Instruct-eval Alternatives

instruct-eval reviews and mentions

• Eval mmlu result against various infer methods (HF_Causal, VLLM, AutoGPTQ, AutoGPTQ-exllama)
  1 project | /r/LocalLLaMA | 8 Sep 2023

  I modified declare-lab's instruct-eval scripts, add support to VLLM, AutoGPTQ (and new autoGPTQ support exllama now), and test the mmlu result. I also add support to fastllm (which can accelerate ChatGLM2-6b.The code is here https://github.com/declare-lab/instruct-eval , I'd like to hear any errors in those code.

• [D] Red Pajamas Instruct 7B. Is it really that bad or some some ggml/quantization artifact? Vicuna-7b has no issue writing stories and even does basic text transformation. Yet RP refuses to do anything most of the time. It does generate a story if you run it as a raw model, but gets into a loop.
  1 project | /r/MachineLearning | 27 May 2023

  Well, I ran it with exactly the same parameters I ran Vicuna 7b, although I ran Vicuna with llama.cpp. while PJ can only be ran with ggml (I don't have a GPU). And Vicuna looped only when temperature reached 0. Given how hard it loops, I think it is some bug with ggml. Testers claim it should be close to 7b alpaca/vicuna:https://github.com/declare-lab/flan-eval

• [P] The first RedPajama models are here! The 3B and 7B models are now available under Apache 2.0, including instruction-tuned and chat versions. These models aim replicate LLaMA as closely as possible.
  1 project | /r/MachineLearning | 6 May 2023
• Best Instruct-Trained Alternative to Alpaca/Vicuna?
  2 projects | /r/LanguageTechnology | 23 Apr 2023

  For a list of other instruction tuned models, you can check out this benchmark here: https://github.com/declare-lab/flan-eval

• [R]Comprehensive List of Instruction Datasets for Training LLM Models (GPT-4 & Beyond)
  2 projects | /r/MachineLearning | 21 Apr 2023

  Great resource! I’ve recently also benchmarked many of the popular instruction models here: https://github.com/declare-lab/flan-eval

• Stability AI Launches the First of Its StableLM Suite of Language Models
  24 projects | news.ycombinator.com | 19 Apr 2023

  I really dislike this approach of announcing new models that some companies have taken, they don't mention evaluation results or performance of the model, but instead talk about how "transparent", "accessible" and "supportive" these models are.

  Anyway, I have benchmarked stablelm-base-alpha-3b (the open-source version, not the fine-tuned one which is under a NC license) using the MMLU benchmark and the results are rather underwhelming compared to other open source models:

  * stablelm-base-alpha-3b (3B params): 25.6% average accuracy

  * flan-t5-xl (3B params): 49.3% average accuracy

  * flan-t5-small (80M params): 29.4% average accuracy

  MMLU is just one benchmark, but based on the blog post, I don't think it will yield much better results in others. I'll leave links to the MMLU results of other proprietary[0] and open-access[1] models (results may vary by ±2% depending on the parameters used during inference).

  [0]: https://paperswithcode.com/sota/multi-task-language-understa...

  [1]: https://github.com/declare-lab/flan-eval/blob/main/mmlu.py#L...

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