From 4d2f28527e2a3d158e0b4bdf559c8d3e5b92a9f5 Mon Sep 17 00:00:00 2001 From: debrupf2946 Date: Mon, 8 Jul 2024 18:44:39 +0530 Subject: [PATCH] updated EXPERIMENTS.MD and added their artefacts and data respectively --- graph_rag/experiments/EXPERIMENTS.MD | 36 +- .../artifacts/data_keras/index1.md | 93 ++++ .../artifacts/data_keras/index2.md | 146 ++++++ .../artifacts/data_keras/index3.md | 21 + .../artifacts/data_keras/index4.md | 7 + .../artifacts/data_keras/index5.md | 7 + .../Graph_visualization_gemma2_mscb.html | 464 ++++++++++++++++++ .../artifacts/gemma2/gemma2graphIndex.pkl | Bin 0 -> 44020 bytes .../Graph_visualization_mistral_mscb.html | 369 ++++++++++++++ .../artifacts/mistral/mistralgraphIndex.pkl | Bin 0 -> 2262196 bytes .../Graph_visualization_neuralchat_mscb.html | 369 ++++++++++++++ .../graphIndex_neuralchat_mscb.pkl | Bin 0 -> 44281 bytes .../Graph_visualization_phi3-med_mscb.html | 464 ++++++++++++++++++ .../phi3-med/graphIndex_phi3_medium_mscb.pkl | Bin 0 -> 43746 bytes .../phi3/Graph_visualization_phi3_mscb.html | 369 ++++++++++++++ .../artifacts/phi3/graphIndex_phi3_mscb.pkl | Bin 0 -> 42692 bytes 16 files changed, 2332 insertions(+), 13 deletions(-) create mode 100644 graph_rag/experiments/artifacts/data_keras/index1.md create mode 100644 graph_rag/experiments/artifacts/data_keras/index2.md create mode 100644 graph_rag/experiments/artifacts/data_keras/index3.md create mode 100644 graph_rag/experiments/artifacts/data_keras/index4.md create mode 100644 graph_rag/experiments/artifacts/data_keras/index5.md create mode 100644 graph_rag/experiments/artifacts/gemma2/Graph_visualization_gemma2_mscb.html create mode 100644 graph_rag/experiments/artifacts/gemma2/gemma2graphIndex.pkl create mode 100644 graph_rag/experiments/artifacts/mistral/Graph_visualization_mistral_mscb.html create mode 100644 graph_rag/experiments/artifacts/mistral/mistralgraphIndex.pkl create mode 100644 graph_rag/experiments/artifacts/neural_chat/Graph_visualization_neuralchat_mscb.html create mode 100644 graph_rag/experiments/artifacts/neural_chat/graphIndex_neuralchat_mscb.pkl create mode 100644 graph_rag/experiments/artifacts/phi3-med/Graph_visualization_phi3-med_mscb.html create mode 100644 graph_rag/experiments/artifacts/phi3-med/graphIndex_phi3_medium_mscb.pkl create mode 100644 graph_rag/experiments/artifacts/phi3/Graph_visualization_phi3_mscb.html create mode 100644 graph_rag/experiments/artifacts/phi3/graphIndex_phi3_mscb.pkl diff --git a/graph_rag/experiments/EXPERIMENTS.MD b/graph_rag/experiments/EXPERIMENTS.MD index ec1109b..c5527ee 100644 --- a/graph_rag/experiments/EXPERIMENTS.MD +++ b/graph_rag/experiments/EXPERIMENTS.MD @@ -14,11 +14,16 @@ we tried following llms - Zypher(4-bit) - llama3(ollama-version) - codellama(ollama--version) -- cohere/aya(4-bit quantized). +- cohere/aya(4-bit quantized) +- phi3(ollama) +- phi3:medium(ollama) +- neural-chat(ollama) +- gemma2(ollama) +- mistral(ollama) ### Embeddings for embeddings we tried -- microsoft/Codebert +- microsoft/codebert-base - Salesforce/embedding ### Libraries @@ -28,14 +33,19 @@ At initial phase we are looking for libraries out there in community that solves - networkX graph builder ### Table -| Model | Embeddings | Libraries | Remarks | Documents | artifacts -|:----------------------------|:-------------------|:--------------------------|:-----------|:----------|:-------------------------------------------------------| -| gpt-4o| open-ai| Neo4jGraphBuilder | k | keras-io | | -| Gemini | gemini | llama-index graph builder | k | keras-nlp | - [viz](artifacts/vizualization/ex1.html) | -| Gemini | gemini | llama-index graph builder | Rate-error | keras-io | | -| Gemini | microsoft/Codebert | llama-index graph builder | k | keras-nlp | - [viz](artifacts/vizualization/gem_mcode_k_nlp.html) | -| Zypher(4-bit) | microsoft/Codebert | llama-index graph builder | k | keras-nlp | -[viz](artifacts/vizualization/zy_knlp.html) | -| Zypher(4-bit) | microsoft/Codebert | llama-index graph builder | k | keras-io | -[viz](artifacts/vizualization/examp.html) | -| llama3(ollama-version) | microsoft/Codebert | llama-index graph builder | k | keras-nlp | -[viz](artifacts/vizualization/Graph_visualization.html) -| codellama(ollama--version) | microsoft/Codebert | llama-index graph builder | k | keras-nlp | -[viz](artifacts/vizualization/code_1.html) -| gpt-neo-2.7B-4bit-quantized | microsoft/Codebert | llama-index graph builder | k | keras-nlp | [viz](artifacts/vizualization/graph_gpt3-neo.html) \ No newline at end of file +| Model | Embeddings | Libraries | Remarks | Documents | artifacts +|:----------------------------|:-------------------|:--------------------------|:-----------|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------| +| gemma2(ollama) | microsoft/codebert-base | llama-index graph builder | nil | [data_keras](artifacts/data_keras) | -[viz](artifacts/gemma2/Graph_visualization_gemma2_mscb.html)
[index](artifacts/gemma2/gemma2graphIndex.pkl) | +| mistral(ollama) | microsoft/codebert-base | llama-index graph builder | nil | [data_keras](artifacts/data_keras) | -[viz](artifacts/mistral/Graph_visualization_mistral_mscb.html)
[index](artifacts/mistral/mistralgraphIndex.pkl) | +| neural-chat(ollama) | microsoft/codebert-base | llama-index graph builder | nil | [data_keras](artifacts/data_keras) | -[viz](artifacts/neural_chat/Graph_visualization_neuralchat_mscb.html)
[index](artifacts/neural_chat/graphIndex_neuralchat_mscb.pkl) | +| phi3:medium(ollama) | microsoft/codebert-base | llama-index graph builder | nil | [data_keras](artifacts/data_keras) | -[viz](artifacts/phi3-med/Graph_visualization_phi3-med_mscb.html)
[index](artifacts/phi3-med/graphIndex_phi3_medium_mscb.pkl) | +| phi3(ollama) | microsoft/codebert-base | llama-index graph builder | nil | [data_keras](artifacts/data_keras) | -[viz](artifacts/phi3/Graph_visualization_phi3_mscb.html)
[index](artifacts/phi3/graphIndex_phi3_mscb.pkl) | +| gpt-4o| open-ai| Neo4jGraphBuilder | k | keras-io | | +| Gemini | gemini | llama-index graph builder | k | keras-nlp | - [viz](artifacts/vizualization/ex1.html) | +| Gemini | gemini | llama-index graph builder | Rate-error | keras-io | | +| Gemini | microsoft/codebert-base | llama-index graph builder | k | keras-nlp | - [viz](artifacts/vizualization/gem_mcode_k_nlp.html) | +| Zypher(4-bit) | microsoft/codebert-base | llama-index graph builder | k | keras-nlp | -[viz](artifacts/vizualization/zy_knlp.html) | +| Zypher(4-bit) | microsoft/codebert-base | llama-index graph builder | k | keras-io | -[viz](artifacts/vizualization/examp.html) | +| llama3(ollama-version) | microsoft/codebert-base | llama-index graph builder | k | keras-nlp | -[viz](artifacts/vizualization/Graph_visualization.html) +| codellama(ollama--version) | microsoft/codebert-base | llama-index graph builder | k | keras-nlp | -[viz](artifacts/vizualization/code_1.html) +| gpt-neo-2.7B-4bit-quantized | microsoft/codebert-base | llama-index graph builder | k | keras-nlp | [viz](artifacts/vizualization/graph_gpt3-neo.html) \ No newline at end of file diff --git a/graph_rag/experiments/artifacts/data_keras/index1.md b/graph_rag/experiments/artifacts/data_keras/index1.md new file mode 100644 index 0000000..e0b831d --- /dev/null +++ b/graph_rag/experiments/artifacts/data_keras/index1.md @@ -0,0 +1,93 @@ +# KerasTuner + +Star + +KerasTuner is an easy-to-use, scalable hyperparameter optimization framework +that solves the pain points of hyperparameter search. Easily configure your +search space with a define-by-run syntax, then leverage one of the available +search algorithms to find the best hyperparameter values for your models. +KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms +built-in, and is also designed to be easy for researchers to extend in order to +experiment with new search algorithms. + +--- +## Quick links + +* [Getting started with KerasTuner](/guides/keras_tuner/getting_started/) +* [KerasTuner developer guides](/guides/keras_tuner/) +* [KerasTuner API reference](/api/keras_tuner/) +* [KerasTuner on GitHub](https://github.com/keras-team/keras-tuner) + + +--- +## Installation + +Install the latest release: + +``` +pip install keras-tuner --upgrade +``` + +You can also check out other versions in our +[GitHub repository](https://github.com/keras-team/keras-tuner). + + +--- +## Quick introduction + +Import KerasTuner and TensorFlow: + +```python +import keras_tuner +import keras +``` + +Write a function that creates and returns a Keras model. +Use the `hp` argument to define the hyperparameters during model creation. + +```python +def build_model(hp): + model = keras.Sequential() + model.add(keras.layers.Dense( + hp.Choice('units', [8, 16, 32]), + activation='relu')) + model.add(keras.layers.Dense(1, activation='relu')) + model.compile(loss='mse') + return model +``` + +Initialize a tuner (here, `RandomSearch`). +We use `objective` to specify the objective to select the best models, +and we use `max_trials` to specify the number of different models to try. + +```python +tuner = keras_tuner.RandomSearch( + build_model, + objective='val_loss', + max_trials=5) +``` + +Start the search and get the best model: + +```python +tuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val)) +best_model = tuner.get_best_models()[0] +``` + +To learn more about KerasTuner, check out [this starter guide](https://keras.io/guides/keras_tuner/getting_started/). + + +--- +## Citing KerasTuner + +If KerasTuner helps your research, we appreciate your citations. +Here is the BibTeX entry: + +```bibtex +@misc{omalley2019kerastuner, + title = {KerasTuner}, + author = {O'Malley, Tom and Bursztein, Elie and Long, James and Chollet, Fran\c{c}ois and Jin, Haifeng and Invernizzi, Luca and others}, + year = 2019, + howpublished = {\url{https://github.com/keras-team/keras-tuner}} +} +``` diff --git a/graph_rag/experiments/artifacts/data_keras/index2.md b/graph_rag/experiments/artifacts/data_keras/index2.md new file mode 100644 index 0000000..58f6539 --- /dev/null +++ b/graph_rag/experiments/artifacts/data_keras/index2.md @@ -0,0 +1,146 @@ +# KerasNLP + +Star + +KerasNLP is a natural language processing library that works natively +with TensorFlow, JAX, or PyTorch. Built on Keras 3, these models, layers, +metrics, and tokenizers can be trained and serialized in any framework and +re-used in another without costly migrations. + +KerasNLP supports users through their entire development cycle. Our workflows +are built from modular components that have state-of-the-art preset weights when +used out-of-the-box and are easily customizable when more control is needed. + +This library is an extension of the core Keras API; all high-level modules are +[`Layers`](/api/layers/) or +[`Models`](/api/models/) that receive that same level of polish +as core Keras. If you are familiar with Keras, congratulations! You already +understand most of KerasNLP. + +See our [Getting Started guide](/guides/keras_nlp/getting_started) +to start learning our API. We welcome +[contributions](https://github.com/keras-team/keras-nlp/blob/master/CONTRIBUTING.md). + +--- +## Quick links + +* [KerasNLP API reference](/api/keras_nlp/) +* [KerasNLP on GitHub](https://github.com/keras-team/keras-nlp) +* [List of available pre-trained models](/api/keras_nlp/models/) + +## Guides + +* [Getting Started with KerasNLP](/guides/keras_nlp/getting_started/) +* [Uploading Models with KerasNLP](/guides/keras_nlp/upload/) +* [Pretraining a Transformer from scratch](/guides/keras_nlp/transformer_pretraining/) + +## Examples + +* [GPT-2 text generation](/examples/generative/gpt2_text_generation_with_kerasnlp/) +* [Parameter-efficient fine-tuning of GPT-2 with LoRA](/examples/nlp/parameter_efficient_finetuning_of_gpt2_with_lora/) +* [Semantic Similarity](/examples/nlp/semantic_similarity_with_keras_nlp/) +* [Sentence embeddings using Siamese RoBERTa-networks](/examples/nlp/sentence_embeddings_with_sbert/) +* [Data Parallel Training with tf.distribute](/examples/nlp/data_parallel_training_with_keras_nlp/) +* [English-to-Spanish translation](/examples/nlp/neural_machine_translation_with_keras_nlp/) +* [GPT text generation from scratch](/examples/generative/text_generation_gpt/) +* [Text Classification using FNet](/examples/nlp/fnet_classification_with_keras_nlp/) + +--- +## Installation + +KerasNLP supports both Keras 2 and Keras 3. We recommend Keras 3 for all new +users, as it enables using KerasNLP models and layers with JAX, TensorFlow and +PyTorch. + +### Keras 2 Installation + +To install the latest KerasNLP release with Keras 2, simply run: + +``` +pip install --upgrade keras-nlp +``` + +### Keras 3 Installation + +There are currently two ways to install Keras 3 with KerasNLP. To install the +stable versions of KerasNLP and Keras 3, you should install Keras 3 **after** +installing KerasNLP. This is a temporary step while TensorFlow is pinned to +Keras 2, and will no longer be necessary after TensorFlow 2.16. + +``` +pip install --upgrade keras-nlp +pip install --upgrade keras +``` + +To install the latest nightly changes for both KerasNLP and Keras, you can use +our nightly package. + +``` +pip install --upgrade keras-nlp-nightly +``` + +**Note:** Keras 3 will not function with TensorFlow 2.14 or earlier. + +See [Getting started with Keras](/getting_started/) for more information on +installing Keras generally and compatibility with different frameworks. + +--- +## Quickstart + +Fine-tune BERT on a small sentiment analysis task using the +[`keras_nlp.models`](/api/keras_nlp/models/) API: + +```python +import os +os.environ["KERAS_BACKEND"] = "tensorflow" # Or "jax" or "torch"! + +import keras_nlp +import tensorflow_datasets as tfds + +imdb_train, imdb_test = tfds.load( + "imdb_reviews", + split=["train", "test"], + as_supervised=True, + batch_size=16, +) +# Load a BERT model. +classifier = keras_nlp.models.BertClassifier.from_preset( + "bert_base_en_uncased", + num_classes=2, +) +# Fine-tune on IMDb movie reviews. +classifier.fit(imdb_train, validation_data=imdb_test) +# Predict two new examples. +classifier.predict(["What an amazing movie!", "A total waste of my time."]) +``` + +--- +## Compatibility + +We follow [Semantic Versioning](https://semver.org/), and plan to +provide backwards compatibility guarantees both for code and saved models built +with our components. While we continue with pre-release `0.y.z` development, we +may break compatibility at any time and APIs should not be consider stable. + +## Disclaimer + +KerasNLP provides access to pre-trained models via the `keras_nlp.models` API. +These pre-trained models are provided on an "as is" basis, without warranties +or conditions of any kind. The following underlying models are provided by third +parties, and subject to separate licenses: +BART, DeBERTa, DistilBERT, GPT-2, OPT, RoBERTa, Whisper, and XLM-RoBERTa. + +## Citing KerasNLP + +If KerasNLP helps your research, we appreciate your citations. +Here is the BibTeX entry: + +```bibtex +@misc{kerasnlp2022, + title={KerasNLP}, + author={Watson, Matthew, and Qian, Chen, and Bischof, Jonathan and Chollet, + Fran\c{c}ois and others}, + year={2022}, + howpublished={\url{https://github.com/keras-team/keras-nlp}}, +} +``` diff --git a/graph_rag/experiments/artifacts/data_keras/index3.md b/graph_rag/experiments/artifacts/data_keras/index3.md new file mode 100644 index 0000000..3cd2b39 --- /dev/null +++ b/graph_rag/experiments/artifacts/data_keras/index3.md @@ -0,0 +1,21 @@ +# KerasCV Bounding Boxes + +All KerasCV components that process bounding boxes require a `bounding_box_format` +argument. This argument allows you to seamlessly integrate KerasCV components into +your own workflows while preserving proper behavior of the components themselves. + +Bounding boxes are represented by dictionaries with two keys: `'boxes'` and `'classes'`: + +``` +{ + 'boxes': [batch, num_boxes, 4], + 'classes': [batch, num_boxes] +} +``` + +To ensure your bounding boxes comply with the KerasCV specification, you can use [`keras_cv.bounding_box.validate_format(boxes)`](https://github.com/keras-team/keras-cv/blob/master/keras_cv/bounding_box/validate_format.py). + +The bounding box formats supported in KerasCV +[are listed in the API docs](/api/keras_cv/bounding_box/formats) +If a format you would like to use is missing, +[feel free to open a GitHub issue on KerasCV](https://github.com/keras-team/keras-cv/issues)! diff --git a/graph_rag/experiments/artifacts/data_keras/index4.md b/graph_rag/experiments/artifacts/data_keras/index4.md new file mode 100644 index 0000000..ff1ab8f --- /dev/null +++ b/graph_rag/experiments/artifacts/data_keras/index4.md @@ -0,0 +1,7 @@ +# KerasCV + +These guides cover the [KerasCV](/keras_cv/) library. + +## Available guides + +{{toc}} diff --git a/graph_rag/experiments/artifacts/data_keras/index5.md b/graph_rag/experiments/artifacts/data_keras/index5.md new file mode 100644 index 0000000..f174b0d --- /dev/null +++ b/graph_rag/experiments/artifacts/data_keras/index5.md @@ -0,0 +1,7 @@ +# KerasNLP + +These guides cover the [KerasNLP](/keras_nlp/) library. + +## Available guides + +{{toc}} diff --git a/graph_rag/experiments/artifacts/gemma2/Graph_visualization_gemma2_mscb.html b/graph_rag/experiments/artifacts/gemma2/Graph_visualization_gemma2_mscb.html new file mode 100644 index 0000000..2cbf83f --- /dev/null +++ b/graph_rag/experiments/artifacts/gemma2/Graph_visualization_gemma2_mscb.html @@ -0,0 +1,464 @@ + + + + + + + + + +
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