Model Benchmarking: Unveiling Insights into Language Models
Recently, the language model community has been buzzing with discussions about the performance of various models. A particular model that caught our attention is Beyonder, which, in casual testing, seems to be one of the rare non-broken Mixture of Experts (MoEs). It incorporates openchat-3.5, a model previously benchmarked by the community.
But what's the best inference engine? This question often arises, and it's crucial to consider the source code for testing methods. Without transparency, rankings lack validity and reproducibility.
One frustration echoed in the community is the lack of support for fine-tuning Mixtral, leaving many to grapple with its intricacies. This sentiment highlights the importance of collaborative efforts in the open-source model tuning community.
While some models like Solar and its variants didn't meet expectations, there are hidden gems like **SOLAR 10.7b Instruct v1.0 uncensored**. Finetuned on the **Toxic DPO** dataset, it outshines others in various tests (source).
As the community dives deeper into quantization methods, questions about hf bitsandbytes 4-bit quantization arise. The choice between nf4 or fp4 and the impact on results sparks curiosity. It's suggested to explore alternatives like gguf for potentially enhanced quality.
Concerns about Mixtral_34Bx2_MoE_60B's performance and its response to specific queries add an interesting layer to the ongoing discussions. Users eagerly await the release of Mixtral_34Bx2_MoE_60B GGUF for further exploration.
While model rankings are valuable, concerns are raised about the adequacy of the test set. The number of multiple-choice questions may need reconsideration to ensure a more robust evaluation of models.
Amidst the complexities, questions arise about models passing the single letter test. Tokenization nuances are considered, leading to inquiries about the efficacy of models like Mixtral_34bx2 compared to Mistral Medium.
As the rankings shift and new models emerge, the community eagerly anticipates developments in the open-source domain. Continuous improvements underscore the dynamic nature of language models.