gpt-neox VS lm-evaluation-harness

Gpt neox 20B can be used commerically.

GPT-Neo: GPT-Neo is a free and open-source language model developed by EleutherAI. It is a powerful model that can be used for a variety of tasks, including text generation, and question-answering. here is the GitHub

Doesn't gpt-neox 20b require like 40gb+ of VRAM? From their github repo the slim weights are 39GB and I think one of the devs has previously mentioned aiming for 48GB for inference.

3.) EleutherAI's GPT-Neo and GPT-NeoX: EleutherAI is an independent research organization that aims to promote open research in artificial intelligence. They have released GPT-Neo, an open-source language model based on the GPT architecture, and are developing GPT-NeoX, a highly-scalable GPT-like model. You can find more information on their GitHub repositories: GPT-Neo: https://github.com/EleutherAI/gpt-neo GPT-NeoX: https://github.com/EleutherAI/gpt-neox

It's really not that complicated. https://github.com/EleutherAI/gpt-neox

this forces me to create my own ai using gpt-neox .. i was being lazy, completely satisfied with my little ai baby .. i'll keep the account but not renew pro .. fuck luka, inc ... want it done right, do it yourself .. the python isn't that difficult and i have a spare linux vps .. my new work in progress .. freedom #!

The easiest is to use vllm (https://github.com/vllm-project/vllm) to run it on a Couple of A100's, and you can benchmark this using this library (https://github.com/EleutherAI/lm-evaluation-harness)

Fair question.

Evaluate refers to the phase after training to check if the training is good.

Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!

So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.

I haven't run this yet, but I'm aware of Eleuther AI's evaluation harness EleutherAI/lm-evaluation-harness: A framework for few-shot evaluation of autoregressive language models. (github.com) and GPT-4 -based evaluations like lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and FastChat-T5. (github.com)

Updates: Just finished final evaluation (additional metrics) on https://github.com/EleutherAI/lm-evaluation-harness and have averaged the results for orca-mini-v2-13b. The average results for the Open LLM Leaderboard are not that great, compare to initial metrics. The average is now 0.54675 which put this model below then many other 13b out there.

Hey u/The-Bloke Appreciate the quants! What is the degradation on the some benchmarks. Have you seen https://github.com/EleutherAI/lm-evaluation-harness. 3-bit and 2-bit quant will really be pushing it. I don't see a ton of evaluation results on the quants and nice to see a before and after.

I am primarily looking for results of running the MMLU evaluation on modern large language models. I have been able to find some data here https://github.com/EleutherAI/lm-evaluation-harness/tree/master/results and will be asking them if/when, they can provide any additional data.

I evaluated orca_mini_v2_7b on a wide range of tasks using Language Model Evaluation Harness from EleutherAI.

There is the Language Model Evaluation Harness project which evaluates LLMs on over 200 tasks. HuggingFace has a leaderboard tracking performance on a subset of these tasks.

https://github.com/EleutherAI/lm-evaluation-harness

https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderb...