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v000000/L3.1-Niitorm-8B-DPO-t0.0001
L3.1-Niitorm-8B-DPO-t0.0001 is a text generation model from v000000. Use it when you need the model to write or continue text. It is set up for transformers.
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From the Hugging Face model README

<b>New: DPO'd Gutenberg Version (full epoch training).</b>
RP model, Niitama 1.1 as a base, nearswapped with one of the smartest 3.1 models "Storm", then DPO'd, mostly abliterated.
Essentially, it's an improved Niitama 1.1
Gutenberg DPO creates more human-like prose/story writing and greately lessen synthetic feeling outputs.
This is a merge and finetune of pre-trained language models.
Resultant merge finetuned on jondurbin/gutenberg-dpo-v0.1 for 1 epoch, 1.5e-5 learning rate, on Nvidia A100.
This model was merged using the <b>NEARSWAP t0.0001</b> merge algorithm.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
layer_range: [0, 32]
- model: akjindal53244/Llama-3.1-Storm-8B
layer_range: [0, 32]
merge_method: nearswap
base_model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
t:
- value: 0.0001
dtype: float16
# Then, DPO Finetune
# [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1)
I used a higher learning rate and full dataset when training compared to my "L3.1-Celestial-Stone-2x8B-DPO". This caused lower loss and better adaption to the chosen style.
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
Credit to Alchemonaut.
Credit to Sao10K.
Credit to Grimjim.
Credit to mlabonne.
Credit to jondurbin.
Credit to woofwolfy.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 27.89 |
| IFEval (0-Shot) | 76.89 |
| BBH (3-Shot) | 30.51 |
| MATH Lvl 5 (4-Shot) | 14.88 |
| GPQA (0-shot) | 5.93 |
| MuSR (0-shot) | 7.26 |
| MMLU-PRO (5-shot) | 31.85 |