Luca-MN-iMat-GGUF / README.md
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---
license: apache-2.0
base_model_relation: quantized
quantized_by: Quant-Cartel
base_model: rAIfle/Luca-MN-bf16
pipeline_tag: text-generation
tags:
- iMat
- GGUF
- unsloth
- trl
- sft
---
```
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Y888 888P Y888 888P ,ee 888 888 888 888
"88 88" "88 88" "88 888 888 888 888
b
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d888 'Y ,"Y88b 888,8, d88 ,e e, 888
C8888 "8" 888 888 " d88888 d88 88b 888
Y888 ,d ,ee 888 888 888 888 , 888
"88,d88 "88 888 888 888 "YeeP" 888
PROUDLY PRESENTS
```
# Luca-MN-iMat-GGUF
Quantized with love from fp32.
* Importance Matrix calculated using [groups_merged.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
* 92 chunks
* n_ctx=512
* Importance Matrix uses fp32 precision model weights, fp32.imatrix file to be added in repo
Original model README [here](https://huggingface.co/rAIFle/Luca-MN-bf16) and below:
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6569a4ed2419be6072890cf8/T_ITjuaHakgamjwuElcAs.png)
## Luca-MN-iMat-GGUF
This thing was just intended as an experiment but it turned out quite good. I had it both name and prompt imagegen for itself.
Created by running a high-r LoRA-pass over Nemo-Base with 2 epochs of some RP data, then a low-r pass with 0.5 epochs of the c2-data, then 3 epochs of DPO using [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1).
## Prompting
Use the `Mistral V3-Tekken` context- and instruct-templates. Temperature at about `1.25` seems to be the sweet spot, with either MinP at `0.05` or TopP at `0.9`. DRY/Smoothing etc depending on your preference.