ChatRWKV VS llama-retrieval-plugin

Most LLMs actually do a decent job out of the box if you ask them for step by step instructions. Tree of tough is one way to improve the results, reflexion is another that can be used separate or additionally. The downside is that most models will run quickly into their token limit (around 2k for most). However the new SuperHot models can handle up to 8k and then there are the RMVK-Raven models, they are RNNs and not transformers like all the other LLMs and can theoretically handle infinite context lengths (but they loose "focus" after a while).

RWKV models inference: https://github.com/BlinkDL/ChatRWKV (fast CUDA).

I'm most interested in that last one. I think I heard the RWKV models are very fast, don't need much Ram, and can have huge context tokens, so maybe their 14b can work for me. I wasn't sure how ready for use they were though, but looking more into it, stuff like rwkv.cpp and ChatRWKV and a whole lot of other community projects are mentioned on their github.

You can use ChatRWKV v2 (https://github.com/BlinkDL/ChatRWKV) to run Raven🐦 (compatible with vanilla RWKV):

I feel compelled to summon /u/bo_peng here and to mention his work on RWKV. (See https://github.com/BlinkDL/ChatRWKV and related repos.)

Please test https://github.com/BlinkDL/ChatRWKV which is a good chatbot despite only trained on the Pile :)

For the demonstration purpose, you will be guided with the prompt execution using the Lastmile AI. However, you are free to execute the prompt directly on ChatGPT or other LLMs. If you are a developer, you may programmatically execute the prompt or provide an API or a service to your customers.

For the demonstration purposes, we'll be making use of AiConig driven approach of LastMileAI. If you are new to the LastMileAI or AiConfig, I request you to please read the following blogs

Hope you had a lot of fun with the AiConfig, systematic instruction driven modern artwork using the AiConfig and LastMileAI workbook. That said, your creativity or imagination is the only limit.

Hope you had a lot of fun with the AiConfig, Prompt routing and using the LastMileAI workbook. Creativity or imagination is the limit. You can potentially create or build any useful AI based apps or service in a matter of minutes.

You need to do the following steps and tweak the prompts based on your requirements. Especially the context or the content for which you wish to perform the trend analysis. There are two ways with which you could execute the AiConfig. Here is the first option of using the config and load it up to LastMileAI and perform the cell by cell execution from top to bottom.

When it comes to the hands on, As a preference, AiConfig is the framework through which you will be presented with the prompt building mechanism. There are various reasons like easy to iterate, test and see the responses in real-time with the LastMileAI Workbook. When it comes to the Prompt Chaining I personally prefer the AiConfig and the Workbook approach. However, it's purely optional for this development.

Our team at LastMile AI built a Streamlit app powered by AIConfig that lets you experiment with OpenAI's strategies on your prompts.

Now that you have understood the meaning and importance of "Composition", you will be guided with the mechanism to have a hands-on experience. For this purpose, we'll be making use of AiConig driven approach of LastMileAI. If you are new to the LastMileAI or AiConfig, I request you to please read the following blogs

I would highly recommend you to start using the LastMileAI workbooks for building the prompt based apps. Download the AiConfig and then start playing with it. Finetune as per your requirements and design or develop an API to serve the prompt based AI Apps.

Mhh, a viable alternative might be to setup a locally hosted search engine, like SearX and then use a LLM in conjunction with the llama-retrieval-plugin, that also gives you a database that is human readable and the LLM can give direct links to the source.