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image:: https://python-gsoc.org/logos/fury_logo.png + :width: 40 + :target: https://fury.gl/latest/index.html + + + +Google Summer of Code Final Work Product +======================================== + +.. post:: August 21 2024 + :author: Robin Roy + :tags: google + :category: gsoc + +- **Name:** Robin Roy +- **Organization:** Python Software Foundation +- **Sub-Organization:** FURY +- **Project:** `Improving Community Engagement: AI communication automation using LLM `_ + + +Abstract +-------- + +This project had the goal to implement a `Large Language Model(LLM) `_ chatbot which understands the FURY repository. The purpose of the project is to reduce the barrier of entry to scientific visualization. `Retrieval Augmented Generation(RAG) `_ was used to get necessary context for every user query. Multiple variations were explored, including Fine-Tuning models, mixing Fine-Tuning and RAG and RAG alone. Multiple `chunking strategies `_ were also explored for data collection and storage. The models are served to the user through a Discord Bot and a GitHub App. All the API endpoints are hosted using `HuggingFace Spaces `_. `Pinecone `_ was used as the database for storing embeddings. Benchmarking, data collection, and testing were done on `another repository `_. + + +Proposed Objectives +------------------- + +The objectives of the GSoC project could be broadly classified as: + +- **Figuring out hosting.** + We had a constraint on hosting to try and minimize the cost. We managed to complete the whole project with 100% free hosting. Work here included: + * Experiments with `Google Colab `_ notebook hosting. + * Experiments with `Kaggle `_ notebook hosting. + * Experiments with `HuggingFace `_ spaces hosting. + +- **Choosing the technologies to use.** + Work here included: + * Experiments with local `GGUF (GPT-Generated Unified Format)`_ models. + * Experiments with different quantizations. + * Experiments with `Ollama. `_. + * Experiements with `LlamaCPP. `_ + * Experiments with `Groq `_. + * Experiments with `Google Gemini `_. + +- **Work on the backend architecture.** + Backend architecture was heavily influenced by HuggingFace and its limitations. Work here included: + * Choosing the API architecture. + * Integrating different models. + * Improving concurrent requests support. + * Improving the UX of the endpoints. + +- **Work on improving model accuracy.** + This was a recurring work and kept happening on most weeks. It included: + * Model Benchmarking + * Data Collection + * Experiments on Retrieval Augmented Generation. + * Experiments on Fine-Tuning. + * Experiments on Chunking. + * Experiments on Retrieval quantity. + +- **Discord Bot integration.** + The work included: + * Building the Discord Bot. + * Improving the UX of the bot. + * Improving performance of the bot. + +- **GitHub App integration.** + The work included: + * Building the GitHub App integration. + * Improving the UX of the integration. + +Objectives Completed +-------------------- + +- **Figuring out hosting.** + + As mentioned, we had a constraint on the cost. So we searched for how to host the application for free. This took us to explore interesting directions like Google Colab and Kaggle Notebooks. In the end, HuggingFace was decided to be the best place. Everything is containerized and currently hosted on HuggingFace. + + This also meant that all the upcoming design/architectural choices will have to be based on HuggingFace. This will have some weird quirks at some places but overall HuggingFace was an excellent choice for the job. + + A very detailed blog on hosting is available `here `_. + + The plan is to move all the HuggingFace repositories from my personal account to FURY's account. But here, I'll link to all my personal repositories which are currently active as I'm writing this report. + + * `Embeddings Endpoint `_ + This endpoint converts natural language to embeddings. The model is loaded using HuggingFace SentenceTransformer. + + * `Ollama Endpoint `_ + This endpoint could be used to communicate with the Ollama models. The perk of using this is it is more convenient and generally faster. A seperate repository was required because a single free HuggingFace Space cannot allocate more than 16 GB RAM and 2vCPUs. Token generation speed will be hit if it's not a seperate repository. + + * `Database Endpoint `_ + This endpoint was used to get the K-Nearest (or Approximate) embeddings based on cosine similarity. The parameter K could be passed to adjust it. We used Pinecone as the database. + + * `FURY Discord Bot `_ + The repository for the Discord bot. It was required to use threading here which is a quirk of HuggingFace. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. Discord bot ran on a seperate thread while a server ran on the main thread. + + * `FURY external cloud endpoints `_ + This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a seperate repo to abstract the logic and simplify calling different endpoints as was required. Basically you can hotswap multiple LLM models by changing the REST API parameters. + + * `GitHub App `_ + Repository for the GitHub application. Receives webhooks from GitHub and acts upon it using GraphQL queries. + + * `FURY Engine `_ + This is the main endpoint both Discord and GitHub frontend applications hit. It orchestrates all the other endpoints. The architecture on how it works is detailed later below. + + +- **Choosing the technologies to use** + + Choosing the technology depended largely on HuggingFace hardware support. We experimented inferencing LlamaCPP directly, inferencing Ollama, tested different quantizations and so on. Phi-3-mini-4k-instruct was chosen initially as the LLM. We rolled with it using Ollama for a few weeks. But as luck have it, I ended up discovering Groq which is a cloud provider who provide free LLM endpoints. We used Groq from then on, later also integrated Gemini since they also have free tier. + + You can hotswap between a local model, a Groq model, a Gemini normal model or a Gemini Fine-Tuned model as you wish using the FURY Engine endpoint. it'll all integrate cleanly with the Pinecone database outputs and give a standard API response. + + +- **Work on the backend architecture** + + This is the present backend architecture. + + .. raw:: html + + + + You're only hitting the FURY Engine endpoint, the remaining are all abstracted away. You can tell the engine you need to use Gemini and it'll do that for you. This is also expandable, if you have a new provider, you can add a new endpoint and connect it to FURY Engine. + + The data to REST endpoint will look like this + .. code-block:: json + + { + "query": "Render a cube in fury", + "llm": "llama3-70b-8192", + "knn": "3", + "stream": false + } + + Every output response will look like this + + .. code-block:: json + + { + "response": "Yes, this is how it would be done python import fury....", + "references": "1, 2, 3" + } + + So if you do + + `curl -X POST https://robinroy03-fury-engine.hf.space/api/groq/generate -H "Content-Type: application/json" -d '{"query": "How do I create a sphere in FURY?", "llm": "llama3-70b-8192", "knn": "3", "stream": false}'` + + You'll get a response from ``llama3-70b-8192`` using ``Groq``. If you do ``https://robinroy03-fury-engine.hf.space/api/google/generate`` you can call any Google Gemini models like ``gemini-1.5-pro`` or ``gemini-1.5-flash``. Same for ``Ollama``. + + A detailed blog on architecture is available `here. `_ + + +- **Work on improving model accuracy** + + The initial version had major issues of hallucination and was unable to retrieve relevant context. We fix them by collecting more data, improving RAG, setting up a benchmark and so on. + + Initial version used a naive parser to parse code, later my mentors told me to use an AST parser. I chunked the entire repo using this and it performed relatively better. For model benchmarking, we had 2 tests, one QnA testing and one code testing. If the code compiles, the model gets one point. + + All the benchmarking, data parsing, database upsertion scripts are `here. `_ + + We used an image model called ``moondream2`` to validate the output generated by the model. Since FURY is a graphics library, we need to judge the image to see whether it is correct or not. + + Fine-Tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `_. + + A detailed blog on benchmarking is available `here `_. + + A detailed blog on chunking is available `here `_. + + +- **Discord Bot integration** + + This included building the Discord bot and connecting it with the backend API. As mentioned above, threading was used to get the bot running on the server. But this won't affect any other part of the bot and it'll work as usual. + + This is how the discord integration looks like: + + .. image:: /_static/images/gsoc_robin_discord.jpg + :alt: Present Discord Bot UI. + + + The code runs! This is the ouput of the code: + + .. image:: /_static/images/gsoc_robin_discord_demo.jpg + :alt: Output of the code. + + + Work was also done on improving the UX of the bot. There is 👍 and 👎 options available for the user to rate the answer. We'll use those signals to improve the bot further. There are reference links at the bottom that leads to the exact places where the answers are sourced from. You can technically also use the discord bot as a search engine if you want to. + + Initially the bot had a sync over async problem. It was later fixed. Now multiple people can converse with the bot simultaneously. + + +- **GitHub App integration** + + This included building the GitHub app and figuring out how to setup the UX for it. GitHub used GraphQL, but we didn't use a seperate GraphQL library for this. We used a custom setup to query GraphQL endpoints. For us who only work with 1 or 2 commands, it works well. The code is `here `_. + + GitHub App UI looks like this: + + .. image:: /_static/images/robin_gsoc_github_ui.jpg + :alt: Present GitHub App UI. + + It is similar to Discord because the results come from the same backend. Refer the backend architecture above for reference. + + +Objectives in Progress +---------------------- + +- **Improving the LLM output** (ongoing) + + This will continue till I'm satisfied. It's a never ending journey :) Much of this GSoC was setting up things and getting it all to work as one piece. There are tons of new ideas coming up every day to increase LLM accuracy. I'll explore them and try interesting ones. + +- **Tests for all endpoints** (ongoing) + + It's important to have tests for all endpoints. Testing includes the following: + * Check the endpoints with valid data to see the response. Validate the JSON format. + * Check the endpoints with incorrect schema and record the response. + * Test by adjusting parameters like KNN. + +- **X Bot** (Optional Goal) + + I had a talk about this with my mentors. This can be done by plugging the LLM backend to an X bot frontend, but they suggested to spend my time improving model accuracy than simply add another frontend for the LLM application. + + +Timeline +-------- + + +.. list-table:: GSoC 2024 Weekly Reports + :widths: 15 50 50 + :header-rows: 1 + + * - Week + - Description + - Blog Post Link + * - Week 0 + - Community Bonding! + - https://fury.gl/latest/posts/2024/2024-05-28-week-0-robin.html + * - Week 1 + - It officially begins… + - https://fury.gl/latest/posts/2024/2024-06-06-week-1-robin.html + * - Week 2 + - The first iteration! + - https://fury.gl/latest/posts/2024/2024-06-16-week2-robin.html + * - Week 3 + - Data Data Data! + - https://fury.gl/latest/posts/2024/2024-06-16-week3-robin.html + * - Week 4 + - Pipeline Improvements and Taking The Bot Public! + - https://fury.gl/latest/posts/2024/2024-07-01-week-4-robin.html + * - Week 5 + - LLM Benchmarking & Architecture Modifications + - https://fury.gl/latest/posts/2024/2024-07-01-week-5-robin.html + * - Week 6 + - UI Improvements and RAG performance evaluation + - https://fury.gl/latest/posts/2024/2024-07-27-week6-robin.html + * - Week 7 + - Surviving final examinations + - https://fury.gl/latest/posts/2024/2024-07-27-week7-robin.html + * - Week 8 + - Gemini Finetuning + - https://fury.gl/latest/posts/2024/2024-07-27-week8-robin.html + * - Week 9 + - None + - None + * - Week 10 + - None + - None + * - Week 11 + - None + - None + * - Week 12 + - None + - None From dd9e04b1cc61e06deca01eacf9d841a39993546b Mon Sep 17 00:00:00 2001 From: Robin Date: Thu, 22 Aug 2024 22:54:12 +0530 Subject: [PATCH 2/8] DOC: final version --- .../2024/2024-08-21-final-report-robin.rst | 111 ++++++++++-------- 1 file changed, 64 insertions(+), 47 deletions(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index aaff5a70c..646008758 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -20,7 +20,7 @@ Google Summer of Code Final Work Product :tags: google :category: gsoc -- **Name:** Robin Roy +- **Name:** `Robin Roy `_ - **Organization:** Python Software Foundation - **Sub-Organization:** FURY - **Project:** `Improving Community Engagement: AI communication automation using LLM `_ @@ -29,7 +29,7 @@ Google Summer of Code Final Work Product Abstract -------- -This project had the goal to implement a `Large Language Model(LLM) `_ chatbot which understands the FURY repository. The purpose of the project is to reduce the barrier of entry to scientific visualization. `Retrieval Augmented Generation(RAG) `_ was used to get necessary context for every user query. Multiple variations were explored, including Fine-Tuning models, mixing Fine-Tuning and RAG and RAG alone. Multiple `chunking strategies `_ were also explored for data collection and storage. The models are served to the user through a Discord Bot and a GitHub App. All the API endpoints are hosted using `HuggingFace Spaces `_. `Pinecone `_ was used as the database for storing embeddings. Benchmarking, data collection, and testing were done on `another repository `_. +The goal of this project was to implement a `Large Language Model(LLM) `_ chatbot that understands the FURY repository. The purpose of the project is to reduce the barrier of entry to scientific visualization. `Retrieval Augmented Generation(RAG) `_ was used to get the necessary context for every user query. Multiple variations were explored, including Fine-Tuning models, mixing Fine-Tuning and RAG and RAG alone. Multiple `chunking strategies `_ were also explored for data collection and storage. The models are served to the user through a Discord Bot and a GitHub App. All the API endpoints are hosted using `HuggingFace Spaces `_. `Pinecone `_ was used as the database for storing embeddings. Benchmarking, data collection, and testing were done on `another repository `_. Proposed Objectives @@ -45,10 +45,10 @@ The objectives of the GSoC project could be broadly classified as: - **Choosing the technologies to use.** Work here included: - * Experiments with local `GGUF (GPT-Generated Unified Format)`_ models. + * Experiments with local `GGUF (GPT-Generated Unified Format) `_ models. * Experiments with different quantizations. - * Experiments with `Ollama. `_. - * Experiements with `LlamaCPP. `_ + * Experiments with `Ollama `_. + * Experiments with `LlamaCPP. `_ * Experiments with `Groq `_. * Experiments with `Google Gemini `_. @@ -72,7 +72,7 @@ The objectives of the GSoC project could be broadly classified as: The work included: * Building the Discord Bot. * Improving the UX of the bot. - * Improving performance of the bot. + * Improving the performance of the bot. - **GitHub App integration.** The work included: @@ -83,71 +83,69 @@ Objectives Completed -------------------- - **Figuring out hosting.** - As mentioned, we had a constraint on the cost. So we searched for how to host the application for free. This took us to explore interesting directions like Google Colab and Kaggle Notebooks. In the end, HuggingFace was decided to be the best place. Everything is containerized and currently hosted on HuggingFace. - This also meant that all the upcoming design/architectural choices will have to be based on HuggingFace. This will have some weird quirks at some places but overall HuggingFace was an excellent choice for the job. + This also meant that all the upcoming design/architectural choices would have to be based on HuggingFace. This will have some weird quirks at some places but overall HuggingFace was an excellent choice for the job. A very detailed blog on hosting is available `here `_. - The plan is to move all the HuggingFace repositories from my personal account to FURY's account. But here, I'll link to all my personal repositories which are currently active as I'm writing this report. + The plan is to move all the HuggingFace repositories from my account to FURY's account. But here, I'll link to all my repositories which are currently active as I'm writing this report. * `Embeddings Endpoint `_ This endpoint converts natural language to embeddings. The model is loaded using HuggingFace SentenceTransformer. * `Ollama Endpoint `_ - This endpoint could be used to communicate with the Ollama models. The perk of using this is it is more convenient and generally faster. A seperate repository was required because a single free HuggingFace Space cannot allocate more than 16 GB RAM and 2vCPUs. Token generation speed will be hit if it's not a seperate repository. + This endpoint could be used to communicate with the Ollama models. The perk of using this is it is more convenient and generally faster. A separate repository was required because a single free HuggingFace Space cannot allocate more than 16 GB RAM and 2vCPUs. Token generation speed will be hit if it's not a seperate repository. * `Database Endpoint `_ This endpoint was used to get the K-Nearest (or Approximate) embeddings based on cosine similarity. The parameter K could be passed to adjust it. We used Pinecone as the database. * `FURY Discord Bot `_ - The repository for the Discord bot. It was required to use threading here which is a quirk of HuggingFace. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. Discord bot ran on a seperate thread while a server ran on the main thread. + The repository for the Discord bot. It was required to use threading here which is a quirk of HuggingFace. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. The Discord bot ran on a separate thread while a server ran on the main thread. * `FURY external cloud endpoints `_ - This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a seperate repo to abstract the logic and simplify calling different endpoints as was required. Basically you can hotswap multiple LLM models by changing the REST API parameters. + This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a seperate repo to abstract the logic and simplify calling different endpoints as required. You can hot-swap multiple LLM models by changing the REST API parameters. * `GitHub App `_ - Repository for the GitHub application. Receives webhooks from GitHub and acts upon it using GraphQL queries. + Repository for the GitHub application. Receives webhooks from GitHub and acts upon them using GraphQL queries. * `FURY Engine `_ - This is the main endpoint both Discord and GitHub frontend applications hit. It orchestrates all the other endpoints. The architecture on how it works is detailed later below. + This is the main endpoint both Discord and GitHub frontend applications hit. It orchestrates all the other endpoints. The architecture of how it works is detailed later below. - **Choosing the technologies to use** + Choosing the technology depended largely on HuggingFace hardware support. We experimented with inferencing LlamaCPP directly, inferencing Ollama, tested different quantizations and so on. Phi-3-mini-4k-instruct was chosen initially as the LLM. We rolled with it using Ollama for a few weeks. But as luck has it, I ended up discovering Groq is a cloud provider that provides free LLM endpoints. We used Groq from then on, and later also integrated Gemini since they also have a free tier. - Choosing the technology depended largely on HuggingFace hardware support. We experimented inferencing LlamaCPP directly, inferencing Ollama, tested different quantizations and so on. Phi-3-mini-4k-instruct was chosen initially as the LLM. We rolled with it using Ollama for a few weeks. But as luck have it, I ended up discovering Groq which is a cloud provider who provide free LLM endpoints. We used Groq from then on, later also integrated Gemini since they also have free tier. - - You can hotswap between a local model, a Groq model, a Gemini normal model or a Gemini Fine-Tuned model as you wish using the FURY Engine endpoint. it'll all integrate cleanly with the Pinecone database outputs and give a standard API response. + You can hot-swap between a local model, a Groq model, a Gemini normal model or a Gemini Fine-Tuned model as you wish using the FURY Engine endpoint. it'll all integrate cleanly with the Pinecone database outputs and give a standard API response. - **Work on the backend architecture** - This is the present backend architecture. - .. raw:: html + .. image:: /_static/images/gsoc_llm_robin_week5.jpg + :alt: Present backend architecture - - + You're only hitting the FURY Engine endpoint, the remaining are all abstracted away. You can tell the engine you need to use Gemini and it'll do that for you. This is also expandable, if you have a new provider, you can add a new endpoint and connect it to FURY Engine. - The data to REST endpoint will look like this + The data to the REST endpoint will look like this + .. code-block:: json { - "query": "Render a cube in fury", - "llm": "llama3-70b-8192", - "knn": "3", - "stream": false + "query": "Render a cube in fury", + "llm": "llama3-70b-8192", + "knn": "3", + "stream": false } - + Every output response will look like this .. code-block:: json { - "response": "Yes, this is how it would be done python import fury....", - "references": "1, 2, 3" + "response": "Yes, this is how it would be done python import fury....", + "references": "1, 2, 3" } So if you do @@ -160,16 +158,15 @@ Objectives Completed - **Work on improving model accuracy** - The initial version had major issues of hallucination and was unable to retrieve relevant context. We fix them by collecting more data, improving RAG, setting up a benchmark and so on. - Initial version used a naive parser to parse code, later my mentors told me to use an AST parser. I chunked the entire repo using this and it performed relatively better. For model benchmarking, we had 2 tests, one QnA testing and one code testing. If the code compiles, the model gets one point. + The Initial version used a naive parser to parse code, later my mentors told me to use an AST parser. I chunked the entire repo using this and it performed relatively better. For model benchmarking, we had 2 tests, one QnA testing and one code testing. If the code compiles, the model gets one point. - All the benchmarking, data parsing, database upsertion scripts are `here. `_ + All the benchmarking, data parsing, and database upsertion scripts are `here. `_ We used an image model called ``moondream2`` to validate the output generated by the model. Since FURY is a graphics library, we need to judge the image to see whether it is correct or not. - Fine-Tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `_. + Fine-tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `_. A detailed blog on benchmarking is available `here `_. @@ -177,55 +174,75 @@ Objectives Completed - **Discord Bot integration** - This included building the Discord bot and connecting it with the backend API. As mentioned above, threading was used to get the bot running on the server. But this won't affect any other part of the bot and it'll work as usual. - This is how the discord integration looks like: + This is what the discord integration looks like: .. image:: /_static/images/gsoc_robin_discord.jpg :alt: Present Discord Bot UI. - The code runs! This is the ouput of the code: + The code runs! This is the output of the code: .. image:: /_static/images/gsoc_robin_discord_demo.jpg :alt: Output of the code. - Work was also done on improving the UX of the bot. There is 👍 and 👎 options available for the user to rate the answer. We'll use those signals to improve the bot further. There are reference links at the bottom that leads to the exact places where the answers are sourced from. You can technically also use the discord bot as a search engine if you want to. + Work was also done on improving the UX of the bot. There are 👍 and 👎 options available for the user to rate the answer. We'll use those signals to improve the bot further. There are reference links at the bottom that lead to the exact places where the answers are sourced from. You can technically also use the Discord bot as a search engine if you want to. - Initially the bot had a sync over async problem. It was later fixed. Now multiple people can converse with the bot simultaneously. + Initially, the bot had a sync over async problem. It was later fixed. Now multiple people can converse with the bot simultaneously. - **GitHub App integration** - - This included building the GitHub app and figuring out how to setup the UX for it. GitHub used GraphQL, but we didn't use a seperate GraphQL library for this. We used a custom setup to query GraphQL endpoints. For us who only work with 1 or 2 commands, it works well. The code is `here `_. + This included building the GitHub app and figuring out how to setup the UX for it. GitHub used GraphQL, but we didn't use a separate GraphQL library for this. We used a custom setup to query GraphQL endpoints. For us who only work with 1 or 2 commands, it works well. The code is `here `_. GitHub App UI looks like this: .. image:: /_static/images/robin_gsoc_github_ui.jpg :alt: Present GitHub App UI. - It is similar to Discord because the results come from the same backend. Refer the backend architecture above for reference. + It is similar to Discord because the results come from the same backend. Refer to the backend architecture above for reference. -Objectives in Progress ----------------------- +Other Objectives +---------------- - **Improving the LLM output** (ongoing) - This will continue till I'm satisfied. It's a never ending journey :) Much of this GSoC was setting up things and getting it all to work as one piece. There are tons of new ideas coming up every day to increase LLM accuracy. I'll explore them and try interesting ones. - **Tests for all endpoints** (ongoing) - It's important to have tests for all endpoints. Testing includes the following: * Check the endpoints with valid data to see the response. Validate the JSON format. * Check the endpoints with incorrect schema and record the response. * Test by adjusting parameters like KNN. -- **X Bot** (Optional Goal) +- **X Bot** (Optional Goal, deferred for now) + I had a talk about this with my mentors. This can be done by plugging the LLM backend into an X bot frontend, but they suggested spending my time improving model accuracy rather than simply adding another frontend for the LLM application. + + +Other Open Source tasks +----------------------- + +GSoC isn't all about what I do with my project. It exists along with the 3 other cool projects my peers - `Wachiou `_, `Iñigo `_ and `Kaustav `_ did. I learnt a lot through them reviewing my PRs and me reviewing their PRs. I attended all the weekly meetings of Wachiou to learn about his progress and to learn new stuff. He attended all my meetings too, which was awesome :) + +Contributions to FURY apart from the ones directly part of GSoC: + * https://github.com/fury-gl/fury/pull/862 - Rendering videos on a cube + * https://github.com/fury-gl/fury/pull/861 - docstring improvements + * https://github.com/fury-gl/fury/pull/891 - Codespell fix + * https://github.com/fury-gl/fury/pull/893 - .gitignore modification + * https://github.com/fury-gl/fury/issues/924 - Raised issue + +Contributions to other repositories during this time, due to GSoC work: + * https://github.com/langchain-ai/langchain/issues/23515 - Langchain issue raised + * https://github.com/github/docs/issues/34258 - GitHub issue raised + * https://github.com/github/docs/pull/34259 - PR for the raised GitHub issue + * https://github.com/orgs/community/discussions/136436 - GitHub feature request + + +Acknowledgement +--------------- - I had a talk about this with my mentors. This can be done by plugging the LLM backend to an X bot frontend, but they suggested to spend my time improving model accuracy than simply add another frontend for the LLM application. +I am very thankful to my mentors `Serge Koudoro `_ and `Mohamed Abouagour `_. They were awesome and provided me with a comfortable environment to work in. Also got to thank `Beleswar Prasad Padhi `_ who gave me a very good introduction to opensource. The good thing about open source is I can still work on this (and other FURY projects) till I'm satisfied. I'm excited to continue contributing to the open source community. Timeline From 9e18d3271dbe30a595eb49d032802e85a6978965 Mon Sep 17 00:00:00 2001 From: Robin Date: Thu, 22 Aug 2024 23:00:59 +0530 Subject: [PATCH 3/8] fix: codespell --- .../posts/2024/2024-08-21-final-report-robin.rst | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index 646008758..83e83340a 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -38,12 +38,14 @@ Proposed Objectives The objectives of the GSoC project could be broadly classified as: - **Figuring out hosting.** + We had a constraint on hosting to try and minimize the cost. We managed to complete the whole project with 100% free hosting. Work here included: * Experiments with `Google Colab `_ notebook hosting. * Experiments with `Kaggle `_ notebook hosting. * Experiments with `HuggingFace `_ spaces hosting. - **Choosing the technologies to use.** + Work here included: * Experiments with local `GGUF (GPT-Generated Unified Format) `_ models. * Experiments with different quantizations. @@ -53,6 +55,7 @@ The objectives of the GSoC project could be broadly classified as: * Experiments with `Google Gemini `_. - **Work on the backend architecture.** + Backend architecture was heavily influenced by HuggingFace and its limitations. Work here included: * Choosing the API architecture. * Integrating different models. @@ -60,6 +63,7 @@ The objectives of the GSoC project could be broadly classified as: * Improving the UX of the endpoints. - **Work on improving model accuracy.** + This was a recurring work and kept happening on most weeks. It included: * Model Benchmarking * Data Collection @@ -69,12 +73,14 @@ The objectives of the GSoC project could be broadly classified as: * Experiments on Retrieval quantity. - **Discord Bot integration.** + The work included: * Building the Discord Bot. * Improving the UX of the bot. * Improving the performance of the bot. - **GitHub App integration.** + The work included: * Building the GitHub App integration. * Improving the UX of the integration. @@ -95,7 +101,7 @@ Objectives Completed This endpoint converts natural language to embeddings. The model is loaded using HuggingFace SentenceTransformer. * `Ollama Endpoint `_ - This endpoint could be used to communicate with the Ollama models. The perk of using this is it is more convenient and generally faster. A separate repository was required because a single free HuggingFace Space cannot allocate more than 16 GB RAM and 2vCPUs. Token generation speed will be hit if it's not a seperate repository. + This endpoint could be used to communicate with the Ollama models. The perk of using this is it is more convenient and generally faster. A separate repository was required because a single free HuggingFace Space cannot allocate more than 16 GB RAM and 2vCPUs. Token generation speed will be hit if it's not a separate repository. * `Database Endpoint `_ This endpoint was used to get the K-Nearest (or Approximate) embeddings based on cosine similarity. The parameter K could be passed to adjust it. We used Pinecone as the database. @@ -104,7 +110,7 @@ Objectives Completed The repository for the Discord bot. It was required to use threading here which is a quirk of HuggingFace. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. The Discord bot ran on a separate thread while a server ran on the main thread. * `FURY external cloud endpoints `_ - This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a seperate repo to abstract the logic and simplify calling different endpoints as required. You can hot-swap multiple LLM models by changing the REST API parameters. + This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a separate repo to abstract the logic and simplify calling different endpoints as required. You can hot-swap multiple LLM models by changing the REST API parameters. * `GitHub App `_ Repository for the GitHub application. Receives webhooks from GitHub and acts upon them using GraphQL queries. @@ -211,6 +217,7 @@ Other Objectives This will continue till I'm satisfied. It's a never ending journey :) Much of this GSoC was setting up things and getting it all to work as one piece. There are tons of new ideas coming up every day to increase LLM accuracy. I'll explore them and try interesting ones. - **Tests for all endpoints** (ongoing) + It's important to have tests for all endpoints. Testing includes the following: * Check the endpoints with valid data to see the response. Validate the JSON format. * Check the endpoints with incorrect schema and record the response. From 0c4362d38663d347f116669a58b52eeb9cd7bbce Mon Sep 17 00:00:00 2001 From: Robin Date: Thu, 22 Aug 2024 23:13:10 +0530 Subject: [PATCH 4/8] fix: CI and doc UI --- .../2024/2024-08-21-final-report-robin.rst | 73 ++++++++++--------- 1 file changed, 37 insertions(+), 36 deletions(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index 83e83340a..f299351b2 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -38,52 +38,53 @@ Proposed Objectives The objectives of the GSoC project could be broadly classified as: - **Figuring out hosting.** - We had a constraint on hosting to try and minimize the cost. We managed to complete the whole project with 100% free hosting. Work here included: - * Experiments with `Google Colab `_ notebook hosting. - * Experiments with `Kaggle `_ notebook hosting. - * Experiments with `HuggingFace `_ spaces hosting. -- **Choosing the technologies to use.** + * Experiments with `Google Colab `_ notebook hosting. + * Experiments with `Kaggle `_ notebook hosting. + * Experiments with `HuggingFace `_ spaces hosting. +- **Choosing the technologies to use.** Work here included: - * Experiments with local `GGUF (GPT-Generated Unified Format) `_ models. - * Experiments with different quantizations. - * Experiments with `Ollama `_. - * Experiments with `LlamaCPP. `_ - * Experiments with `Groq `_. - * Experiments with `Google Gemini `_. -- **Work on the backend architecture.** + * Experiments with local `GGUF (GPT-Generated Unified Format) `_ models. + * Experiments with different quantizations. + * Experiments with `Ollama `_. + * Experiments with `LlamaCPP. `_ + * Experiments with `Groq `_. + * Experiments with `Google Gemini `_. +- **Work on the backend architecture.** Backend architecture was heavily influenced by HuggingFace and its limitations. Work here included: - * Choosing the API architecture. - * Integrating different models. - * Improving concurrent requests support. - * Improving the UX of the endpoints. -- **Work on improving model accuracy.** + * Choosing the API architecture. + * Integrating different models. + * Improving concurrent requests support. + * Improving the UX of the endpoints. +- **Work on improving model accuracy.** This was a recurring work and kept happening on most weeks. It included: - * Model Benchmarking - * Data Collection - * Experiments on Retrieval Augmented Generation. - * Experiments on Fine-Tuning. - * Experiments on Chunking. - * Experiments on Retrieval quantity. -- **Discord Bot integration.** + * Model Benchmarking + * Data Collection + * Experiments on Retrieval Augmented Generation. + * Experiments on Fine-Tuning. + * Experiments on Chunking. + * Experiments on Retrieval quantity. +- **Discord Bot integration.** The work included: - * Building the Discord Bot. - * Improving the UX of the bot. - * Improving the performance of the bot. -- **GitHub App integration.** + * Building the Discord Bot. + * Improving the UX of the bot. + * Improving the performance of the bot. +- **GitHub App integration.** The work included: - * Building the GitHub App integration. - * Improving the UX of the integration. + + * Building the GitHub App integration. + * Improving the UX of the integration. + Objectives Completed -------------------- @@ -108,7 +109,7 @@ Objectives Completed * `FURY Discord Bot `_ The repository for the Discord bot. It was required to use threading here which is a quirk of HuggingFace. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. The Discord bot ran on a separate thread while a server ran on the main thread. - + * `FURY external cloud endpoints `_ This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a separate repo to abstract the logic and simplify calling different endpoints as required. You can hot-swap multiple LLM models by changing the REST API parameters. @@ -130,7 +131,7 @@ Objectives Completed .. image:: /_static/images/gsoc_llm_robin_week5.jpg :alt: Present backend architecture - + You're only hitting the FURY Engine endpoint, the remaining are all abstracted away. You can tell the engine you need to use Gemini and it'll do that for you. This is also expandable, if you have a new provider, you can add a new endpoint and connect it to FURY Engine. @@ -217,11 +218,11 @@ Other Objectives This will continue till I'm satisfied. It's a never ending journey :) Much of this GSoC was setting up things and getting it all to work as one piece. There are tons of new ideas coming up every day to increase LLM accuracy. I'll explore them and try interesting ones. - **Tests for all endpoints** (ongoing) - It's important to have tests for all endpoints. Testing includes the following: - * Check the endpoints with valid data to see the response. Validate the JSON format. - * Check the endpoints with incorrect schema and record the response. - * Test by adjusting parameters like KNN. + + * Check the endpoints with valid data to see the response. Validate the JSON format. + * Check the endpoints with incorrect schema and record the response. + * Test by adjusting parameters like KNN. - **X Bot** (Optional Goal, deferred for now) I had a talk about this with my mentors. This can be done by plugging the LLM backend into an X bot frontend, but they suggested spending my time improving model accuracy rather than simply adding another frontend for the LLM application. From e2e7de1e3112531b9ed08158d61d4d92fae9d9ad Mon Sep 17 00:00:00 2001 From: Robin Date: Sun, 25 Aug 2024 20:31:55 +0530 Subject: [PATCH 5/8] blog links added, other suggestions incorporated --- .../2024/2024-08-21-final-report-robin.rst | 101 +++++++++--------- 1 file changed, 52 insertions(+), 49 deletions(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index f299351b2..5593e2a12 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -20,16 +20,16 @@ Google Summer of Code Final Work Product :tags: google :category: gsoc -- **Name:** `Robin Roy `_ -- **Organization:** Python Software Foundation -- **Sub-Organization:** FURY -- **Project:** `Improving Community Engagement: AI communication automation using LLM `_ +- **Name:** `Robin Roy `__ +- **Organization:** `Python Software Foundation `__ +- **Sub-Organization:** `FURY `__ +- **Project:** `Improving Community Engagement: AI communication automation using LLM `__ Abstract -------- -The goal of this project was to implement a `Large Language Model(LLM) `_ chatbot that understands the FURY repository. The purpose of the project is to reduce the barrier of entry to scientific visualization. `Retrieval Augmented Generation(RAG) `_ was used to get the necessary context for every user query. Multiple variations were explored, including Fine-Tuning models, mixing Fine-Tuning and RAG and RAG alone. Multiple `chunking strategies `_ were also explored for data collection and storage. The models are served to the user through a Discord Bot and a GitHub App. All the API endpoints are hosted using `HuggingFace Spaces `_. `Pinecone `_ was used as the database for storing embeddings. Benchmarking, data collection, and testing were done on `another repository `_. +The goal of this project was to implement a `Large Language Model (LLM) `__ chatbot that understands the FURY repository. The purpose of the project is to reduce the barrier of entry to scientific visualization. `Retrieval Augmented Generation (RAG) `__ was used to get the necessary context for every user query. Multiple variations were explored, including Fine-Tuning models, mixing Fine-Tuning and RAG and RAG alone. Multiple `chunking strategies `__ were also explored for data collection and storage. The models are served to the user through a Discord Bot and a GitHub App. All the API endpoints are hosted using `HuggingFace Spaces `_. `Pinecone `__ was used as the database for storing embeddings. Benchmarking, data collection, and testing were done on `another repository `__. Proposed Objectives @@ -40,19 +40,19 @@ The objectives of the GSoC project could be broadly classified as: - **Figuring out hosting.** We had a constraint on hosting to try and minimize the cost. We managed to complete the whole project with 100% free hosting. Work here included: - * Experiments with `Google Colab `_ notebook hosting. - * Experiments with `Kaggle `_ notebook hosting. - * Experiments with `HuggingFace `_ spaces hosting. + * Experiments with `Google Colab `__ notebook hosting. + * Experiments with `Kaggle `__ notebook hosting. + * Experiments with `HuggingFace `__ spaces hosting. - **Choosing the technologies to use.** Work here included: - * Experiments with local `GGUF (GPT-Generated Unified Format) `_ models. + * Experiments with local `GGUF (GPT-Generated Unified Format) `__ models. * Experiments with different quantizations. - * Experiments with `Ollama `_. - * Experiments with `LlamaCPP. `_ - * Experiments with `Groq `_. - * Experiments with `Google Gemini `_. + * Experiments with `Ollama `__. + * Experiments with `LlamaCPP. `__ + * Experiments with `Groq `__. + * Experiments with `Google Gemini `__. - **Work on the backend architecture.** Backend architecture was heavily influenced by HuggingFace and its limitations. Work here included: @@ -90,35 +90,38 @@ Objectives Completed -------------------- - **Figuring out hosting.** - As mentioned, we had a constraint on the cost. So we searched for how to host the application for free. This took us to explore interesting directions like Google Colab and Kaggle Notebooks. In the end, HuggingFace was decided to be the best place. Everything is containerized and currently hosted on HuggingFace. + As mentioned, we had a constraint on the cost. We explored different options for free hosting. This took us to explore interesting directions like Google Colab and Kaggle Notebooks. In the end, HuggingFace was decided to be the best place. Everything is containerized and currently hosted on HuggingFace. - This also meant that all the upcoming design/architectural choices would have to be based on HuggingFace. This will have some weird quirks at some places but overall HuggingFace was an excellent choice for the job. + This also meant that all the upcoming design/architectural choices would have to be based on HuggingFace. This will cause some challenges on the Discord bot hosting but overall HuggingFace was a solid choice. - A very detailed blog on hosting is available `here `_. + A very detailed blog on hosting is available `here `__. The plan is to move all the HuggingFace repositories from my account to FURY's account. But here, I'll link to all my repositories which are currently active as I'm writing this report. - * `Embeddings Endpoint `_ + * `Embeddings Endpoint `__ This endpoint converts natural language to embeddings. The model is loaded using HuggingFace SentenceTransformer. - * `Ollama Endpoint `_ + * `Ollama Endpoint `__ This endpoint could be used to communicate with the Ollama models. The perk of using this is it is more convenient and generally faster. A separate repository was required because a single free HuggingFace Space cannot allocate more than 16 GB RAM and 2vCPUs. Token generation speed will be hit if it's not a separate repository. - * `Database Endpoint `_ + * `Database Endpoint `__ This endpoint was used to get the K-Nearest (or Approximate) embeddings based on cosine similarity. The parameter K could be passed to adjust it. We used Pinecone as the database. - * `FURY Discord Bot `_ - The repository for the Discord bot. It was required to use threading here which is a quirk of HuggingFace. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. The Discord bot ran on a separate thread while a server ran on the main thread. + * `FURY Discord Bot `__ + The repository for the Discord bot. It was required to use threading here which is a side-effect of HuggingFace Spaces. HuggingFace server only activates once there is an active live endpoint. Discord did not need an endpoint, but we had to make one to get the server activated. The Discord bot ran on a separate thread while a server ran on the main thread. - * `FURY external cloud endpoints `_ + * `FURY external cloud endpoints `__ This repository orchestrated external APIs from 3rd party providers like Groq and Gemini. We made it a separate repo to abstract the logic and simplify calling different endpoints as required. You can hot-swap multiple LLM models by changing the REST API parameters. - * `GitHub App `_ + * `GitHub App `__ Repository for the GitHub application. Receives webhooks from GitHub and acts upon them using GraphQL queries. - * `FURY Engine `_ + * `FURY Engine `__ This is the main endpoint both Discord and GitHub frontend applications hit. It orchestrates all the other endpoints. The architecture of how it works is detailed later below. + * `FURY Data Parsing/Benchmarking/Testing Repo (GitHub) `__ + This is a GitHub repository and contains all the parsing, bechmarking and testing scripts. + - **Choosing the technologies to use** Choosing the technology depended largely on HuggingFace hardware support. We experimented with inferencing LlamaCPP directly, inferencing Ollama, tested different quantizations and so on. Phi-3-mini-4k-instruct was chosen initially as the LLM. We rolled with it using Ollama for a few weeks. But as luck has it, I ended up discovering Groq is a cloud provider that provides free LLM endpoints. We used Groq from then on, and later also integrated Gemini since they also have a free tier. @@ -161,7 +164,7 @@ Objectives Completed You'll get a response from ``llama3-70b-8192`` using ``Groq``. If you do ``https://robinroy03-fury-engine.hf.space/api/google/generate`` you can call any Google Gemini models like ``gemini-1.5-pro`` or ``gemini-1.5-flash``. Same for ``Ollama``. - A detailed blog on architecture is available `here. `_ + A detailed blog on architecture is available `here. `__ - **Work on improving model accuracy** @@ -169,15 +172,15 @@ Objectives Completed The Initial version used a naive parser to parse code, later my mentors told me to use an AST parser. I chunked the entire repo using this and it performed relatively better. For model benchmarking, we had 2 tests, one QnA testing and one code testing. If the code compiles, the model gets one point. - All the benchmarking, data parsing, and database upsertion scripts are `here. `_ + All the benchmarking, data parsing, and database upsertion scripts are `here. `__ We used an image model called ``moondream2`` to validate the output generated by the model. Since FURY is a graphics library, we need to judge the image to see whether it is correct or not. - Fine-tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `_. + Fine-tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `__. - A detailed blog on benchmarking is available `here `_. + A detailed blog on benchmarking is available `here `__. - A detailed blog on chunking is available `here `_. + A detailed blog on chunking is available `here `__. - **Discord Bot integration** @@ -201,7 +204,7 @@ Objectives Completed - **GitHub App integration** - This included building the GitHub app and figuring out how to setup the UX for it. GitHub used GraphQL, but we didn't use a separate GraphQL library for this. We used a custom setup to query GraphQL endpoints. For us who only work with 1 or 2 commands, it works well. The code is `here `_. + This included building the GitHub app and figuring out how to setup the UX for it. GitHub used GraphQL, but we didn't use a separate GraphQL library for this. We used a custom setup to query GraphQL endpoints. For us who only work with 1 or 2 commands, it works well. The code is `here `__. GitHub App UI looks like this: @@ -231,7 +234,7 @@ Other Objectives Other Open Source tasks ----------------------- -GSoC isn't all about what I do with my project. It exists along with the 3 other cool projects my peers - `Wachiou `_, `Iñigo `_ and `Kaustav `_ did. I learnt a lot through them reviewing my PRs and me reviewing their PRs. I attended all the weekly meetings of Wachiou to learn about his progress and to learn new stuff. He attended all my meetings too, which was awesome :) +GSoC isn't all about what I do with my project. It exists along with the 3 other cool projects my peers - `Wachiou `__, `Iñigo `__ and `Kaustav `__ did. I learnt a lot through them reviewing my PRs and me reviewing their PRs. I attended all the weekly meetings of Wachiou to learn about his progress and to learn new stuff. He attended all my meetings too, which was awesome :) Contributions to FURY apart from the ones directly part of GSoC: * https://github.com/fury-gl/fury/pull/862 - Rendering videos on a cube @@ -250,7 +253,7 @@ Contributions to other repositories during this time, due to GSoC work: Acknowledgement --------------- -I am very thankful to my mentors `Serge Koudoro `_ and `Mohamed Abouagour `_. They were awesome and provided me with a comfortable environment to work in. Also got to thank `Beleswar Prasad Padhi `_ who gave me a very good introduction to opensource. The good thing about open source is I can still work on this (and other FURY projects) till I'm satisfied. I'm excited to continue contributing to the open source community. +I am very thankful to my mentors `Serge Koudoro `__ and `Mohamed Abouagour `__. They were awesome and provided me with a comfortable environment to work in. Also got to thank `Beleswar Prasad Padhi `__ who gave me a very good introduction to opensource. The good thing about open source is I can still work on this (and other FURY projects) till I'm satisfied. I'm excited to continue contributing to the open source community. Timeline @@ -266,40 +269,40 @@ Timeline - Blog Post Link * - Week 0 - Community Bonding! - - https://fury.gl/latest/posts/2024/2024-05-28-week-0-robin.html + - `Blog 0 `__ * - Week 1 - It officially begins… - - https://fury.gl/latest/posts/2024/2024-06-06-week-1-robin.html + - `Blog 1 `__ * - Week 2 - The first iteration! - - https://fury.gl/latest/posts/2024/2024-06-16-week2-robin.html + - `Blog 2 `__ * - Week 3 - Data Data Data! - - https://fury.gl/latest/posts/2024/2024-06-16-week3-robin.html + - `Blog 3 `__ * - Week 4 - Pipeline Improvements and Taking The Bot Public! - - https://fury.gl/latest/posts/2024/2024-07-01-week-4-robin.html + - `Blog 4 `__ * - Week 5 - LLM Benchmarking & Architecture Modifications - - https://fury.gl/latest/posts/2024/2024-07-01-week-5-robin.html + - `Blog 5 `__ * - Week 6 - UI Improvements and RAG performance evaluation - - https://fury.gl/latest/posts/2024/2024-07-27-week6-robin.html + - `Blog 6 `__ * - Week 7 - Surviving final examinations - - https://fury.gl/latest/posts/2024/2024-07-27-week7-robin.html + - `Blog 7 `__ * - Week 8 - Gemini Finetuning - - https://fury.gl/latest/posts/2024/2024-07-27-week8-robin.html + - `Blog 8 `__ * - Week 9 - - None - - None + - Hosting FineTuned Models + - `Blog 9 `__ * - Week 10 - - None - - None + - Learning GraphQL + - `Blog 10 `__ * - Week 11 - - None - - None + - Getting the App Live + - `Blog 11 `__ * - Week 12 - - None - - None + - Wrapping things up + - `Blog 12 `__ From 453d1885c3a285f89a5eb900cd4cd9c6d4159a5c Mon Sep 17 00:00:00 2001 From: Robin Date: Sun, 25 Aug 2024 20:38:15 +0530 Subject: [PATCH 6/8] fix: codespell --- docs/source/posts/2024/2024-08-21-final-report-robin.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index 5593e2a12..5513c7fbf 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -120,7 +120,7 @@ Objectives Completed This is the main endpoint both Discord and GitHub frontend applications hit. It orchestrates all the other endpoints. The architecture of how it works is detailed later below. * `FURY Data Parsing/Benchmarking/Testing Repo (GitHub) `__ - This is a GitHub repository and contains all the parsing, bechmarking and testing scripts. + This is a GitHub repository and contains all the parsing, benchmarking and testing scripts. - **Choosing the technologies to use** From eee17ca154cdf12bdc898963b89865cb431e8da4 Mon Sep 17 00:00:00 2001 From: Robin Date: Sun, 25 Aug 2024 21:09:42 +0530 Subject: [PATCH 7/8] rephrasing text --- docs/source/posts/2024/2024-08-21-final-report-robin.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index 5513c7fbf..0b2d1f0cb 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -38,7 +38,7 @@ Proposed Objectives The objectives of the GSoC project could be broadly classified as: - **Figuring out hosting.** - We had a constraint on hosting to try and minimize the cost. We managed to complete the whole project with 100% free hosting. Work here included: + We were constrained by the need to minimize hosting costs. We managed to complete the whole project with 100% free hosting. Work here included: * Experiments with `Google Colab `__ notebook hosting. * Experiments with `Kaggle `__ notebook hosting. From e77bbb7c776fe2dead249df6356afa7df7e9cbe5 Mon Sep 17 00:00:00 2001 From: Robin Date: Mon, 26 Aug 2024 01:54:05 +0530 Subject: [PATCH 8/8] fix: links mofied to support naming consistencies --- .../2024/2024-08-21-final-report-robin.rst | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/source/posts/2024/2024-08-21-final-report-robin.rst b/docs/source/posts/2024/2024-08-21-final-report-robin.rst index 0b2d1f0cb..1f2a82fca 100644 --- a/docs/source/posts/2024/2024-08-21-final-report-robin.rst +++ b/docs/source/posts/2024/2024-08-21-final-report-robin.rst @@ -176,11 +176,11 @@ Objectives Completed We used an image model called ``moondream2`` to validate the output generated by the model. Since FURY is a graphics library, we need to judge the image to see whether it is correct or not. - Fine-tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `__. + Fine-tuning was done on Google AI Studio. We Fine-Tuned using question/answer pairs from Discord and GitHub discussions. We later tried mixing RAG + Fine-Tuning. A detailed blog on Fine-Tuning is available `here `__. A detailed blog on benchmarking is available `here `__. - A detailed blog on chunking is available `here `__. + A detailed blog on chunking is available `here `__. - **Discord Bot integration** @@ -275,10 +275,10 @@ Timeline - `Blog 1 `__ * - Week 2 - The first iteration! - - `Blog 2 `__ + - `Blog 2 `__ * - Week 3 - Data Data Data! - - `Blog 3 `__ + - `Blog 3 `__ * - Week 4 - Pipeline Improvements and Taking The Bot Public! - `Blog 4 `__ @@ -287,22 +287,22 @@ Timeline - `Blog 5 `__ * - Week 6 - UI Improvements and RAG performance evaluation - - `Blog 6 `__ + - `Blog 6 `__ * - Week 7 - Surviving final examinations - - `Blog 7 `__ + - `Blog 7 `__ * - Week 8 - Gemini Finetuning - - `Blog 8 `__ + - `Blog 8 `__ * - Week 9 - Hosting FineTuned Models - - `Blog 9 `__ + - `Blog 9 `__ * - Week 10 - Learning GraphQL - - `Blog 10 `__ + - `Blog 10 `__ * - Week 11 - Getting the App Live - - `Blog 11 `__ + - `Blog 11 `__ * - Week 12 - Wrapping things up - - `Blog 12 `__ + - `Blog 12 `__