One of several major highlights of MythoMax-L2–13B is its compatibility Using the GGUF structure. GGUF offers quite a few strengths above the former GGML structure, such as enhanced tokenization and aid for Distinctive tokens.
Optimize resource utilization: End users can enhance their components options and configurations to allocate ample resources for effective execution of MythoMax-L2–13B.
The GPU will complete the tensor Procedure, and The end result will be stored around the GPU’s memory (and never in the data pointer).
knowledge details to the actual tensor’s information, or NULL if this tensor is undoubtedly an operation. It can also place to a different tensor’s information, after which it’s often known as a watch
Teknium's primary unquantised fp16 model in pytorch format, for GPU inference and for more conversions
Instrument use is supported in both equally the 1B and 3B instruction-tuned products. Equipment are specified from the person inside a zero-shot environment (the product has no prior specifics of the equipment builders will use).
While it provides scalability and impressive utilizes, compatibility problems with legacy programs and recognised constraints ought to be navigated thoroughly. As a result of achievement tales in business and educational investigate, MythoMax-L2–13B showcases true-entire world apps.
MythoMax-L2–13B has found useful read more apps in many industries and is used productively in several use scenarios. Its impressive language technology qualities enable it to be well suited for an array of programs.
If you are able and prepared to add It will likely be most gratefully obtained and can help me to help keep offering a lot more versions, and to begin work on new AI tasks.
The design is created to be extremely extensible, permitting users to customise and adapt it for several use scenarios.