diff --git a/README.md b/README.md index c60cfc1..d8cfe18 100644 --- a/README.md +++ b/README.md @@ -24,10 +24,12 @@ You can find the Gemma models on GitHub, Hugging Face models, Kaggle, Google Clo | LangChain | This [tutorial](partner-quickstarts/gemma-langchain.ipynb) shows you how to get started with Gemma and LangChain, running in Google Cloud or in your Colab environment. | | MongoDB | This [article](partner-quickstarts/rag_with_hugging_face_gemma_mongodb.ipynb) presents how to leverage Gemma as the foundation model in a retrieval-augmented generation pipeline or system. | -## Workshops -| Notebook | Description | -| --------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- | -| [Workshop_How_to_Fine_tuning_Gemma.ipynb](Workshops/Workshop_How_to_Fine_tuning_Gemma.ipynb/Keras_Gemma_2_Quickstart.ipynb) | Recommended finetuning notebook for getting started | +## Workshops and technical talks +| Notebook | Description | +| --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | +| [Workshop_How_to_Fine_tuning_Gemma.ipynb](Workshops/Workshop_How_to_Fine_tuning_Gemma.ipynb/Keras_Gemma_2_Quickstart.ipynb) | Recommended finetuning notebook for getting started | +| [Self_extend_Gemma.ipynb](Gemma/Self_extend_Gemma.ipynb) | Self-extend context window for Gemma in the I/O 2024 [Keras talk](https://www.youtube.com/watch?v=TV7qCk1dBWA) | +| [Gemma_control_vectors.ipynb](Gemma/Gemma_control_vectors.ipynb) | Implement [control vectors](https://arxiv.org/abs/2310.01405) with Gemma in the I/O 2024 [Keras talk](https://www.youtube.com/watch?v=TV7qCk1dBWA) | ## Accompanying notebooks for the Build with AI video series | Folder | @@ -51,7 +53,6 @@ You can find the Gemma models on GitHub, Hugging Face models, Kaggle, Google Clo | [Chat_and_distributed_pirate_tuning.ipynb](Gemma/Chat_and_distributed_pirate_tuning.ipynb) | Chat with Gemma 7B and finetune it so that it generates responses in pirates' tone. | | [gemma_inference_on_tpu.ipynb](Gemma/gemma_inference_on_tpu.ipynb) | Basic inference of Gemma with JAX/Flax on TPU. | | [gemma_data_parallel_inference_in_jax_tpu.ipynb](Gemma/gemma_data_parallel_inference_in_jax_tpu.ipynb) | Parallel inference of Gemma with JAX/Flax on TPU. | -| [Gemma_control_vectors.ipynb](Gemma/Gemma_control_vectors.ipynb) | Implement [control vectors](https://arxiv.org/abs/2310.01405) with Gemma in the I/O 2024 [Keras talk](https://www.youtube.com/watch?v=TV7qCk1dBWA). | | [Gemma_Basics_with_HF.ipynb](Gemma/Gemma_Basics_with_HF.ipynb) | Load, run, finetune and deploy Gemma using [Hugging Face](https://huggingface.co/). | | [Gemma_with_Langfun_and_LlamaCpp.ipynb](Gemma/Gemma_with_Langfun_and_LlamaCpp.ipynb) | Leverage [Langfun](https://github.com/google/langfun) to seamlessly integrate natural language with programming using Gemma 2 and [LlamaCpp](https://github.com/ggerganov/llama.cpp). | | [Gemma_with_Langfun_and_LlamaCpp_Python_Bindings.ipynb](Gemma/Gemma_with_Langfun_and_LlamaCpp_Python_Bindings.ipynb) | Leverage [Langfun](https://github.com/google/langfun) for smooth language-program interaction with Gemma 2 and [llama-cpp-python](https://github.com/abetlen/llama-cpp-python). | @@ -71,8 +72,6 @@ You can find the Gemma models on GitHub, Hugging Face models, Kaggle, Google Clo | [Prompt_chaining.ipynb](Gemma/Prompt_chaining.ipynb) | Illustrate prompt chaining and iterative generation with Gemma. | | [LangChain_chaining.ipynb](Gemma/LangChain_chaining.ipynb) | Illustrate LangChain chaining with Gemma. | | [Advanced_Prompting_Techniques.ipynb](Gemma/Advanced_Prompting_Techniques.ipynb) | Illustrate advanced prompting techniques with Gemma. | -| **Long context** | | -| [Self_extend_Gemma.ipynb](Gemma/Self_extend_Gemma.ipynb) | Self-extend context window for Gemma in the I/O 2024 [Keras talk](https://www.youtube.com/watch?v=TV7qCk1dBWA). | | **RAG** | | | [RAG_with_ChromaDB.ipynb](Gemma/RAG_with_ChromaDB.ipynb) | Build a Retrieval Augmented Generation (RAG) system with Gemma using [ChromaDB](https://www.trychroma.com/) and [Hugging Face](https://huggingface.co/). | | [Minimal_RAG.ipynb](Gemma/Minimal_RAG.ipynb) | Minimal example of building a RAG system with Gemma using [Google UniSim](https://github.com/google/unisim) and [Hugging Face](https://huggingface.co/). |