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PROUDLY PRESENTS

experiment_2_8b-iMat-GGUF

Quantization Notes: Quantized from 3500 checkpoint. Use repetition penalty (--repeat-penalty on llama.cpp) of ~1.15 with Q6_K and lower and ~1.18 with IQ3_M and lower for best results.

Quantized from fp16 with love.

Weighted quantizations were created using fp16 GGUF and groups_merged-enhancedV2-TurboMini.txt in 189 chunks and n_ctx=512
This method of calculating the importance matrix showed improvements in some areas for Mistral 7b and Llama3 8b models, see above post for details
The enhancedv2-turbomini file appends snippets from turboderp's calibration data to the standard groups_merged.txt file

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.

Original model card here and below

experiment_2_8b-fp16

Another experimental train w/ unsloth. This time, roughly 0.6 epochs of the cleaned c2-logs. My metaparams are probably bad, since the loss-value was super weird at the end. Also uploaded another version in the checkpoint-3500-branch that may mitigate some of that.