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---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
---
# Model Card for Envoid_Mixtral-Instruct-ITR-8x7B-GGUF
<!-- Provide a quick summary of what the model is/does. -->
Envoid_Mixtral-Instruct-ITR-8x7B quantized with love.
Starting out with Q4_K_M, and iterating from there. Future plans for imatrix/IQ quants (pending compute power).
First time doing quantizations so any feedback is greatly appreciated.
Original model card below for reference.
---
license: cc-by-nc-4.0
---
# Caution this model may be unpredictable
## Mixtral-Instruct-ITR (Interpolative Training Regression)
We have to go back, edition.
For this model I took what I learned in the making of [Cat-8x7B](https://huggingface.co/Envoid/Cat-8x7B) and went back to the very beginning and SLERP merged [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) onto [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
While the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being.
The hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun.
Apologies about the small shard size (keep forgetting to change the mergekit config back)
## The model is a lot less likely to refuse certain requests in this state:
so if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case.
The model still responds well to [INST] Thingie [/INST] formatting quite well.
Or if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config
```
models:
- model: ./mistralai_Mixtral-8x7B-Instruct-v0.1
- model: ./mistralai_Mixtral-8x7B-v0.1
merge_method: slerp
base_model: ./mistralai_Mixtral-8x7B-v0.1
parameters:
t:
- value: 0.5
dtype: float16
``` |