open-llms VS Local-LLM-Comparison-Colab-UI

Everyone knows about ChatGPT by now, but did you know there are other models like "Mistral" or "Falcon" - you can view a full list of open-source models here or on huggingface.

This is the most well-maintained list of commercially usable open LLMs: https://github.com/eugeneyan/open-llms

MPT, OpenLLaMA, and Falcon are probably the most generally useful.

For code, Replit Code (specifically replit-code-instruct-glaive) and StarCoder (WizardCoder-15B) are the current top open models and both can be used commercially.

There's also MPT, which has a 7B, and Falcon, with a 7B and 40B although they have not had the inference tuning in community projects that the llamas have had. This is a good repo for reviewing what's available atm: https://github.com/eugeneyan/open-llms

If I was going to do the same I would at least build off of something, check out https://github.com/eugeneyan/open-llms, you should at least have a decent understanding of artificial neural networks (ANNs) and this link is pretty good on the basic concepts you need inc classification and learning types, good luck friend.

This list might help: https://github.com/eugeneyan/open-llms

In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.

You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.

That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).

I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)

[1] https://github.com/turboderp/exllama#results-so-far

[2] https://github.com/aigoopy/llm-jeopardy

[3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...

[4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder

The best 7B I tried is WizardLM. It's my go-to model.

If you want to try it out, you can use Google Colab here with Oobabooga Text Generation UI: Link (Remember to check the instruction template and generation parameters)

I'm really impressed by wizardLM-7B.q4_2 (GPT4all) running on my 8gb M2 Mac Air. Fast response, fewer hallucinations than other 7B models I've tried. GPT4All's beta document collection and query function is respectable--going to test it more tomorrow. FWIW wizardLM-7B.q4_2 was ranked very high here https://github.com/Troyanovsky/Local-LLM-comparison.

I made a series of Colab notebooks for different models: https://github.com/Troyanovsky/Local-LLM-comparison

I found it performing very well too in my testing (Repo). It's my second favorite model after WizardLM-13B.

My entire list at: Local LLM Comparison Repo

I just tried HyperMantis and updated the results in the repo. It performs not bad but worse than Nous-Hermes-13B.