Model Card for Mixtral-Instruct-ITR-8x7B-GGUF

• Model creator: Envoid
• Original model: Mixtral-Instruct-ITR-8x7B

Envoid_Mixtral-Instruct-ITR-8x7B quantized with love.

Starting out with Q4_K_M, and iterating from there. All quantizations based on original fp16 model.

Future plans for imatrix/IQ quants (pending compute power).

First time doing quantizations so any feedback is greatly appreciated.

Name	Quant method	Bits	ppl*
Mixtral-Instruct-ITR-8x7B.Q2_K.gguf	Q2_K	2	+0.6717 ppl
Mixtral-Instruct-ITR-8x7B.Q3_K_S.gguf	Q3_K_S	3	+0.5551 ppl
Mixtral-Instruct-ITR-8x7B.Q3_K_M.gguf	Q3_K_M	3	+0.2496 ppl
Mixtral-Instruct-ITR-8x7B.Q3_K_L.gguf	Q3_K_L	4	+0.1764 ppl
Mixtral-Instruct-ITR-8x7B.Q4_K_M.gguf	Q4_K_M	5	+0.0532 ppl
Mixtral-Instruct-ITR-8x7B.Q5_K_S.gguf	Q5_K_S	5	+0.0400 ppl
Mixtral-Instruct-ITR-8x7B.Q5_K_M.gguf	Q5_K_M	6	+0.0122 ppl
Mixtral-Instruct-ITR-8x7B.Q6_K.gguf	Q6_K	6	+0.008 ppl
Mixtral-Instruct-ITR-8x7B.Q8_0.gguf	Q8_0	8	+0.004 ppl

*Perplexity @ LLaMA-v1-7B for reference

Original model card below.

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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 and went back to the very beginning and SLERP merged mistralai/Mixtral-8x7B-Instruct-v0.1 onto 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