From 39de59d00b419680fd951325ef4489b655259a41 Mon Sep 17 00:00:00 2001 From: Qiu Qin Date: Mon, 28 Oct 2024 13:41:45 -0400 Subject: [PATCH 1/3] Update AQUA model registration tips to show screenshots for registering model from HuggingFace. --- ai-quick-actions/register-tips.md | 22 ++++++++++++++---- .../web_assets/register-button.png | Bin 0 -> 805233 bytes .../web_assets/register-model.png | Bin 0 -> 387376 bytes 3 files changed, 17 insertions(+), 5 deletions(-) create mode 100644 ai-quick-actions/web_assets/register-button.png create mode 100644 ai-quick-actions/web_assets/register-model.png diff --git a/ai-quick-actions/register-tips.md b/ai-quick-actions/register-tips.md index f0a3f520..040be511 100644 --- a/ai-quick-actions/register-tips.md +++ b/ai-quick-actions/register-tips.md @@ -9,13 +9,25 @@ Table of Contents: - [Model Evaluation](evaluation-tips.md) - [Model Fine Tuning](fine-tuning-tips.md) -## Upload model artifact to Object Storage +The AI Quick Action model explorer allows you to register a model from Hugging Face or Object Storage with a few clicks. -AI Quick Actions supports user-provided models that can be deployed, fined-tuned and evaluated. You can now upload -and test models with artifacts downloaded from model repositories like Hugging Face, etc. or from your own models. +![Register new model](web_assets/register-button.png) -While registering the model in AI Quick Actions, you need to specify the Object Storage location where the model artifact is stored. -If you are downloading the model from the Hugging Face Hub, follow the download instructions [here](https://huggingface.co/docs/huggingface_hub/main/en/guides/download). +## Register Model from Hugging Face + +To register a model from Hugging Face, select "Download from Hugging Face" in the dropdown under model artifact. Then you may select a verified model from the "Select Model" dropdown, or you may "register any model" by entering the model name. + +Note that for gated models, please authenticate to Hugging Face by running `huggingface-cli login` command in terminal. See details in [Hugging Face CLI documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli). + +![Register model from Hugging Face](web_assets/register-model.png) + +## Upload Model Artifact to Object Storage Manually + +AI Quick Actions also supports user-provided models that can be deployed, fined-tuned and evaluated. + +While registering the model in AI Quick Actions, you need to specify the Object Storage location where the model artifact is stored. +You may first prepare your model files locally and then upload it to object storage. +For example, you can download the model from the Hugging Face Hub using the download instructions [here](https://huggingface.co/docs/huggingface_hub/main/en/guides/download). Once downloaded, use [oci-cli](https://github.com/oracle/oci-cli) to upload these artifacts to the correct object storage location. The object storage bucket needs to be versioned, run the following command to check whether versioning is set up. 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.../deploy-model-advanced-options.png | Bin 0 -> 286086 bytes ai-quick-actions/web_assets/deploy-model.png | Bin 226144 -> 195220 bytes 3 files changed, 26 insertions(+), 15 deletions(-) create mode 100644 ai-quick-actions/web_assets/deploy-model-advanced-options.png diff --git a/ai-quick-actions/model-deployment-tips.md b/ai-quick-actions/model-deployment-tips.md index 88a54c15..362a286a 100644 --- a/ai-quick-actions/model-deployment-tips.md +++ b/ai-quick-actions/model-deployment-tips.md @@ -20,13 +20,15 @@ replacement for applications using OpenAI API. Model deployments are a managed r the OCI Data Science service. For more details about Model Deployment and managing it through the OCI console please see the [OCI docs](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). -### Deploying an LLM +## Deploying an LLM After picking a model from the model explorer, if the "Deploy Model" is enabled you can use this form to quickly deploy the model: ![Deploy Model](web_assets/deploy-model.png) +### Compute Shape + The compute shape selection is critical, the list available is selected to be suitable for the chosen model. @@ -40,16 +42,13 @@ For a full list of shapes and their definitions see the [compute shape docs](htt The relationship between model parameter size and GPU memory is roughly 2x parameter count in GB, so for example a model that has 7B parameters will need a minimum of 14 GB for inference. At runtime the memory is used for both holding the weights, along with the concurrent contexts for the user's requests. -The "inference mode" allows you to choose between the default completion endpoint(`/v1/completions`) and the chat endpoint (`/v1/chat/completions`). -* The default completion endpoint is designed for text completion tasks. It’s suitable for generating text based on a given prompt. -* The chat endpoint is tailored for chatbot-like interactions. It allows for more dynamic and interactive conversations by using a list of messages with roles (system, user, assistant). This is ideal for applications requiring back-and-forth dialogue, maintaining context over multiple turns. It is recommended that you deploy chat models (e.g. `meta-llama/Llama-3.1-8B-Instruct`) using the chat endpoint. +### Advanced Options -Once deployed, the model will spin up and become available after some time, then you're able to try out the model -from the deployments tab using the test model, or programmatically. +You may click on the "Show Advanced Options" to configure options for "inference container" and "inference mode". -![Try Model](web_assets/try-model.png) +![Advanced Options](web_assets/deploy-model-advanced-options.png) -### Advanced Deployment Options +### Inference Container Configuration The service allows for model deployment configuration to be overridden when creating a model deployment. Depending on the type of inference container used for deployment, i.e. vLLM or TGI, the parameters vary and need to be passed with the format @@ -58,12 +57,24 @@ the type of inference container used for deployment, i.e. vLLM or TGI, the param For more details, please visit [vLLM](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#command-line-arguments-for-the-server) or [TGI](https://huggingface.co/docs/text-generation-inference/en/basic_tutorials/launcher) documentation to know more about the parameters accepted by the respective containers. -![Model Deployment Parameters](web_assets/model-deployment-params.png) +### Inference Mode + +The "inference mode" allows you to choose between the default completion endpoint(`/v1/completions`) and the chat endpoint (`/v1/chat/completions`). +* The default completion endpoint is designed for text completion tasks. It’s suitable for generating text based on a given prompt. +* The chat endpoint is tailored for chatbot-like interactions. It allows for more dynamic and interactive conversations by using a list of messages with roles (system, user, assistant). This is ideal for applications requiring back-and-forth dialogue, maintaining context over multiple turns. It is recommended that you deploy chat models (e.g. `meta-llama/Llama-3.1-8B-Instruct`) using the chat endpoint. + + +### Test Your Model + +Once deployed, the model will spin up and become available after some time, then you're able to try out the model +from the deployments tab using the test model, or programmatically. + +![Try Model](web_assets/try-model.png) -### Inferencing Model +## Inferencing Model -#### Using oci-cli +### Using oci-cli ```bash oci raw-request --http-method POST --target-uri /predict --request-body '{ @@ -78,7 +89,7 @@ oci raw-request --http-method POST --target-uri /predict - Note: Currently `oci-cli` does not support streaming response, use Python or Java SDK instead. -#### Using Python SDK (without streaming) +### Using Python SDK (without streaming) ```python # The OCI SDK must be installed for this example to function properly. @@ -120,7 +131,7 @@ res = requests.post(endpoint, json=body, auth=auth, headers={}).json() print(res) ``` -#### Using Python SDK (with streaming) +### Using Python SDK (with streaming) To consume streaming Server-sent Events (SSE), install [sseclient-py](https://pypi.org/project/sseclient-py/) using `pip install sseclient-py`. @@ -176,7 +187,7 @@ for event in client.events(): # print(line) ``` -#### Using Java (with streaming) +### Using Java (with streaming) ```java /** @@ -305,7 +316,7 @@ public class RestExample { ``` -### Troubleshooting +## Troubleshooting If the model should fail to deploy, reasons might include lack of GPU availability, or policy permissions. diff --git a/ai-quick-actions/web_assets/deploy-model-advanced-options.png b/ai-quick-actions/web_assets/deploy-model-advanced-options.png new file mode 100644 index 0000000000000000000000000000000000000000..7e7feef1a00350d5f6d62a5c677b0a512c7806ad GIT binary patch literal 286086 zcmeFZcT^K=*Eb9z7Ced-l&W$B=^{Nqf(@iAAiXF^2~}!4eLJ2QaK%&r~4k*|+kq<}BZ08`!b9QkcR)4)H8E>?Q3Mn)VPCw6gc z=M>=J-naz#fH?*K+SlPc&B68a_%;rXXeh__KdvzbzBm3J1D}mCzkhFkdXHl#@ZUk; 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Configuration Update Options The available shapes for models in AI Quick Actions are pre-configured for both registration and deployment for models available in the Model Explorer. However, if you need to add more shapes to the list of