset to upload it using SPCTL.
+
+7. `Upload SPCTL config file as a resource?`: Answer `N` unless you need to use SPCTL from within the TEE during the order execution. In this case, your script should run a `curl` command to download SPCTL and find the uploaded `config.json` in the `/sp/inputs/` subdirectories.
+
+8. Wait for the Jupyter order to be ready and find a link in the output; for example:
+
+```shell
+===================================================
+Jupyter instance is available at: https://beja-bine-envy.superprotocol.io
+===================================================
+```
+
+8. Open the link in your browser to access Jupyter’s UI.
+
+**Note**:
+
+The data in `/sp/output` will not be published as the order result when running the Jupyter server. To save your fine-tuning results, upload them either:
+- via Python code
+- using the integrated terminal in the Jupyter server
+- using SPCTL with the config uploaded at Step 7.
+
+## Support
+
+If you have any issues or questions, contact Super Protocol on [Discord](https://discord.gg/superprotocol) or via the [contact form](https://superprotocol.zendesk.com/hc/en-us/requests/new).

docs/cli/Guides/Solutions/vllm.md

@@ -0,0 +1,101 @@
+---
+id: "vllm"
+title: "vLLM"
+slug: "/guides/solutions/vllm"
+sidebar_position: 4
+---
+
+This guide provides step-by-step instructions for running an AI model inference using the Super Protocol packaging of [vLLM](https://www.vllm.ai/), an inference and serving engine for LLMs.
+
+The <a id="solution"><span className="dashed-underline">solution</span></a> allows you to run LLM inference within Super Protocol's Trusted Execution Environment (TEE).
+
+## Prerequisites
+
+- [SPCTL](https://docs.develop.superprotocol.com/cli/)
+- Git
+- BNB and SPPI tokens (opBNB) to pay for transactions and orders
+
+## Repository
+
+Clone the repository with Super Protocol solutions:
+
+```shell
+git clone https://github.com/Super-Protocol/solutions.git
+```
+
+The vLLM solution includes a Dockerfile and a helper script `run-vllm.sh` that facilitates workflow creation. Note that `run-vllm.sh` does not build an image and instead uses a pre-existing solution offer.
+
+## run-vllm.sh
+
+Copy SPCTL’s binary and its `config.json` to the `vllm/scripts` directory inside the cloned Super-Protocol/solutions repository.
+
+### Place an order
+
+1. Initiate a dialog to construct and place an order:
+
+```shell
+./run-vllm.sh
+```
+
+2. `Select domain option`:
+
+- `1) Temporary Domain (*.superprotocol.io)` is suitable for testing and quick deployments.
+- `2) Own domain` will require you to provide a domain name, TLS certificate, private key, and a tunnel server auth token.
+
+3. `Enter TEE offer id`: Enter a compute offer ID. This determines the available compute resources and cost of your order. You can find the full list of available compute offers on the [Marketplace](https://marketplace.superprotocol.com/).
+
+4. `Provide model as resource JSON path, numeric offer id, or folder path`: Enter one of the following:
+
+- a path to the model's resource JSON file, if it was already uploaded with SPCTL
+- model offer ID, if the model exists on the Marketplace
+- a path to the local directory with the model to upload it using SPCTL.
+
+5. `Enter API key` or press `Enter` to generate one automatically.
+
+Wait for the deployment to be ready and find the information about it in the output, for example:
+
+```shell
+===================================================
+VLLM server is available at: https://whau-trug-nail.superprotocol.io
+API key: d75c577d-e538-4d09-8f59-a0f00ae961a3
+Order IDs: Launcher=269042, VLLM=269044
+===================================================
+```
+
+### API
+
+Once deployed on Super Protocol, your model runs inside a TEE and exposes an OpenAI-compatible API. You can interact with it as you would with a local vLLM instance.
+
+Depending on the type of request you want to make, use the following API endpoints:
+
+- Chat Completions (`/v1/chat/completions`)
+- Text Completions (`/v1/completions`)
+- Embeddings (`/v1/embeddings`)
+- Audio Transcriptions & Translations (`/v1/audio/transcriptions`, `/v1/audio/translations`)
+
+See the [full list of API endpoints](https://docs.vllm.ai/en/latest/serving/openai_compatible_server/).
+
+## Dry run
+
+```shell
+./run-vllm.sh --suggest-only
+```
+
+The option `--suggest-only` allows you to perform a dry run without actually uploading files and creating orders.
+
+Complete the dialog, as usual; only use absolute paths.
+
+In the output, you will see a prepared command for running the script non-interactively, allowing you to easily modify the variables and avoid re-entering the dialog. For example:
+
+```shell
+RUN_MODE=temporary \
+MODEL_RESOURCE=55 \
+VLLM_API_KEY=9c6dbf44-cef7-43a4-b362-43295b244446 \
+/home/user/vllm/scripts/run-vllm.sh \
+--config ./config.json \
+--tee 8
+```
+
+## Support
+
+If you have any issues or questions, contact Super Protocol on [Discord](https://discord.gg/superprotocol) or via the [contact form](https://superprotocol.zendesk.com/hc/en-us/requests/new).