ChangeLog May 30, 2023 - June 30, 2023

Swarms

Version 2.1.0 (2023-07-03)

• Landed upon a glitch while using the whisperx repository, pip was insistent on finding a branch or tag named v3 that simply did not exist in the parallel universe of the repo. Turns out, specifying a non-existent branch isn't really well received, who'd have guessed?

Version 2.0.1 (2023-06-30)

• We've tinkered with the AI and implemented meta prompting for every agent (boss and worker). So now, they can use their noggin' and think a bit before making decisions. We're sure they'll appreciate the extra brainpower.

• In the pursuit of more control, we launched a new feature – a worker Swarm class. These workers are all equal, they can self scale, and they're able to spawn new workers when they need help. It's a whole new world of autonomy.

• The ship hit a minor turbulence when the module unittesting/swarms.py seemed to be lost in the ether. It was a classic ModuleNotFoundError: No module named 'unittesting/swarms'.

Version 2.0.0 (2023-06-28)

• We gave birth to a new baby, BabyAGI! It's now equipped with an AutoGPT instance as a tool. We're confident that it will outshine its parents.

• We dived deep into the Ocean and found an incredible treasure - The Ocean Vector DB. It's now the main embedding database for all our agents. Boss and worker, all will benefit from the deep Ocean.

AthenaCreate

1. Upgraded from Docker to Kubernetes

2. Autoscaling and GPU Resources Management

3. AWS Deployment with Terraform

4. Data Backup and Disaster Recovery

5. Deployment Plan Creation

VisualNexus

VisualNexus: The Nexus of Visual Intelligence and SOTA AI

My vision for VisualNexus isn’t just about creating a tool; it’s about igniting a paradigm shift in how we understand and utilize visual…

https://medium.com/@kyeg/visualnexus-the-nexus-of-visual-intelligence-and-sota-ai-f971753a415a

GitHub - kyegomez/VisualNexus at main

An plug in and play pipeline that utilizes segment anything to segment datasets with rich detail for downstream fine-tuning on vision models like CLIP, ViT, Imagebind, and so on! - GitHub - kyegom...

https://github.com/kyegomez/VisualNexus/tree/main

Version 1.1.0

New Features:

• MobileSAM Integration:
Introduced the class MobileSAM that works with the Hugging Face datasets. This integration is an advancement that allows automatic segmentation and labeling of every image in the dataset. It's like a self-driving car but for data labeling, reducing human input and hence, the chance of errors. One small step for code, one giant leap for data science.

• Support for Streaming Datasets:
The beast of a machine that this framework has become now supports streaming datasets from Hugging Face. VisualNexus eats big data for breakfast now.

Bug Fixes:

• Loading Large Datasets:
We ran into a roadblock while trying to load very large datasets. Like a SpaceX rocket with a fuel problem, our pipeline was struggling to lift off due to memory issues. After performing RCA, it was determined that the problem lay in our dataset loading technique. We solved this problem by integrating dataset streaming from Hugging Face, which now allows us to handle virtually unlimited sizes of datasets without breaking a sweat.

• Segmentation of Non-Image Data:
A minor hitch we encountered was that the framework attempted to segment non-image data. Turns out it was trying to overachieve. We've given it a bit of a pep talk, and the problem has been resolved. We now have a data check integrated into our pipeline to ensure only the appropriate data types are processed.

Version 1.0.0

New Features:

• VisualNexus Birth:
We gave birth to VisualNexus, the brainchild of our vision for AI. It was a painless delivery. With this open-source training pipeline, you can segment and label visual datasets using a single model. It's like having your own private AI assistant, but less creepy and definitely more useful.

• VisualNexus GitHub Repository:
For maximum openness, transparency, and collaboration, we have made our code available on GitHub. It's now open season for coders and AI enthusiasts who want to make this even better. After all, we are standing on the shoulders of giant nerds.

Bug Fixes:

• GitHub Repository Access: It was reported that some users experienced difficulty when trying to access the repository. We found that our GitHub repo was acting like a bouncer at a nightclub. After RCA, it was discovered that this issue was due to incorrect repository settings. Now, we've sorted out the access rights, and it's all smooth sailing.

Next Steps

End of VisualNexus Log

Kosmos-X API,

Version 2.0 - Now Ready for the Stars

Key Updates:

• Introduced conditional input processing - Allowing the API to flexibly handle both text and image inputs.

• Introduced dynamic billing using the Stripe API - Clients are now billed based on actual usage. The spaceship needs fuel, folks!

• Created Dockerfile for containerization - Making the API easily shippable, scalable and space-ready!

• Deployed as a Kubernetes service - Allowing for self-healing, scalability and easy management in the harsh environment of the cloud.

Challenges Faced:

MechaZilla

Changes and Improvements

• Summoned a model directory section in the README.md for users to choose their robotic transformer destiny. 🤖

• Introduced the RT-1 and Gato models into our universe, detailing their architectures and superpowers. 💪

• Unveiled secrets about the training data and datasets that went into making these transformer titans. 🗂️

• Segregated the dataset directory into control and vision & language datasets for ease of navigation. It's all about making life multiplanetary...or at least multicategorical. 🚀

• Embedded teleportation links to relevant resources and repositories for each model and dataset. Because, why should hyperloops be limited to terrestrial transit? 🕳️

• Enlisted additional PALM-E datasets into the directory. More data, more power. 💾

• Refurbished the table-like sections for a visually pleasing experience. All work and no aesthetics makes life a dull ride! 🎨

Errors and Bugs

• Stumbled upon a wormhole (aka broken link) leading to the Gato code repository.
• Root Cause Analysis: Someone had fat-fingered the URL.
• Solution: Fixed the coordinates and made sure the wormhole now leads to the right dimension (correct URL).

• Caught a typo in the description of the RGB Stacking simulator dataset.
• Root Cause Analysis: Perhaps a QWERTY keyboard layout in a DVORAK world?
• Solution: Corrected the typo and restored universal balance.

• Found a missing link for the PALM-E datasets.
• Root Cause Analysis: Looks like our Martian link-miner bots took a day off.
• Solution: Requested the Hugging Face alliance to dispatch the respective dataset links. It's a cooperative universe after all.

Version 1.1

Changes and Improvements

• Welcomed PALM-E datasets into the fold and put them under the same roof as the PALM-E model.

• Gave the README.md a facelift. Now it's not just user-friendly, but user-enthralling.

• Buffed up the table format to ensure no eyes were glazed in the process of reading.

• Crafted an introductory paragraph to prime the users for the epic saga of the changelog section.

• Infused an Elon Musk-esque charm into the content, making every sentence worth its weight in Falcon boosters. 🦅

Errors and Bugs

• No bugs or errors reported. The code is as clean as a freshly landed Starship! 🚀

RoboCat

RoboCat Changelog and Debugging Log

1.0.0 — Initial Version (Before)

• Introduced the VQGAN model for encoding image data.

• Implemented the GATO model for processing encoded data.

• Managed data collection and preprocessing.

• Handled image encoding with VQGAN.

• Prepared tensor inputs for GATO.

• Fed encoded images to GATO.

1.1.0 — The Integrated Approach (After)

Added

• Incorporated VQGAN and GATO into a single class, RoboCat. This upgrade is kind of like adding a turbocharger to your car. Not necessary but undeniably cool. 😎

Changed

• Isolated data collection and preprocessing into a standalone method for better modularity. Just imagine if you had to assemble your electric vehicle (EV) every time you wanted to drive. That wouldn't be efficient, would it?

• Isolated image encoding with VQGAN into a standalone method. It's just like optimizing the battery range in a Tesla. More range = more efficiency = more happiness.

• Isolated feeding of encoded images to GATO into a standalone method. Like separating the autopilot system from the main driving system. Better organization, better debugging, fewer headaches.

Debugging

• Encountered a few errors during data preprocessing and image encoding. It was like trying to land a rocket without any calculations; you're bound to hit some snags.

• Root cause analysis revealed that most of these issues were due to incorrect or incompatible data inputs. It’s like when you realize you’ve been trying to fit a square peg in a round hole. 🤦‍♂️

Fixed

• Implemented error handling to manage these issues better. Now, even if an error occurs, it's managed gracefully and doesn't halt the whole system. Just like Tesla's smart summon, if the path isn't clear, it will wait or find a different path.

• Instead of the code crashing like a Cybertruck into a bollard, we have an error message that tells us what went wrong. It's always better to have an early warning system in place, be it for software bugs or alien invasions. 👽

Upcoming

• Now that we have a great base model, future versions will look into more granular error handling and optimization. Think of this as tuning your EV for ludicrous speed. More efficiency, fewer bugs, and much, much faster!

MegaByte

Changes/Improvements

1. Updated the code to import the necessary modules and classes from the correct locations.

2. Corrected the indentation and added necessary line breaks to improve code readability.

3. Added a missing import statement for the rearrange function from the einops library.

4. Modified the code to fix the issue of repeated code execution and error in the model's forward pass.

5. Fixed the error related to the input tensor shape in the forward method of the MEGABYTE class.

6. Updated the code to pass the correct tensor shape and modality value for the forward pass.

7. Fixed the issue of the missing padding parameter in the RMSNorm class initialization.

8. Modified the code to unpack the result of the top_k function correctly and handle the tensor dimensions.

9. Adjusted the token_shift function to correctly handle tensor dimensions and concatenate the tensors along the last dimension.

10. Updated the code to handle the case of empty input by adding a separate method forward_empty.

11. Fixed the issue with the variable names in the forward method of the MEGABYTE class.

12. Corrected the tensor reshaping and concatenation in the forward method to match the expected input format.

13. Updated the code to handle the case of flattened input dimensions correctly and auto-padding the input tensor to the nearest multiple of the sequence length.

14. Adjusted the tensor dimensions and reshaping in the forward method to match the model architecture.

15. Fixed the error related to the incorrect dimensions of the positional embeddings in the forward method.

16. Modified the code to include the entire file within the code block for better readability.

Errors/Bugs Encountered

1. Assertion Error: "ids.ndim in {2, self.stages + 1}" in the forward method of the MEGABYTE class.
• Root Cause: The input tensor dimensions were not matching the expected dimensions for the hierarchical stages.
• Solution: Adjusted the tensor reshaping and concatenation to match the expected input format for the model.

2. AttributeError: "'Tensor' object has no attribute 'ndim'" in the forward method of the MEGABYTE class.
• Root Cause: The input tensor shape was being accessed incorrectly using the ndim attribute.
• Solution: Modified the code to use the shape attribute instead of ndim to access the tensor shape.

3. Index Error: "IndexError: index 0 is out of bounds for dimension 0 with size 0" in the main code block.
• Root Cause: The input tensor shape was not matching the expected dimensions for the forward pass.
• Solution: Adjusted the tensor shapes and ensured the correct indexing to fix the error.

4. Missing Attribute: "AttributeError: 'MEGABYTE' object has no attribute 'stage_start_tokens'" in the forward method of the MEGABYTE class.
• Root Cause: The stage_start_tokens attribute was missing from the MEGABYTE class.
• Solution: Added the stage_start_tokens attribute to the MEGABYTE class initialization and used it in the forward method accordingly.

5. Incorrect Import: "ModuleNotFoundError: No module named 'MEGABYTE_pytorch'" in the main code block.
• Root Cause: The import statement for the MEGABYTE module was incorrect.
• Solution: Updated the import statement to import the MEGABYTE class correctly from the MEGABYTE_pytorch module.

6. Missing

• Root Cause: The import statement for the RMSNorm class was incorrect or missing.

• Solution: Added the import statement for the RMSNorm class from the correct module.

1. Incorrect Variable Name: "NameError: name 'text' is not defined" in the main code block.
• Root Cause: The variable name text was used instead of x_text in the main code block.
• Solution: Updated the variable name to x_text to match the defined variable.

Conclusion

The Distiller

Distiller v1.1.0 Changelog

Enhancements:

HuggingFace Integration

Improved Interruption Modes

Addition of Multi-Agent Capability

Bug Fixes:

Tokenization Error

Misalignment in Agent Turn-Taking

Tree of Thoughts

Version 2.0.0 Tree of Thoughts Changelog

Improvements and Refactoring

• We've moved away from procedural style and embraced object-oriented programming. Now, all the code resides within a class named TreeOfThoughtsV2. This makes the code more organized, easier to understand, and paves the way for future improvements.

• All the functions are now methods of the TreeOfThoughtsV2 class. This means we no longer need to pass common parameters (like task, x, y, etc.) around.

• The args object and the task object are now instance attributes of the TreeOfThoughtsV2 class. They're initialized when the class instance is created and can be easily accessed from any method, eliminating the need to pass them as parameters to every function call.

Errors and Bugs

• During the refactoring, we came across issues where variables like gpt were not being recognized because we were treating them as global variables. We solved this by making it an instance attribute self.gpt initialized in the constructor.

• Initially, there were issues with accessing the args object within class methods as it was being passed as an argument to the functions. The solution was to make it an instance attribute and access it via self.args.

• We also had to ensure that the class was instantiated before calling the run method. The if __name__ == '__main__': block now creates an instance of TreeOfThoughtsV2 and calls its run method.

Future Works

Overview

Changes and Improvements

Model Selection

API Key Handling

API Model Validation

Logging and Error Handling

Rate Limit Handling

Code Cleanup

Bug Fixes

API Call Formatting

State Evaluation Error

Prompt Construction

Conclusion

Andromeda

Version 2.0.0

Changes & Improvements:

1. Dataset Loading: Initially, the model was not processing the dataset correctly due to a coding oversight. The solution involved switching to the Hugging Face dataset with the id "ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered", which is a great resource to tap into, by the way.

2. Data Preparation: The data needed to be prepared in a format that the Andromeda model could utilize effectively. To that end, a function prep_sample was added to handle this, making the data ingestion process seamless.

3. Custom Dataset Class: The custom AlpacaDataset class was overhauled and aptly renamed as WizardDataset. This class was equipped with necessary functionalities like the ability to pad and tokenize the sequences correctly. It's as if it learned how to be a magician overnight. Only it was a few hours of coding instead.

4. Training Loop: The training loop was updated to reflect changes in data structure and improved to add a learning rate scheduler which dynamically adjusts the learning rate during training, a pretty nifty thing to have around if you ask me.

Encountered Errors & Solutions:

1. KeyError: We ran into a small mishap involving a KeyError which originated from misalignment between keys in the dictionary and those being called in the training loop. This was quickly fixed by aligning the keys correctly in both the WizardDataset and training loop.

Andromeda Model and Tokenizer Updates

1. Model and Tokenizer Classes Improvements 🚀

2. Debugging KeyError: 'input_ids' 🔎

3. Addressing the RuntimeError in Data Collation 🛠

4. TypeError: string indices must be integers ⚡

5. Dealing with ValueError in Text Input 🌌

Improvements & Enhancements:

1. Abstract Base Class for Custom Tokenizer & Embedder: Introduced abstract base classes for creating custom tokenizers and embedding layers. This design pattern helps to implement custom solutions without breaking the existing system, following the philosophy of "do no harm" to established working code.

2. Integrated Custom Tokenization & Embedding into TransformerWrapper: TransformerWrapper class is modified to accept custom tokenizers and embedders. This offers flexibility to developers to integrate custom solutions and modify the behavior of the model, without hardcoding it inside the TransformerWrapper.

3. Optimized Dataset Preprocessing: Improved the dataset preprocessing pipeline by integrating custom tokenization directly into it. This allows for more efficient use of resources and faster training times.

Encountered Bugs & Issues:

1. No Support for Custom Tokenization and Embedding: The initial version of TransformerWrapper did not support custom tokenization and embedding. This was a limiting factor for developers looking to experiment with different tokenization and embedding strategies.
Solution: Introduced optional parameters for custom tokenizer and embedder in the TransformerWrapper. If provided, the wrapper uses the custom functions; else, it falls back to the default ones.

2. Missing Pythonic Code Standards: Some parts of the code didn't adhere to Python's "Zen of Python" guidelines, which promotes readability and simplicity.
Solution: Revised the code to follow pythonic standards, making it more readable, maintainable, and friendly for other developers.

3. Inefficient Dataset Preprocessing: The initial dataset preprocessing was not using the potential of the custom tokenizer effectively. This resulted in unnecessary overhead during model training.
Solution: Refactored the preprocessing pipeline to utilize the custom tokenizer in the most efficient way possible.