Differences between gpt4all and OpenLLM?

[code2graph]

I've been exploring libraries that provide wrappers for LLM (large language models) and came across both gpt4all and OpenLLM. On a first glance, both seem to offer wrappers for a wide array of LLMs. I'm hoping to get some clarification on the differences between these two repositories.

Functional Differences: Are there any core functional or technical differences between gpt4all and OpenLLM?

Performance and Efficiency: Are there any performance benchmarks comparing the two, especially in terms of inference time or resource usage?

Primary Use Cases: Is one library geared towards a specific use-case or audience compared to the other?

Thank you in advance for your insights!

[aarnphm]

I believe that gpt4all is a project released early April from the Nomic team, and it built on top of llama.cpp, or at least they provide binding on top of the work at the ggml team.

OpenLLM fits in where you can deploy different model architecture, as well as running on different backend implementation (pytorch, tensorflow, flax, vllm, gguf(wip))

1. Functional difference

• correct me if I'm wrong, but gpt4all focus on local deployment of models (based on llama.cpp), whereas OpenLLM provides nice UX for you to play with a wide range of LLM, and then can bring such AI application to production (k8s, your own VPC, cloud, etc.)
• OpenLLM supports serving fine-tuning, loading adapters dynamically, quantization, integrations with langchain, hf agents, and etc.

2. Performance

• I currently don't have bandwidth to do a thorough benchmarking, because gpt4all and openllm are pretty different project. Where gpt4all focus on local development, OpenLLM can also do local development (given that you have a GPU to run for now as CPU is on our roadmap) and production deployment

In the ballpark, we have some additional optimization baked in, such as PagedAttention, FastTransformer, as well as quantization method such as gptq, bitsandbytes (flash attention is on the roadmap)

3. Use case

Seems like to me that gpt4all doesn't have a SDK per say (again correct me if I'm wrong), whereas OpenLLM is Python-first, and we have SDK for you to build AI application (integration with BentoML, LangChain, LlamaIndex (wip)). What we have seen so far is people trying to build chatbot, and tbh any type of application that you can think of when working with these models

Note that some of the items that we have on the roadmap

• AWQ quantization (see here)
• More solid support for GPTQ (wip)
• CPU support (GGUF and in extension GGML)

Hope this explains a bit better. Interested to hear more about your usecase.

[tpfau]

Is this statment: (given that you have a GPU to run for now as CPU is on our roadmap) still current, i.e. is it still planned to allow CPU only deployment with OpenLLM?
I'm just wondering what the status of OpenLLM is with respect to CPU support.

[aarnphm]

I have been playing with ctranslate and gguf recently, but it is currently still a roadmap item. For our users they are still currently only looking for GPU deployment.