base_model: mistralai/Mistral-Nemo-Instruct-2407
library_name: transformers
quantized_by: InferenceIllusionist
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
tags:
- iMat
- gguf
- Mistral
license: apache-2.0
Mistral-Nemo-Instruct-12B-iMat-GGUF
Important Note: Inferencing is only available on this fork of llama.cpp at the moment: https://github.com/iamlemec/llama.cpp/tree/mistral-nemo (All credits to iamlemec for his work on Mistral-Nemo support)
Other front-ends like the main branch of llama.cpp, kobold.cpp, and text-generation-web-ui may not work as intended
Quantized from Mistral-Nemo-Instruct-2407 fp16
- Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 92 chunks and n_ctx=512
- Static fp16 will also be included in repo
For a brief rundown of iMatrix quant performance please see this PR
All quants are verified working prior to uploading to repo for your safety and convenience
KL-Divergence Reference Chart
(Click on image to view in full size)
Tip: If you are getting a cudaMalloc failed: out of memory error, try passing an argument for lower context in llama.cpp, e.g. for 8k: -c 8192
If you have all ampere generation or newer cards, you can use flash attention like so: -fa
Provided Flash Attention is enabled you can also use quantized cache to save on VRAM e.g. for 8-bit: -ctk q8_0 -ctv q8_0
Original model card can be found here