How mythomax l2 can Save You Time, Stress, and Money.

If you're able and ready to lead It'll be most gratefully gained and can help me to keep supplying much more products, and to begin Focus on new AI projects.

Certainly one of the best accomplishing and most favored good-tunes of Llama two 13B, with prosperous descriptions and roleplay. #merge

Filtering was extensive of such community datasets, in addition to conversion of all formats to ShareGPT, which was then even further reworked by axolotl to make use of ChatML. Get far more facts on huggingface

The Azure OpenAI Services shops prompts & completions through the provider to watch for abusive use and to establish and increase the standard of Azure OpenAI’s content material management devices.

Numerous GPTQ parameter permutations are delivered; see Furnished Documents under for specifics of the options supplied, their parameters, and the software made use of to develop them.

# trust_remote_code remains set as Accurate considering the fact that we even now load codes from local dir as an alternative to transformers

Chat UI supports the llama.cpp API server specifically without the need to have for an adapter. You are able to do this using the llamacpp endpoint form.

MythoMax-L2–13B stands out for its Improved overall performance metrics in comparison to previous products. Some of its noteworthy pros involve:

Prompt Format OpenHermes two now works by using ChatML since the prompt structure, opening up a much more structured technique for partaking the LLM in multi-flip chat dialogue.

Cite Even though each and every work is produced to stick to citation model procedures, there may be some discrepancies. You should make reference to the suitable style guide or other resources if you have any inquiries. Find Citation Fashion

While MythoMax-L2–13B gives several strengths, it is vital to consider its constraints and possible constraints. Knowledge these restrictions may also help consumers make educated choices and enhance their use in the product.

In ggml tensors are represented because of the ggml_tensor struct. Simplified a little for our needs, get more info it looks like the subsequent:

Sequence Length: The length in the dataset sequences utilized for quantisation. Ideally This can be the same as the model sequence size. For some extremely lengthy sequence designs (16+K), a lower sequence duration can have for use.

--------------------

Leave a Reply

Your email address will not be published. Required fields are marked *