From 2624905bcf984910c4d6a42dc714c9c00daea8fa Mon Sep 17 00:00:00 2001 From: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 9 Aug 2023 10:01:02 +0300 Subject: [PATCH] Update README.md replaced former model `gpt2` with the existing one in the demo - `falcon-7b` --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 79698ef..89ea4fb 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ huggingface-mlrun -This demo demonstrates how to fine tune a LLM and build an ML application: the **MLOps master bot**! We'll train [`gpt2-medium`](https://huggingface.co/gpt2) on [**Iguazio**'s MLOps blogs](https://www.iguazio.com/blog/) and cover how easy it is to take a model and code from development to production. Even if its a big scary LLM model, MLRun will take care of the dirty work! +This demo demonstrates how to fine tune a LLM and build an ML application: the **MLOps master bot**! We'll train [`falcon-7b`](https://huggingface.co/tiiuae/falcon-7b) on [**Iguazio**'s MLOps blogs](https://www.iguazio.com/blog/) and cover how easy it is to take a model and code from development to production. Even if its a big scary LLM model, MLRun will take care of the dirty work! We will use: * [**HuggingFace**](https://huggingface.co/) - as the main machine learning framework to get the model and tokenizer. @@ -11,7 +11,7 @@ We will use: The demo contains a single [notebook](./tutorial.ipynb) that covers the two main stages in every MLOps project: -* **Training Pipeline Automation** - Demonstrating how to get an existing model (`GPT2-Medium`) from HuggingFace's Transformers package and operationalize it through all of its life cycle phases: data collection, data ppreparation, training and evaluation, as a fully automated pipeline. +* **Training Pipeline Automation** - Demonstrating how to get an existing model (`falcon-7b`) from HuggingFace's Transformers package and operationalize it through all of its life cycle phases: data collection, data ppreparation, training and evaluation, as a fully automated pipeline. * **Application Serving Pipeline** - Showing how to productize the newly trained LLM as a serverless function. You can find all the python source code under [/src](./src) @@ -64,4 +64,4 @@ Your environment should include `MLRUN_ENV_FILE= Note: You can also use a remote MLRun service (over Kubernetes), instead of starting a local mlrun, -> edit the [mlrun.env](./mlrun.env) and specify its address and credentials \ No newline at end of file +> edit the [mlrun.env](./mlrun.env) and specify its address and credentials