mergekit
autogen
| mergekit | autogen | |
|---|---|---|
| 6 | 32 | |
| 3,521 | 25,506 | |
| 18.7% | 7.7% | |
| 9.2 | 9.9 | |
| 6 days ago | 3 days ago | |
| Python | Jupyter Notebook | |
| GNU Lesser General Public License v3.0 only | Creative Commons Attribution 4.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mergekit
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Language Models Are Super Mario: Absorbing Abilities from Homologous Models
For others like me who’d not heard of merging before, this seems to be one tool[0] (there may be others)
[0] https://github.com/arcee-ai/mergekit
- FLaNK AI Weekly 25 March 2025
- Tools for merging pretrained large language models
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Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
mergekit is the tool you need to do this
https://github.com/cg123/mergekit -
Iambe-RP-20b: An uncensored L2 Frankenstein model directly trained with RP-oriented cDPO
I actually asked the creator of mergekit a question here. In his response, I learned how to use task_arithmetic to isolate the deltas. One could, in theory, use WANDA on that model from the second example, then merge it back into another model. However, that's firmly past the frontier of what has been tried, so experimentation might be messy.
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LLMs cannot find reasoning errors, but can correct them
Ah, actually reviewing that more closely I found a link to it in the acknowledgements.
https://github.com/cg123/mergekit
autogen
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Agents of Change: Navigating the Rise of AI Agents in 2024
AutoGen is an AI framework by Microsoft designed to streamline multi-agent conversations. AutoGen allows agents to communicate, share information, and make collective decisions. This setup enhances the responsiveness and dynamism of conversations. Developers use AutoGen to tailor agents to specific roles, such as programmer, content writer, CEO, etc. This enhances their ability to handle tasks from simple queries to intricate problem-solving.
- FLaNK AI Weekly 25 March 2025
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Launch HN: Glide (YC W19) – AI-assisted technical design docs
I am still playing around with the project but FYI, the parsing for the github repo URL at https://glide.agenticlabs.com/ will fail if there's a trailing slash in the repo link i.e. https://github.com/microsoft/autogen/ won't work but https://github.com/microsoft/autogen will.
- Show HN: Prompts as (WASM) Programs
- Enable Next-Gen Large Language
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AutoGen v0.2.2 released
New example notebook demoing video transcript translate with whisper.
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AutoGen v0.2.1 released
New release: v0.2.1
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AI is making us all more productive — but in a weird and unexpected way
I disagree with the conclusion. In software, I've seen 10x engineers in person and I don't think they're replaceable. Whereas, the new college grad or that entry level dev who doesn't design anything and just writes small amounts of code, doing exactly as told is replaceable by an AI. Frameworks similar to Microsoft Autogen(https://github.com/microsoft/autogen) can in theory build agents who can do these tasks with ease whereas a 10x engineer can focus on directing the agents and designing systems.
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Our Hacktoberfest Success Story
Microsoft autogen
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AutoGen v0.2.0b4 released
CompressibleAgent (experimental) can be used to handle long conversations. Notebook: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb
What are some alternatives?
Finetune_LLMs - Repo for fine-tuning Casual LLMs
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
xTuring - Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
LLMLingua - To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
task_vectors - Editing Models with Task Arithmetic
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
difftastic - a structural diff that understands syntax 🟥🟩
AgentVerse - 🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation
makeMoE - From scratch implementation of a sparse mixture of experts language model inspired by Andrej Karpathy's makemore :)
langchain - 🦜🔗 Build context-aware reasoning applications