Local-LLM-Comparison-Colab-UI VS alpaca_eval

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.

Alpaca Eval is open source and was developed by the same team who trained the alpaca model afaik. It is not like what you said in the other comment

I have been going deep in this space for my can-ai-code project and was looking at the config that WizardLM was run with: https://github.com/tatsu-lab/alpaca_eval/blob/main/src/alpaca_eval/models_configs/wizardlm-13b/configs.yaml

an automatic evaluator that is easy to use, fast, cheap and validated against 20K human annotations. It actually has a higher agreement with majority vote of humans than a single human annotator! Of course, our method still has limitations which we discuss here!