This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to meta-llama/Meta-Llama-3-70B-Instruct.
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
Not Mixtral MoE but Merge-kit MoE
EveryoneLLM series of models are a new Mixtral type model created using experts that were finetuned by the community, for the community. This is the first model to release in the series and it is a coding specific model. EveryoneLLM, which will be a more generalized model, will be released in the near future after more work is done to fine tune the process of merging Mistral models into a larger Mixtral models with greater success.
The goal of the EveryoneLLM series of models is to be a replacement or an alternative to Mixtral-8x7b that is more suitable for general and specific use, as well as easier to fine tune. Since Mistralai is being secretive about the "secret sause" that makes Mixtral-Instruct such an effective fine tune of the Mixtral-base model, I've decided its time for the community to directly compete with Mistralai on our own.
EveryoneLLM series of models are a new Mixtral type model created using experts that were finetuned by the community, for the community. This is the first model to release in the series and it is a coding specific model. EveryoneLLM, which will be a more generalized model, will be released in the near future after more work is done to fine tune the process of merging Mistral models into a larger Mixtral models with greater success.
The goal of the EveryoneLLM series of models is to be a replacement or an alternative to Mixtral-8x7b that is more suitable for general and specific use, as well as easier to fine tune. Since Mistralai is being secretive about the "secret sause" that makes Mixtral-Instruct such an effective fine tune of the Mixtral-base model, I've decided its time for the community to directly compete with Mistralai on our own.
- create a custom base image for a Cloud Workstation environment using a Dockerfile
. Uses:
Quantized models from
. Uses:
Quantized models from
A deep dive into model quantization with GGUF and llama.cpp and model evaluation with LlamaIndex
