transformers safetensors mixtral text-generation 4-bit AWQ autotrain_compatible endpoints_compatible conversational en base_model:IBI-CAAI/MELT-Mistral-3x7B-Instruct-v0.1 base_model:quantized:IBI-CAAI/MELT-Mistral-3x7B-Instruct-v0.1 license:apache-2.0 text-generation-inference awq region:us
---
base_model: IBI-CAAI/MELT-Mistral-3x7B-Instruct-v0.1
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- 4-bit
- AWQ
- text-generation
- autotrain_compatible
- endpoints_compatible
inference: false
quantized_by: Suparious
---
# IBI-CAAI/MELT-Mistral-3x7B-Instruct-v0.1 AWQ

- Model creator: [IBI-CAAI](https://huggingface.co/IBI-CAAI)
- Original model: [MELT-Mistral-3x7B-Instruct-v0.1](https://huggingface.co/IBI-CAAI/MELT-Mistral-3x7B-Instruct-v0.1)

## Model Summary

The MELT-Mistral-3x7B-Instruct-v0.1 Large Language Model (LLM) is a pretrained generative text model pre-trained and fine-tuned on using publically avalable medical data.

MELT-Mistral-3x7B-Instruct-v0.1 demonstrated a average 19.7% improvement over Mistral-3x7B-Instruct-v0.1 (MoE of 3 X Mistral-7B-Instruct-v0.1) across 3 USMLE, Indian AIIMS, and NEET medical examination benchmarks.

This is MoE model, thanks to [Charles Goddard](https://huggingface.co/chargoddard) for code/tools.

The Medical Education Language Transformer (MELT) models have been trained on a wide-range of text, chat, Q/A, and instruction data in the medical domain.   

While the model was evaluated using publically avalable [USMLE](https://www.usmle.org/), Indian AIIMS, and NEET medical examination example questions, its use it intented to be more broadly applicable.   

- **Developed by:** [Center for Applied AI](https://caai.ai.uky.edu/)
- **Funded by:** [Institute or Biomedical Informatics](https://www.research.uky.edu/IBI)
- **Model type:** LLM
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** A MoE x 3 [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
